GLM-5.2 Complete Guide: Benchmarks vs GPT-5.6 Sol, Claude Opus 5, Fable 5, Kimi K3, Qwen 3.8 Max, API Pricing, OpenRouter & Local Deployment (August 2026)

By AiCybr Editorial · Published August 5, 2026

Why GLM-5.2 Matters

GLM-5.2 isn’t just another open-weight model—it’s a purpose-built engine for long-horizon autonomous work. Whether you’re building repository-scale coding agents, multi-step research workflows, or persistent tool-using assistants, GLM-5.2 is designed to maintain context and decision-making accuracy over extended interactions.

Here is why this model demands attention:

• Massive Scale: 753B total parameters with a highly efficient 40B active per token.
• Extreme Context: A native 1M-token window capable of ingesting entire codebases.
• True Open Source: Released under the permissive MIT license, enabling unrestricted commercial use.
• Aggressive Pricing: At $1.40 per 1M input tokens, it drastically undercuts proprietary models like Claude Opus 5 and GPT-5.6 Sol.
• Advanced Architecture: 256 routed experts and DeepSeek Sparse Attention for highly efficient long-context retrieval.

While it requires severe hardware to self-host, its broad availability across providers like OpenRouter, Fireworks, Together AI, and Z.ai’s own API makes it immediately accessible for production workflows.

GLM-5.2 is available through the Z.ai API and chat interface, the GLM Coding Plan, OpenRouter, Fireworks AI, Together AI, Novita AI, Hugging Face, and local/self-hosted inference frameworks. The checkpoint is downloadable, but it is not an ordinary laptop model: the official FP8 files alone are roughly 756 GB.

Quick Navigation

• GLM-5.2 at a glance
• Benchmarks and frontier-model comparison
• What is GLM-5.2?
• Is GLM-5.2 open source?
• Why 744B and 753B are both reported
• Architecture explained in plain language
• Capabilities
• Where GLM-5.2 is available
• API pricing
• Local and self-hosted deployment
• Best use cases
• Limitations
• Frequently asked questions

GLM-5.2 at a Glance

Primary sources: Z.ai GLM-5.2 launch, Z.ai model documentation, official Hugging Face model, official configuration, and MIT license.

GLM-5.2 Benchmarks and Frontier Model Comparison

Benchmark results are useful only when their source and test setup are visible. The tables in this section are deliberately separated into three evidence groups:

1. Z.ai’s launch comparison: a broad vendor-reported matrix produced for GLM-5.2’s release.
2. A current multi-model coding table: published by Moonshot AI for Kimi K3 and assembled from several named harnesses and leaderboards.
3. Artificial Analysis Intelligence Index: an independently run composite that puts current proprietary and open-weight models on one methodology.

The figures should not be merged into a single universal ranking. An agent benchmark measures the model plus its harness—the software that provides prompts, tools, retries, time limits, context management, and execution rules.

Z.ai Launch Comparison: Reasoning

The following table is reproduced from Z.ai’s June 16, 2026 GLM-5.2 launch evaluation. All numbers in this table are vendor-reported by Z.ai.

* Z.ai’s table marks selected competitor values as sourced from external reports rather than rerun under every GLM setting. HLE also has multiple variants—text-only, full, with tools, and different context policies—so only like-for-like cells should be compared.

The Takeaway: GLM-5.2 dominates AIME 2026 (99.2), proving its raw mathematical reasoning capabilities, and shows a massive leap over its predecessor, GLM-5.1. However, it doesn’t sweep the board. Proprietary models like Gemini 3.1 Pro still hold the edge in GPQA Diamond, while Claude Opus 4.8 and GPT-5.5 maintain dominance in specialized expert-reasoning tasks. For practitioners, this means GLM-5.2 is a tier-one reasoning engine, but you shouldn’t blindly swap it for Opus 5 if your workload relies heavily on those specific edge-case reasoning tasks.

Z.ai Launch Comparison: Coding and Long-Horizon Engineering

The Takeaway: This is where GLM-5.2 flexes. The generational leap from GLM-5.1 is staggering—jumping from 63.5 to 81.0 on Terminal-Bench 2.1 and crushing FrontierSWE (74.4). While Claude Opus 4.8 and GPT-5.5 still edge it out in raw SWE-bench Pro and DeepSWE scores, GLM-5.2’s performance proves it is fully capable of driving production-grade autonomous coding agents. The gap between open-weights and proprietary models in long-horizon coding has never been narrower.

Z.ai Launch Comparison: Agent and Tool Use

GLM-5.2 is close to Claude Opus 4.8 on MCP-Atlas in this matrix, but it trails Claude, GPT-5.5, and DeepSeek V4 Pro on Tool-Decathlon.

Primary source: Z.ai GLM-5.2 launch and evaluation notes

The Evaluation Settings Behind the GLM Table

Z.ai’s footnotes matter as much as the headline scores:

• Reasoning tests: temperature 1.0, top-p 0.95, and up to 163,840 generated tokens.
• HLE with tools: 300K context with no context-management strategy.
• SWE-bench Pro: OpenHands harness, 400K context, and up to 32K new tokens.
• DeepSWE: Mini-SWE-agent, two-hour timeout, 400K context, two CPUs, 8 GB RAM, and no internet.
• ProgramBench: Claude Code 2.1.156, 400K context, up to 64K output, 2,000 turns, and a six-hour timeout.
• Terminal-Bench 2.1 with Terminus-2: four-hour timeout, 256K context, up to 48K new tokens, and at most 500 episodes.
• Terminal-Bench 2.1 with Claude Code: Claude Code 2.1.167, up to 128K output, no wall-clock limit, and an average over five runs.
• FrontierSWE, PostTrainBench, and SWE-Marathon: 1M context, maximum reasoning effort, and up to 128K output.

This explains why the same GLM-5.2 checkpoint scores 81.0 with Terminus-2 and 82.7 with the best reported harness on Terminal-Bench 2.1.

Current Frontier Coding Comparison: Kimi K3, Fable 5, GPT-5.6 Sol and GLM-5.2

Moonshot AI’s Kimi K3 model card provides a more current comparison that includes models released after Z.ai’s original table. It is still a vendor-published comparison, and its footnotes explicitly mix harnesses: Kimi Code for Kimi K3, Claude Code for several Anthropic/GLM runs, Codex for GPT models, and external leaderboards for some values.

This later table changes the practical interpretation. GLM-5.2 remains a strong open-weight model, but Kimi K3 and the latest proprietary systems lead it on many current agentic-coding tasks. GLM’s distinctive strengths are therefore not an undisputed benchmark lead; they are its combination of MIT licensing, 1M context, broad hosting support, self-hosting, and comparatively low direct API pricing.

Source: Moonshot AI Kimi K3 model card and evaluation footnotes

Independent Composite Snapshot: Artificial Analysis

Artificial Analysis evaluates models with one composite methodology. Its Intelligence Index v4.1 includes nine evaluations covering professional work, banking agents, Terminal-Bench 2.1, scientific coding, HLE, GPQA Diamond, CritPt, knowledge reliability, and long-context reasoning.

Snapshot date: August 5, 2026. Scores and ranks are dynamic. Prices are listed public API rates or the current rate shown by the evaluator; long-context surcharges, cache pricing, routed-provider markups, and subscription plans can differ.

Sources: GLM-5.2, Claude Opus 5, Claude Fable 5, GPT-5.6 Sol, Kimi K3, MiniMax M3, and MiniMax M2.7.

Where Claude Opus 5 Fits

Claude Opus 5 is a current proprietary model with the API ID claude-opus-5, a 1M-token context, a 128K maximum output, and $5/$25 per-million-token pricing. It is included in the independent composite table above. It is not inserted into Z.ai’s June launch table, because that table predates Opus 5 and Anthropic has not published a directly compatible full matrix using Z.ai’s exact prompts, budgets, and harnesses.

Source: Anthropic’s Claude Opus 5 documentation

Where Qwen3.8-Max Fits

Qwen3.8-Max is relevant because it is a 2.4T-parameter, approximately 95B-active, 1M-context frontier model announced by Alibaba in August 2026. However, this guide does not fill its cells with precise benchmark claims from secondary comparison posts. As of the research cutoff, a public, methodology-backed Qwen3.8 table suitable for a controlled GLM-5.2 comparison was not independently verifiable, and Artificial Analysis had not published a common-method score.

That does not establish that Qwen3.8 is weak. It means the evidence is not yet aligned well enough for a precise numerical comparison. The responsible entry is therefore “not yet available,” not an invented or mixed-harness score.

Sources: Qwen3.8 launch page and Artificial Analysis model directory

MiniMax M2.7 vs MiniMax M3

The model the user may see written as “MiniMax 2.7” is officially MiniMax M2.7. Its model card reports 56.2 on SWE-bench Pro, 57.0 on Terminal-Bench 2.0, and 39.8 on NL2Repo. Terminal-Bench 2.0 is not directly comparable with the 2.1 results elsewhere in this article.

MiniMax M3 is the newer model and should be the default current comparison. Artificial Analysis scores M3 at 44 versus M2.7 at 38, while M3 increases context to 1M and uses a 428B/23B MoE design.

Sources: MiniMax M2.7 model card and Artificial Analysis MiniMax M3

Benchmark Verdict: What This Means for You

Let’s cut through the noise: GLM-5.2 is not the undisputed “best model in the world.” Proprietary titans like Claude Opus 5 and GPT-5.6 Sol, along with the open-weight Kimi K3, still edge it out on several key metrics.

However, GLM-5.2 is arguably the most disruptive model on the market right now.

Why? Because it delivers ~90% of frontier proprietary performance with an MIT license and a 1M context window, all at a fraction of the API cost ($1.40/1M input tokens). If you are building high-volume, long-horizon coding agents where context caching and sheer token volume make Claude or GPT cost-prohibitive, GLM-5.2 is your definitive answer. Always run your own evaluations, but GLM-5.2 has completely rewritten the ROI equation for agentic workflows.

What Is GLM-5.2?

GLM-5.2 is a large, text-only foundation model designed around a specific problem: keeping an AI agent useful over a long and complicated task.

Many language models can answer a coding question or generate a function. Long-horizon engineering is harder. A model may need to:

1. Read a large repository.
2. Understand architectural constraints.
3. Create a plan.
4. Modify dozens of files.
5. Run tools and tests.
6. Interpret failures.
7. Revise the implementation.
8. Preserve earlier decisions through hundreds of turns.
9. Produce a final verification report.

The difficulty is not simply fitting more text into a prompt. The model must continue to retrieve relevant facts, follow instructions, use tools correctly, avoid shortcuts, and recover from mistakes after a long sequence of actions.

Z.ai positions GLM-5.2 for this class of work. It extends the GLM-5 family from a roughly 200K-token context to a native 1M-token context, adds a more efficient sparse-attention indexing design, improves speculative decoding, and exposes multiple reasoning-effort levels.

The result is not a small local coding assistant. It is an enterprise-scale open-weight model intended to compete with high-end hosted models while giving organizations the option to control the weights and serving infrastructure.

Is GLM-5.2 Open Source?

GLM-5.2’s official model repository uses the MIT License. That is substantially more permissive than many “open” model licenses.

The MIT License permits users to:

• Use the software and model files
• Copy them
• Modify them
• Merge them into other work
• Publish and redistribute them
• Sublicense them
• Sell products or services built with them

The main license requirement is retaining the copyright and license notice in copies or substantial portions. The software is provided without warranty.

This makes GLM-5.2 suitable for commercial research, internal enterprise deployment, hosted services, derivative models, and product integration from a model-license perspective.

Two qualifications still matter:

1. Open weights are not the same as a fully reproducible open-source training project. Z.ai publishes the weights, configuration, inference support, model card, and related technical material, but it does not publish the complete training data or everything required to recreate the model from zero.

2. Provider terms still apply. Running the MIT-licensed checkpoint on your own infrastructure is different from using Z.ai, OpenRouter, Fireworks, Together, or another hosted service. A provider can impose its own acceptable-use, privacy, rate-limit, and billing terms.

For most practical discussions, the most accurate description is:

GLM-5.2 is an MIT-licensed open-weight model with unusually permissive commercial-use rights.

Why Do Some Sources Say 744B and Others Say 753B?

Users will encounter two parameter figures:

• 753B parameters on the released Hugging Face checkpoint metadata
• 744B total / approximately 40B active in descriptions of the GLM-5 backbone and on several provider pages

The official configuration does not provide a single explanatory field reconciling these two totals. The difference may come from counting conventions—for example, whether embeddings, auxiliary prediction layers, or other components are included—but Z.ai has not published a definitive reconciliation.

Use the figures this way:

• Use 753B when estimating checkpoint storage and total model size, because it is the current official Hugging Face metadata.
• Use approximately 40B active per token when discussing inference compute, because only a subset of the Mixture-of-Experts network is selected for each token.
• Treat 744B as a commonly cited backbone figure rather than silently substituting it for the checkpoint’s 753B metadata.

This distinction does not make the model small. All expert weights still need to be stored and available, even though only part of the network runs for each token.

Architecture Explained in Plain Language

1. Mixture-of-Experts: A Huge Model That Uses Only Part of Itself at Once

A traditional dense model runs every layer’s full set of parameters for every token. GLM-5.2 instead uses a Mixture-of-Experts, or MoE, architecture.

An expert is a specialized feed-forward subnetwork. GLM-5.2 contains 256 routed experts. For each token, a router scores those experts and sends the token through the top eight.

A simplified analogy:

• The complete model is a company with 256 specialist departments.
• Each incoming problem is routed to eight departments most relevant to that problem.
• The company still needs office space for all departments, but only a subset works on each individual request.

This approach offers two advantages:

• The model can contain a very large total capacity.
• Per-token computation is much lower than running all 753B parameters as a dense model.

It also creates engineering challenges:

• Expert weights must be distributed across GPUs.
• Tokens must be routed between GPUs.
• Uneven expert demand can reduce utilization.
• Fast interconnects become important.
• Total memory requirements remain enormous.

The official configuration lists three initial dense layers, followed by sparse MoE layers, one shared expert, and eight routed experts selected per token.

2. DeepSeek Sparse Attention: Looking at Relevant History Instead of Everything

Standard full attention compares each token with every earlier token. At very long sequence lengths, that becomes expensive.

GLM-5.2 uses DeepSeek Sparse Attention (DSA). Instead of applying the expensive attention calculation to every token in a million-token history, a lightweight indexer identifies a smaller relevant set.

The official configuration uses:

• Top 2,048 selected past tokens
• A 1,048,576-token maximum position length
• 64 attention heads

In plain language, the model first asks:

“Which parts of this very long history are most relevant to what I am processing now?”

It then performs the costly attention operation primarily on that selected subset.

This does not mean long-context work is free. The system still has to maintain a large key-value cache, run the indexer, move data between devices, and manage a long prompt. But sparse attention makes the core attention operation more practical than full quadratic attention across the entire sequence.

3. IndexShare: Reusing the Search Work Across Four Layers

Sparse attention needs an indexer to find relevant tokens. If every transformer layer independently scans and ranks the long history, the indexer itself can become expensive.

GLM-5.2 introduces IndexShare:

• One indexer is used across a group of four sparse-attention layers.
• The indexer runs at the first layer in the group.
• Its selected token indices are reused by the following three layers.

Z.ai reports that this cuts per-token FLOPs by 2.9 times at a 1M-token context.

FLOPs means floating-point operations: a rough measure of computation. A 2.9× reduction in this part of the model does not make the entire request 2.9× faster, because serving also includes expert routing, memory access, key-value cache operations, networking, scheduling, and output generation. It does, however, target one of the major costs of sparse long-context attention.

4. MTP and Speculative Decoding: Drafting Several Tokens at Once

GLM-5.2 contains one Multi-Token Prediction (MTP) layer. It can draft multiple possible future tokens, which the full model then verifies.

This is called speculative decoding:

1. A lightweight draft path proposes several next tokens.
2. The full model checks them.
3. Accepted tokens are committed together.
4. Rejected tokens are recalculated normally.

Z.ai reports that changes involving IndexShare, key-value sharing, rejection sampling, and an end-to-end training loss increased average accepted draft length by up to 20% in its experiment, from 4.56 to 5.47 tokens.

Speculative decoding primarily improves serving speed. It does not mean the model is skipping validation or using a less intelligent model as the final authority—the main model still verifies the proposed tokens.

5. A One-Million-Token Context Window

The configuration sets max_position_embeddings to 1,048,576. Z.ai’s direct API advertises a 1M context and up to 128K output tokens.

A million tokens can hold:

• A large software repository
• Extensive documentation and test output
• Long agent histories
• Multiple research papers and supporting documents
• Large legal or policy collections
• A long sequence of tool calls and observations

However, a large context window should not be interpreted as:

• Perfect recall of every token
• Constant quality at every sequence length
• Low latency for million-token prompts
• Cheap memory usage
• A replacement for document organization or retrieval
• Permission to send unnecessary data

Long prompts increase prefill time, cache memory, cost, and the chance that irrelevant information competes with useful information. Test the model at the lengths your application actually uses—such as 32K, 128K, 256K, 500K, and near 1M—rather than assuming performance remains identical at the limit.

Model Configuration Details

The released configuration provides unusually useful architectural detail:

These values come from the official config.json and generation_config.json.

Capabilities

Text Generation

GLM-5.2 accepts text and produces text. It is suitable for:

• Coding
• Reasoning
• Analysis
• Summarization
• Document transformation
• Structured extraction
• Research synthesis
• Planning
• Agent control
• Tool orchestration

Reasoning Modes

Z.ai supports:

• Thinking enabled
• Thinking disabled
• high reasoning effort
• max reasoning effort

On self-hosted SGLang, the current chat template effectively exposes two levels:

• high: lower reasoning effort
• max: highest reasoning effort and the default

Values such as low or medium currently fall through to Max in that local template, so do not assume four distinct local levels merely because an OpenAI-style client accepts those strings.

Use High for ordinary coding, extraction, and routine transformations. Use Max when the task requires difficult planning, scientific reasoning, long-horizon debugging, or repeated tool decisions.

Function Calling

Function calling lets the model return a structured request to execute a tool instead of merely describing what should happen.

Examples:

• Query a database
• Read a file
• Run a test command
• Search documentation
• Call an internal API
• Create a ticket
• Retrieve an account record
• Execute a controlled deployment action

The hosted Z.ai API supports function calling. Local SGLang deployments should use the glm47 tool-call parser because GLM-5.2 emits the newer GLM tool-call format.

Structured Output

Z.ai documents structured output, including JSON-oriented responses. This is important for:

• Data extraction
• API integration
• Workflow state
• Classification
• Forms
• Validation pipelines

Structured output improves formatting consistency but does not guarantee factual correctness. Validate schemas and business rules in application code.

MCP Support

MCP, or Model Context Protocol, is a standard interface for connecting models to tools and data sources.

GLM-5.2 can be used with MCP systems for:

• Web search
• Documentation retrieval
• Internal data access
• Repository tools
• Database queries
• Specialized business applications

Z.ai’s Coding Plan also bundles access to selected MCP services, including web search, web reading, Zread, and vision understanding through a separate vision model.

Context Caching

Long-running agents often resend the same large prefix:

• System instructions
• Tool schemas
• Repository context
• Prior conversation
• Project rules
• Test history

Context caching lets a provider reuse previously processed input at a lower price. Z.ai charges $0.26 per million cached input tokens, compared with $1.40 for fresh input. Fireworks currently lists a lower cached-input rate of $0.14 for its Standard GLM-5.2 path.

Caching can make a much larger difference than a small change in output price for agents that repeatedly send 50K–500K-token prefixes.

Streaming

Streaming returns generated text as it is produced. It improves perceived responsiveness and is useful for long answers, coding agents, terminal interfaces, and interactive applications.

Is GLM-5.2 Multimodal?

No. GLM-5.2 is a text-input, text-output model.

It does not natively accept:

• Images
• Video
• Audio

For image understanding in the Z.ai ecosystem, use a vision model such as GLM-5V-Turbo or GLM-4.6V. The GLM Coding Plan can expose vision understanding through a separate Vision MCP service, but that does not make the underlying GLM-5.2 checkpoint multimodal.

This distinction matters for provider comparisons. A provider interface may let an application use separate tools or models alongside GLM-5.2; that is an orchestration feature, not native GLM-5.2 vision capability.

Where Can You Use GLM-5.2?

Availability Summary

A model’s native context limit and a provider’s endpoint limit are different. For example, the checkpoint supports 1,048,576 tokens, while Together AI currently advertises a 256K context for its serverless endpoint.

1. Z.ai Chat

The simplest way to try GLM-5.2 is the Z.ai chat interface.

Use this route when:

• You want to test answer quality before opening an API account.
• You need interactive reasoning or coding help.
• You do not need programmatic integration.
• You want to compare High and Max effort manually.

The chat product’s tools, quotas, and interface behavior may differ from the raw API.

2. Z.ai General API

The direct Z.ai endpoint is:

https://api.z.ai/api/paas/v4/chat/completions

The model ID is:

glm-5.2

The general API is the appropriate route for:

• Production applications
• Backend automation
• Batch workflows
• Custom agents
• Structured extraction
• Function calling
• Long-context applications
• Direct token-based billing

cURL Example

curl -X POST "https://api.z.ai/api/paas/v4/chat/completions" 
  -H "Content-Type: application/json" 
  -H "Authorization: Bearer $ZAI_API_KEY" 
  -d '{
    "model": "glm-5.2",
    "messages": [
      {
        "role": "system",
        "content": "You are a careful software architect. State assumptions and verify constraints."
      },
      {
        "role": "user",
        "content": "Review this migration plan and identify failure modes."
      }
    ],
    "thinking": {
      "type": "enabled"
    },
    "reasoning_effort": "high",
    "max_tokens": 4096,
    "temperature": 1.0
  }'

OpenAI Python SDK Example

import os
from openai import OpenAI

api_key = os.environ.get("ZAI_API_KEY")
if not api_key:
    raise RuntimeError("Set the ZAI_API_KEY environment variable.")

client = OpenAI(
    api_key=api_key,
    base_url="https://api.z.ai/api/paas/v4/",
)

response = client.chat.completions.create(
    model="glm-5.2",
    messages=[
        {
            "role": "system",
            "content": (
                "You are a senior engineer. Follow the stated constraints, "
                "identify uncertainty, and do not claim tests passed unless run."
            ),
        },
        {
            "role": "user",
            "content": "Create a staged plan for migrating this service from REST to gRPC.",
        },
    ],
)

print(response.choices[0].message.content)

The OpenAI-compatible interface reduces migration work, but provider-specific fields such as thinking and reasoning_effort may need to be sent as additional request fields depending on the SDK.

3. GLM Coding Plan

The GLM Coding Plan is a subscription designed for supported AI coding tools. It is not the same product as the general pay-as-you-go API.

Supported tools currently include:

• Claude Code
• Roo Code
• Kilo Code
• Cline
• OpenCode
• OpenClaw
• Crush
• Goose
• Cursor
• Additional documented integrations

The plan supports GLM-5.2, GLM-5-Turbo, and GLM-4.7, plus selected MCP services.

Current Standard Monthly Prices

Quarterly and annual discounts may be offered. Check the subscription page because promotions and billing terms can change.

Dedicated Endpoints

Do not use the normal general API URL when you intend to consume Coding Plan quota.

Critical Usage Restriction

The Coding Plan is strictly limited to officially supported tools and products. Z.ai states that unsupported SDK use or third-party scenarios can trigger restrictions, throttling, suspension, or a ban.

Therefore:

• Use the general API for your SaaS backend, automated production agent, scheduled job, or custom SDK application.
• Use the Coding Plan inside approved interactive coding tools.

The plan is intended for individual subscribers unless a Team plan is used. Account sharing can also trigger restrictions.

Sources: Coding Plan overview, Quick Start, and Usage Policy.

4. OpenRouter

Yes, GLM-5.2 is available on OpenRouter.

Use:

z-ai/glm-5.2

OpenRouter can expose one or more routes for the same model behind a single API. The available providers, effective price, context limit, speed, uptime, and discounts change dynamically, so inspect the live route list before deployment.

Why Use OpenRouter?

• One API and billing account for many models
• Automatic routing
• Provider selection
• Fallbacks
• Price/speed optimization
• Provider-specific privacy routing controls
• Easy A/B testing against alternative models

Routing Modes

OpenRouter currently describes modes including:

• Balanced: balances cost and speed
• Nitro: prioritizes speed
• Exacto: prioritizes tool-calling accuracy

For sensitive or regulated workloads, do not rely on automatic routing without reviewing which providers can receive the request. Pin an approved provider and inspect its data policy.

OpenRouter Python Example

import os
from openai import OpenAI

api_key = os.environ.get("OPENROUTER_API_KEY")
if not api_key:
    raise RuntimeError("Set the OPENROUTER_API_KEY environment variable.")

client = OpenAI(
    api_key=api_key,
    base_url="https://openrouter.ai/api/v1",
)

response = client.chat.completions.create(
    model="z-ai/glm-5.2",
    messages=[
        {
            "role": "user",
            "content": "Analyze this repository migration and return a risk register.",
        }
    ],
    extra_body={
        "reasoning": {"effort": "high"}
    },
)

print(response.choices[0].message.content)

OpenRouter documents high and xhigh reasoning modes for this model, with xhigh mapping to Max. Provider support can differ, so test the exact route rather than assuming every backend implements every optional field identically.

Source: OpenRouter GLM-5.2 page.

5. Fireworks AI

Fireworks hosts GLM-5.2 on its own infrastructure and exposes it through serverless and managed deployment options.

Fireworks is attractive when full-context hosting and a large cache discount matter. Premium or speed-optimized routes may be available, but their prices and model IDs should be checked on the live Fireworks page rather than treated as permanent.

Source: Fireworks GLM-5.2 model page.

6. Together AI

Together AI lists the following model endpoint:

zai-org/GLM-5.2

Its current model page lists:

• 256K context
• 131,072 maximum output tokens
• Serverless and provisioned throughput
• Function calling
• JSON mode
• $1.40 input
• $0.26 cached input
• $4.40 output per million tokens

This is a good example of why users must distinguish the model’s native 1M capacity from a provider’s endpoint limit. Together lists a 256K cap despite the checkpoint supporting 1M. Its page also contains staged-rollout language, so confirm that the endpoint is enabled for your account before designing a production dependency around it.

Together Python Example

import os
from together import Together

if not os.environ.get("TOGETHER_API_KEY"):
    raise RuntimeError("Set TOGETHER_API_KEY.")

client = Together()

response = client.chat.completions.create(
    model="zai-org/GLM-5.2",
    messages=[
        {
            "role": "user",
            "content": "Map the modules in this codebase and identify circular dependencies.",
        }
    ],
)

print(response.choices[0].message.content)

Source: Together AI GLM-5.2.

7. Novita AI

Novita lists GLM-5.2 as a serverless model with:

• Model ID: zai-org/glm-5.2
• Context: 1,048,576 tokens
• Maximum output: 131,072 tokens
• OpenAI-compatible base URL
• Text input and text output
• Reasoning and agent-oriented features

Example base URL:

https://api.novita.ai/openai

Novita is an option for users who want the full advertised context through a serverless API without managing a cluster.

Source: Novita GLM-5.2 model page.

8. Hugging Face

Hugging Face is the primary location for the official files:

• GLM-5.2 BF16
• GLM-5.2 FP8
• NVIDIA GLM-5.2 NVFP4

The model pages expose:

• Weights
• Configuration
• Tokenizer
• License
• Transformers examples
• vLLM examples
• SGLang examples
• Inference-provider integrations
• Community quantizations

Hugging Face’s “Browse Quantizations” links can surface community formats for llama.cpp, Ollama, LM Studio, and compatible applications. These are not necessarily created or validated by Z.ai. Check:

• Quantization method
• File size
• Context limit
• Tool-call support
• Reasoning parser support
• Quality loss
• RAM requirements
• Community reputation

API Pricing

Direct Z.ai Token Pricing

Example Request Costs

These examples exclude tool fees, retries, provider markups, and taxes.

100K Input + 10K Output

• Input: 0.1 × $1.40 = $0.14
• Output: 0.01 × $4.40 = $0.044
• Total: $0.184

1M Input + 100K Output

• Input: 1 × $1.40 = $1.40
• Output: 0.1 × $4.40 = $0.44
• Total: $1.84

500K Cached Input + 20K Output

• Cached input: 0.5 × $0.26 = $0.13
• Output: 0.02 × $4.40 = $0.088
• Total: $0.218

These calculations show why cache hit rate is central to long-agent economics. Reprocessing a 500K-token prefix as fresh input would cost $0.70 instead of $0.13.

Pricing Caveats

• OpenRouter prices and promotions are dynamic.
• Hosted providers may charge differently for cached input.
• Faster routes can cost more.
• Provider context limits vary.
• Very long requests can have lower rate limits.
• Tool calls may be billed separately.
• Coding Plan credits are not directly interchangeable with API dollars.
• Taxes and regional billing can change the final amount.

Official source: Z.ai pricing.

Local and Self-Hosted Deployment

Can GLM-5.2 Run Locally?

Yes, because the weights are public and MIT-licensed.

But the word local needs qualification.

GLM-5.2 can be self-hosted in your own data center, private cloud, GPU server, or cluster. The official full model is not a normal one-GPU desktop model.

Theoretical Weight Memory

Using the official 753B checkpoint figure:

These are raw weight estimates only. A real deployment also needs memory for:

• Key-value cache
• Runtime workspaces
• Expert routing
• Communication buffers
• CUDA kernels
• Tokenizer and server processes
• Batching
• Concurrent requests
• Long-context cache
• Framework overhead

Official Deployment Guidance

SGLang currently recommends:

• FP8 as the normal deployment choice
• BF16 only on an 8×B300 node or a multi-node setup
• H200, B200, B300, GB300, MI300X, MI325X, and MI355X as supported hardware families in its cookbook
• NVFP4 on supported NVIDIA Blackwell hardware for lower memory use

The BF16 checkpoint is approximately 1.5 TB and does not fit on:

• One 8×H200 node
• One 8×B200 node
• One 4×GB300 node

SGLang reports a verified single-node BF16 deployment on 8×B300, which provides roughly 2.1 TB of aggregate HBM. Smaller GPU configurations require multi-node distribution.

FP8 is much more practical, but still enterprise-scale. Eight H200 GPUs provide enough aggregate HBM for the raw FP8 weights and runtime headroom, although usable context and concurrency depend on cache configuration.

Official and Supported Formats

NVIDIA’s NVFP4 build quantizes the MoE experts’ linear weights and activations to four bits while leaving the shared expert unquantized. SGLang reports accuracy within approximately one point of the FP8 baseline on GPQA Diamond, SciCode, and IFBench for that build.

Supported Frameworks

The official model card lists:

• SGLang 0.5.13.post1 or newer
• vLLM 0.23.0 or newer
• Transformers 5.12.0 or newer
• KTransformers 0.5.12 or newer
• Unsloth 0.1.47-beta or newer
• vLLM-Ascend, xLLM, and SGLang for Ascend NPU deployments

Version requirements can move as support is merged upstream. Pin a tested container or lockfile for production.

SGLang Deployment Example

A basic FP8 launch looks like this:

python3 -m sglang.launch_server 
  --model-path "zai-org/GLM-5.2-FP8" 
  --host 0.0.0.0 
  --port 30000 
  --reasoning-parser glm45 
  --tool-call-parser glm47

A production command needs topology-specific options such as tensor parallelism, expert parallelism, memory fraction, chunked prefill, cache settings, and speculative decoding.

The SGLang cookbook provides verified recipes for different goals:

• Low latency
• Balanced serving
• High throughput
• Prefill/decode disaggregation
• Hierarchical key-value caching
• Context parallelism

Local Reasoning Example

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="EMPTY",
)

response = client.chat.completions.create(
    model="zai-org/GLM-5.2-FP8",
    messages=[
        {
            "role": "user",
            "content": "Analyze this deadlock and propose a minimal safe patch.",
        }
    ],
    extra_body={
        "chat_template_kwargs": {
            "enable_thinking": True,
            "reasoning_effort": "high",
        }
    },
)

message = response.choices[0].message
print("Reasoning:", getattr(message, "reasoning_content", None))
print("Answer:", message.content)

Tool Calling Parser

For SGLang:

• Use glm45 as the reasoning parser.
• Use glm47 as the tool-call parser.

Using the older tool parser can leave tool calls as raw text instead of structured message.tool_calls.

vLLM Deployment Example

The official FP8 page provides a minimal command:

vllm serve "zai-org/GLM-5.2-FP8"

For a large production deployment, specify the correct parallelism and parser settings for the cluster. NVIDIA’s NVFP4 example uses:

vllm serve nvidia/GLM-5.2-NVFP4 
  --tensor-parallel-size 8 
  --enable-expert-parallel 
  --trust-remote-code 
  --reasoning-parser glm45 
  --tool-call-parser glm47 
  --enable-auto-tool-choice 
  --kv-cache-dtype fp8_e4m3 
  --host 0.0.0.0 
  --port 8000

Do not copy an eight-GPU command onto arbitrary hardware and assume it will fit. Check:

• Per-GPU HBM
• Aggregate HBM
• Interconnect
• Quantization
• Context target
• Concurrency
• KV-cache precision
• Tensor/expert parallel layout

Can GLM-5.2 Run in Ollama or LM Studio?

Hugging Face exposes community quantizations compatible with llama.cpp, Ollama, LM Studio, and related applications.

The practical limits remain severe:

• A theoretical 4-bit model is around 376.5 GB before overhead.
• Community formats can be larger due to mixed precision and metadata.
• Long context requires additional memory.
• CPU or unified-memory inference may be extremely slow.
• Tool calling and reasoning-output parsing may not work identically.
• Quantization can reduce benchmark quality.
• A normal 32–128 GB workstation cannot hold the full model.

Therefore:

GLM-5.2 can appear in desktop model tools, but it is not a typical consumer-local model. Treat community desktop deployment as an advanced experiment requiring several hundred gigabytes of memory and storage, not a one-click laptop setup.

For a normal workstation, use a smaller GLM, Qwen, DeepSeek, or other 7B–70B-class model locally, and use GLM-5.2 through an API for difficult tasks.

Can It Run on a Mac?

A very high-memory Apple Silicon system may technically load certain community quantizations with enough unified memory or multi-machine techniques, but the full model remains several hundred gigabytes even at four bits.

Practical concerns include:

• Model download size
• Memory headroom
• Prompt cache
• Very long load time
• Low generation speed
• Limited community runtime validation
• Loss of quality from aggressive quantization

A MacBook or ordinary Mac Studio is not the intended platform. Cloud API access is generally more economical unless the organization has a strict privacy requirement and already owns appropriate high-memory hardware.

GLM-5.2 for Claude Code and Other Coding Agents

There are three main routes.

Route A: GLM Coding Plan

Use the dedicated Anthropic endpoint:

https://api.z.ai/api/anthropic

This is the simplest supported route for Claude Code under the plan.

Route B: A Hosted API Provider

Use OpenRouter, Fireworks, Z.ai, or another provider that offers an Anthropic-compatible API or an integration supported by the coding tool.

Route C: Self-Hosted SGLang

SGLang exposes an Anthropic-compatible /v1/messages endpoint. Launch with the reasoning and tool parsers, then point Claude Code to the local endpoint.

A local configuration pattern is:

export ANTHROPIC_BASE_URL="http://localhost:30000"
export ANTHROPIC_AUTH_TOKEN="local-placeholder"

Exact environment variables can change with Claude Code and SGLang versions. Follow the current integration guide rather than copying stale settings into production.

Agent Safety

A strong coding model can still:

• Modify unrelated files
• Weaken tests
• misread requirements
• Introduce dependencies
• Leak secrets into logs
• Run destructive commands
• Reward-hack an evaluation
• Declare success without verification

Use:

• Version control
• Isolated worktrees or containers
• Explicit scope
• Command allowlists
• Secret redaction
• Build and test gates
• Static analysis
• Human approval for releases
• Receipts or logs for long-running work

Z.ai itself discusses anti-hacking measures in GLM-5.2’s long-horizon reinforcement-learning process, including detecting attempts to read protected evaluation artifacts or fetch reference solutions.

Best Use Cases

1. Repository-Scale Architecture Analysis

GLM-5.2 can ingest a large amount of source, configuration, tests, documentation, and project history.

Ask it to produce:

• System architecture map
• Module responsibilities
• Data flows
• API contracts
• Dependency graph
• Security boundaries
• Technical debt
• Refactoring constraints
• Verification plan

This is a better first task than immediately asking it to rewrite the repository.

2. Long-Horizon Refactoring

Suitable tasks include:

• Splitting a monolith
• Migrating APIs
• Reorganizing modules
• Replacing a framework
• Cross-language porting
• Dependency cleanup
• Storage-schema migration
• Test-harness repair

Require the model to plan, execute in stages, run tests, and report unresolved risks.

3. Debugging Across Many Layers

A bug may involve:

• Frontend state
• API behavior
• Authentication
• Database transactions
• Background jobs
• Networking
• Mobile clients
• Deployment configuration

The 1M context and tool use can help the model follow evidence across these layers.

4. Mobile and Device Debugging

Z.ai specifically highlights Android workflows using:

• ADB
• Logcat
• Screenshots
• Runtime logs
• Permission state
• Background recovery
• Network reconnection

The model itself is text-only, so screenshots require a separate vision tool or model.

5. Research Reproduction

GLM-5.2 can help convert a paper into:

• Model implementation
• Data pipeline
• Training code
• Evaluation scripts
• Reproduction checklist
• Difference report

Do not assume a paper’s result has been reproduced merely because code runs. Compare metrics, seeds, data versions, hardware, and preprocessing.

6. Tool-Using Business Agents

Potential workflows:

• Internal support triage
• Documentation research
• Controlled data extraction
• Compliance evidence collection
• Incident analysis
• Release-note generation
• Repository maintenance
• Infrastructure diagnostics

High-impact actions should remain gated.

7. Long-Document Analysis

The model can analyze:

• Policy libraries
• Technical standards
• Contracts
• Audit logs
• Research collections
• Product documentation

For legal, medical, financial, or compliance use, treat the model as an analysis assistant, not an authority. Require source citations and expert review.

8. Structured Data Extraction

With JSON output and schema validation, it can transform documents into:

• Records
• Classifications
• Entities
• Relationships
• Risk registers
• Requirements
• Test cases

Validate every output before writing to a production database.

When GLM-5.2 Is the Wrong Choice

Use a Smaller Model When

• The task is a short classification.
• Latency matters more than deep reasoning.
• Prompts are below a few thousand tokens.
• Cost must be extremely low.
• You need high throughput for simple requests.
• You need consumer-device deployment.

Use a Vision Model When

• Inputs contain screenshots, photographs, diagrams, scanned pages, or video.
• OCR and layout understanding are central.
• The model must reason directly about images.

GLM-5.2 is text-only.

Use a Mature Proprietary Service When

• Your organization needs a specific enterprise certification.
• A provider’s support/SLA ecosystem matters more than open weights.
• The application relies on a proprietary computer-use environment.
• Your internal testing shows the proprietary model is more reliable.

Avoid Unsupervised Use When

• The model can execute financial transactions.
• It controls production infrastructure.
• It can publish externally.
• It handles safety-critical decisions.
• It can delete data.
• It can merge code without review.

GLM-5.2 vs GLM-5.1

The most meaningful comparison is with its predecessor because Z.ai evaluated both in the same launch framework.

GLM-5.2’s main upgrade is not a small general-knowledge improvement. The largest gains appear in agentic and long-horizon coding benchmarks.

Both direct APIs currently have the same published token price: $1.40 input, $0.26 cached input, and $4.40 output per million tokens. Unless a workload is unusually sensitive to behavior changes, GLM-5.2 is the logical default over GLM-5.1.

GLM-5.2 vs Current Frontier Models

The benchmark section near the beginning of this guide contains the full source-separated comparison. The practical differences are easier to summarize by deployment decision:

GLM-5.2 is most compelling when the requirements combine long context, strong coding, downloadable weights, a permissive MIT license, broad provider availability, and a route to private deployment. A proprietary model can still be the better choice when raw task success matters more than model ownership or serving control.

GLM-5.2 vs Other Open-Weight Models

Among open-weight models, GLM-5.2 is not the smallest, cheapest, or easiest to host. Its value is the combination of:

• 1M native context
• MIT licensing
• Strong long-horizon coding results
• A current Artificial Analysis Intelligence Index score of 51
• Multiple hosted providers
• Mature SGLang and vLLM support
• Direct support for coding-agent workflows

Choose another open-weight model when a smaller checkpoint fits the hardware, lower API cost is more important, native image input is required, or a model performs better in the target language and task. Cost per token is not cost per successful task; measure both.

Prompting GLM-5.2 Effectively

Use Explicit Engineering Constraints

Good prompts define:

• Goal
• Files or modules in scope
• Prohibited changes
• Required interfaces
• Test commands
• Security constraints
• Completion criteria
• Reporting format

Example:

Refactor the authentication module without changing public API signatures,
database schema, token format, or observable runtime behavior.

Before editing:
1. Map current data flow.
2. List affected files.
3. Identify invariants and rollback points.
4. Propose a staged implementation.

During execution:
- Do not add dependencies.
- Do not weaken or delete tests.
- Do not commit.
- Run format, lint, unit tests, and integration tests.

At completion:
- Report changed files.
- Report commands run and exact results.
- List unresolved risks.

Ask for a Plan Before Changes

For complex tasks, separate:

1. Exploration
2. Plan
3. Implementation
4. Verification
5. Closeout

This reduces premature edits and makes drift easier to detect.

Use Repository Instructions

Store persistent rules in a project instruction file such as:

• AGENTS.md
• CLAUDE.md
• Tool-specific project configuration

Include:

• Architecture boundaries
• Build commands
• Test requirements
• Code style
• Forbidden operations
• Secret-handling rules
• Commit policy
• Definition of done

Do Not Overfill the Context

A 1M window is not an invitation to dump everything.

Prioritize:

• Relevant source
• Interfaces
• Tests
• Errors
• Recent decisions
• Constraints
• Current plan

Remove:

• Generated build artifacts
• Duplicate logs
• Irrelevant dependencies
• Historical output no longer needed
• Secrets

Use Max Effort Selectively

Max effort can increase:

• Output length
• Latency
• Token cost
• Tool calls

Use it for the hardest steps, such as architecture, debugging, proof, and recovery. Use High or non-thinking mode for straightforward transformations.

How to Evaluate GLM-5.2 for Your Application

1. Build a Real Test Set

Use 50–200 tasks sampled from actual work:

• Easy
• Typical
• Difficult
• Ambiguous
• Failure recovery
• Long context
• Tool use
• Security-sensitive edge cases

Do not rely only on public benchmark tasks.

2. Keep the Environment Constant

For every model, keep constant:

• Prompt
• Repository state
• Tool definitions
• Network access
• Time budget
• Token budget
• Number of retries
• Hardware class
• Success criteria
• Judge

Changing Claude Code to OpenHands while changing the model makes the comparison uncontrolled.

3. Measure Task Success

Track:

• Fully completed tasks
• Partially completed tasks
• Incorrect completion claims
• Test pass rate
• Human correction time
• Rework
• Regressions
• Tool errors
• Destructive actions
• Cost
• End-to-end time

Tokens per second alone does not measure agent productivity.

4. Test Long Context Directly

Create tests at:

• 32K
• 128K
• 256K
• 500K
• 750K
• Near 1M

Place critical facts near:

• Beginning
• Middle
• End

Measure:

• Retrieval accuracy
• Instruction adherence
• Contradiction handling
• Latency
• Cache behavior
• Cost
• Tool reliability

5. Compare Reasoning Levels

Test:

• Thinking disabled
• High effort
• Max effort

A lower mode may complete simple tasks faster and more cheaply with no meaningful quality loss.

6. Test Every Hosted Provider Separately

The same weights can behave differently because of:

• Quantization
• Prompt formatting
• Parser
• Context cap
• Output cap
• Sampling defaults
• Cache implementation
• Tool-call handling
• Throughput
• Timeouts

Do not assume an OpenRouter route, Together endpoint, Z.ai endpoint, and local FP8 server are interchangeable.

7. Include Security Tests

Try to make the agent:

• Read secret files
• Exfiltrate tokens
• Bypass tests
• Modify protected configuration
• Use the network unexpectedly
• Execute destructive shell commands
• Claim success without evidence
• Follow malicious repository instructions

Use sandboxing and allowlists regardless of benchmark performance.

Limitations

Text Only

GLM-5.2 cannot directly analyze images, video, or audio.

Enormous Self-Hosting Footprint

The official model requires hundreds of gigabytes to more than 1.5 TB of weight memory depending on precision.

Long Context Is Expensive

A million-token request can increase:

• Prefill latency
• cache usage
• memory pressure
• queue time
• total cost

Provider Differences

Some providers expose 1M context; Together currently exposes 256K. Prices and parser behavior also differ.

Benchmark Harness Dependence

Agent scores can move significantly when the harness changes.

Verbosity

Independent analysis found GLM-5.2 Max relatively verbose. This can increase cost and slow long agent loops.

Open Weights Do Not Eliminate Operational Risk

Self-hosting transfers responsibility for:

• Security
• Patching
• access control
• monitoring
• logging
• abuse prevention
• data governance
• scaling
• uptime

Quantization Trade-Offs

FP8 and NVFP4 reduce memory and can improve speed, but quality must be tested on your workload. Community four-bit builds may diverge more.

No Warranty

The MIT license provides the software as-is without warranty.

Frequently Asked Questions

Is GLM-5.2 free?

The weights can be downloaded and used under the MIT License without a model-license fee. Compute, storage, bandwidth, hosted API calls, and engineering work are not free. Z.ai and other providers charge for API usage, while the Coding Plan is subscription-based.

Is GLM-5.2 available on OpenRouter?

Yes. The model ID is z-ai/glm-5.2.

What is the official Z.ai API model name?

Use glm-5.2.

What is the Hugging Face model name?

Use zai-org/GLM-5.2 for BF16 or zai-org/GLM-5.2-FP8 for the official FP8 checkpoint.

How much does GLM-5.2 cost?

The direct Z.ai API charges $1.40 per million fresh input tokens, $0.26 per million cached input tokens, and $4.40 per million output tokens as of August 5, 2026.

How large is GLM-5.2?

The official Hugging Face metadata lists 753B parameters. Several descriptions use a 744B backbone figure and approximately 40B active parameters per token.

What does 40B active mean?

The model has many expert subnetworks, but it routes each token through only a selected subset. Approximately 40B parameters participate in processing a token, while the full weight set remains stored across the serving system.

How many experts does it have?

The official configuration lists 256 routed experts, eight selected per token, and one shared expert.

Is GLM-5.2 multimodal?

No. It accepts text and outputs text.

What is its context window?

The native model limit is 1,048,576 tokens. A hosted provider may expose a lower limit.

What is its maximum output?

Z.ai documents up to 128K output tokens. Novita and Together list 131,072. Providers can set different caps.

Is GLM-5.2 good for coding?

Yes. Coding and long-horizon agents are its central focus. Z.ai reports 62.1 on SWE-bench Pro, 81.0 on Terminal-Bench 2.1 with Terminus-2, and 74.4 FrontierSWE dominance. Later comparison tables show that Kimi K3, GPT-5.6 Sol, and Claude Fable 5 lead it on several current agentic-coding tests, so test the models on the same repository and harness before adopting one.

Can GLM-5.2 run on one GPU?

Not the full official model on ordinary current GPUs. FP8 raw weights are roughly 753 GB. Deployment normally requires a multi-GPU server or cluster. Extremely compressed community builds remain several hundred gigabytes.

Can it run on an RTX 4090 or 5090?

Not as the full model. A 24–32 GB consumer GPU cannot hold hundreds of gigabytes of weights. Heavy CPU/RAM offload would still require hundreds of gigabytes and would be slow.

Can it run in Ollama or LM Studio?

Community quantizations may be available, but they still require several hundred gigabytes of memory/storage and may lose quality or features. This is not typical consumer-local deployment.

What is the best format for self-hosting?

Z.ai and SGLang recommend the official FP8 build for practical serving. BF16 requires roughly 1.5 TB for weights. NVIDIA’s NVFP4 build is an option on supported Blackwell hardware.

Does it support vLLM?

Yes, with vLLM 0.23.0 or newer according to the model card.

Does it support SGLang?

Yes, with SGLang 0.5.13.post1 or newer. SGLang has a dedicated GLM-5.2 deployment cookbook.

Does it support function calling?

Yes. For local SGLang, use the glm47 tool-call parser.

Does it support JSON output?

Z.ai documents structured output, including JSON-oriented output.

Can I use GLM-5.2 commercially?

The model weights use the MIT License, which permits commercial use. You must preserve the license notice and comply with applicable law and any hosted provider’s terms.

Can I fine-tune GLM-5.2?

The license allows modification, but full fine-tuning is computationally extreme. Parameter-efficient training and managed fine-tuning may be more practical, but still require substantial infrastructure because the base model is enormous.

Is the GLM Coding Plan usable for my production API?

No. Z.ai restricts the Coding Plan to officially supported tools and products. Use the normal general API or a hosted provider for production backend workloads.

Which provider is best?

There is no universal answer:

• Use Z.ai for the direct model-maker API and complete documented feature set.
• Use OpenRouter for routing, fallback, and easy comparison.
• Use Fireworks for full context, strong cache pricing, and managed deployment options.
• Use Together when its 256K cap is sufficient and its platform fits your stack.
• Use Novita for a serverless API advertising the full 1M context.
• Self-host when data control justifies the hardware and operational cost.

Is GLM-5.2 better than Claude or GPT?

It is competitive on several coding and long-horizon benchmarks and usually cheaper, but it does not win every test. Proprietary models lead some reasoning, software, and tool benchmarks. GLM-5.2’s major advantages are MIT-licensed weights, self-hosting, 1M context, and cost.

Is GLM-5.2 private?

Self-hosting can keep prompts and outputs within infrastructure you control. Hosted privacy depends on the selected provider, account settings, data-retention policy, logging, and region. Open weights do not automatically make a hosted endpoint private.

Practical Selection Guide

Final Assessment: The Definitive Verdict

GLM-5.2 isn’t competing for the absolute top spot on every leaderboard—and that’s exactly why it’s brilliant. It is arguably the most strategically important open-weight release of 2026 because it forces a recalibration of how we build agentic systems.

It combines four properties that redefine the baseline:

1. Frontier-Scale Capacity: A 753B checkpoint (40B active) that doesn’t compromise on reasoning.
2. Built for Agents: Native 1M context with DeepSeek Sparse Attention, designed for infinite-loop coding tools.
3. Disruptive Economics: At $1.40/1M input tokens, it destroys the proprietary margin for long-context workloads.
4. Pure Open Source: The MIT license means you own your infrastructure, free from vendor lock-in.

Yes, Claude Opus 5 and GPT-5.6 Sol will still win in raw, zero-shot benchmarks. If you’re running a handful of extremely complex, high-stakes tasks where cost is irrelevant, stay with the proprietary leaders.

But if you are building continuous autonomous agents, repository-scale refactoring pipelines, or thousands of parallel tool-using workflows, GLM-5.2 is your engine. It delivers the context, the capability, and the licensing freedom to run at scale without bankrupting your project.

The verdict is simple: GLM-5.2 is the new open-weight standard for long-horizon autonomous work. Stop benchmarking, start building.

Sources and Further Reading

Official Z.ai and Model Files

1. GLM-5.2 launch: Built for Long-Horizon Tasks
2. Z.ai GLM-5.2 model documentation
3. Z.ai API pricing
4. GLM Coding Plan overview
5. GLM Coding Plan quick start
6. GLM Coding Plan usage policy
7. GLM-5.2 BF16 on Hugging Face
8. GLM-5.2 FP8 on Hugging Face
9. Official GLM-5.2 configuration
10. Official MIT license
11. GLM-5 technical report
12. IndexCache / sparse-attention research

Deployment

13. SGLang GLM-5.2 deployment cookbook
14. NVIDIA GLM-5.2 NVFP4 checkpoint
15. GLM-5 GitHub repository

Hosted Providers

16. OpenRouter GLM-5.2
17. Together AI GLM-5.2
18. Fireworks GLM-5.2 model page
19. Novita AI GLM-5.2

Independent Evaluation

20. Artificial Analysis GLM-5.2 Max
21. Artificial Analysis launch analysis
22. Artificial Analysis GLM-5.2 non-reasoning

Current Frontier Comparisons

23. Moonshot AI Kimi K3 model card and comparison table
24. OpenAI GPT-5.6 model benchmark report
25. Anthropic: What’s new in Claude Opus 5
26. MiniMax M2.7 official model card
27. Artificial Analysis Claude Opus 5
28. Artificial Analysis Claude Fable 5
29. Artificial Analysis GPT-5.6 Sol
30. Artificial Analysis Kimi K3
31. Artificial Analysis MiniMax M3
32. Artificial Analysis MiniMax M2.7
33. Qwen3.8 launch page

Model availability, provider routing, prices, rate limits, context caps, and leaderboard positions can change. Recheck the official pages before publishing a purchasing recommendation or deploying a production system.