TL;DR
GLM-5.1 is Z.AI's next-generation flagship model, released April 2026. It's built for agentic engineering: long-running coding tasks, autonomous optimization loops, and complex software projects that require hundreds of iterations. It ranks #1 on SWE-Bench Pro with 58.4, leads on Terminal-Bench 2.0 with 69.0, and improves on GLM-5 across the major coding benchmarks. Open weights are available under the MIT License.
Introduction
Most AI coding agents improve quickly for the first few tool calls, then plateau. After that, more runtime often means more logs, more retries, and little actual progress. You either babysit the agent or accept a partial result.
GLM-5.1 is designed for a different pattern: long-horizon agentic work. Z.AI, the team behind the GLM model family at Zhipu AI, released GLM-5.1 in April 2026 as its most capable model for agentic tasks. The key claim is not just single-pass benchmark performance. It is the ability to keep making useful progress across long runs: hundreds of iterations, thousands of tool calls, and multi-hour optimization loops.
If you are building API-backed agents, this matters operationally. You need to test async outputs, tool-call sequences, streaming responses, retries, and chained API calls before production. Apidog Test Scenarios can help you model those multi-step API workflows and validate that your integration handles long-running agent behavior correctly.
What is GLM-5.1?
GLM-5.1 is a large language model from Zhipu AI, released through the Z.AI developer platform in April 2026. "GLM" stands for General Language Model, a model architecture Zhipu has been developing since 2021.
GLM-5.1 is the successor to GLM-5, which launched in late 2025. The 5.1 release focuses on agentic capabilities:
- working autonomously on long-running coding tasks
- running and analyzing tests across many iterations
- optimizing software based on benchmark feedback
- using tools repeatedly without frequent human intervention
- maintaining useful context across large code and log histories
Z.AI positions GLM-5.1 as a model for agentic engineering rather than primarily as a general chatbot, creative writing model, or pure reasoning model.
The model weights are publicly available on Hugging Face under the MIT License. You can run it locally with vLLM or SGLang, or access it through the BigModel API or the Z.AI developer platform.
GLM-5.1 benchmark performance
Z.AI published benchmark results comparing GLM-5.1 with GLM-5, GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro. The results cover software engineering, reasoning, and agentic tasks.
Software engineering benchmarks
| Benchmark | GLM-5.1 | GLM-5 | GPT-5.4 | Opus 4.6 | Gemini 3.1 Pro |
|---|---|---|---|---|---|
| SWE-Bench Pro | 58.4 | 55.1 | 57.7 | 57.3 | 54.2 |
| NL2Repo | 42.7 | 35.9 | 41.3 | 49.8 | 33.4 |
| Terminal-Bench 2.0 | 69.0 | 56.2 | 75.1 | 65.4 | 68.5 |
| CyberGym | 68.7 | 48.3 | — | 66.6 | — |
Implementation takeaway:
- Use GLM-5.1 when your workload looks like SWE-Bench: issue resolution, bug fixing, PR generation, and repository-level coding.
- Expect strong improvement over GLM-5 on coding benchmarks.
- For terminal-heavy tasks, GPT-5.4 scores higher on Terminal-Bench 2.0, but GLM-5.1 still shows a large gain over GLM-5.
Reasoning benchmarks
| Benchmark | GLM-5.1 | GLM-5 | GPT-5.4 | Opus 4.6 | Gemini 3.1 Pro |
|---|---|---|---|---|---|
| HLE w/ Tools | 52.3 | 50.4 | 52.1* | 53.1* | 51.4* |
| AIME 2026 | 95.3 | 95.4 | 98.7 | 95.6 | 98.2 |
| HMMT Nov. 2025 | 94.0 | 96.9 | 95.8 | 96.3 | 94.8 |
| GPQA-Diamond | 86.2 | 86.0 | 92.0 | 91.3 | 94.3 |
GLM-5.1 is competitive on reasoning benchmarks, but it is not the overall leader. GPT-5.4 and Gemini 3.1 Pro lead on AIME 2026 and GPQA-Diamond.
For implementation decisions, this means GLM-5.1 is better evaluated as a coding and agentic model than as a general-purpose reasoning model.
Agentic task benchmarks
| Benchmark | GLM-5.1 | GLM-5 | GPT-5.4 | Opus 4.6 | Gemini 3.1 Pro |
|---|---|---|---|---|---|
| BrowseComp w/ Context | 79.3 | 75.9 | 82.7 | 84.0 | 85.9 |
| MCP-Atlas Public | 71.8 | 69.2 | 67.2 | 73.8 | 69.2 |
| Tool-Decathlon | 40.7 | 38.0 | 54.6 | 47.2 | 48.8 |
| Agentic | 68.0 | 62.0 | — | — | — |
On MCP-Atlas, GLM-5.1 scores 71.8. On BrowseComp and Tool-Decathlon, it is mid-tier. The Agentic benchmark shows the clearest improvement over GLM-5: 68.0 vs 62.0.
What makes GLM-5.1 different: long-horizon optimization
The benchmark tables are useful, but the more important signal is how GLM-5.1 behaves after many iterations.
Most coding models make early progress, then stop improving. GLM-5.1 is designed to remain useful across longer agent loops where the model repeatedly:
- edits code
- runs tests or benchmarks
- reads failures and logs
- identifies bottlenecks
- changes strategy
- repeats the loop
Scenario 1: vector database optimization over 600 iterations
Z.AI ran GLM-5.1 on a vector search optimization task using the SIFT-1M dataset. The model received a Rust skeleton and had to maximize queries per second while keeping recall above 95%.
Instead of using a short fixed turn budget, Z.AI allowed an outer loop where GLM-5.1 could continue iterating.
Reported results:
- Best single-session result across all models: 3,547 QPS from Claude Opus 4.6
- GLM-5.1 after 600+ iterations and 6,000+ tool calls: 21,500 QPS
- Approximate improvement over the best single-session result: 6x
The key detail is that progress was not linear. GLM-5.1 made structural changes after analyzing its benchmark logs:
- Around iteration 90, it moved from full-corpus scanning to IVF cluster probing with f16 vector compression, improving from about 3,500 QPS to 6,400 QPS.
- Around iteration 240, it introduced a two-stage pipeline using u8 prescoring with f16 reranking, reaching 13,400 QPS.
- Across the full run, six structural transitions occurred.
For developers building coding agents, this is the pattern to test: can the model use benchmark feedback to change architecture, not just tweak syntax?
Scenario 2: GPU kernel optimization over 1,000+ turns
Z.AI also tested GLM-5.1 on GPU kernel optimization. The task was to start from reference PyTorch code and generate faster CUDA kernels.
Reported results:
- GLM-5.1 reached a 3.6x speedup over baseline.
- Claude Opus 4.6 reached 4.2x and still showed headroom at the end of the run.
- GLM-5 plateaued earlier and finished lower.
This shows both the strength and the limitation: GLM-5.1 sustains progress longer than GLM-5, but Claude Opus 4.6 leads on this specific GPU optimization task.
Context window and technical specs
GLM-5.1 supports a 200K token context window. That matters for agentic coding because long-running sessions accumulate:
- source files
- test output
- stack traces
- benchmark logs
- tool call history
- previous failed attempts
- generated patches
| Spec | Value |
|---|---|
| Context window | 200,000 tokens |
| Max output | 163,840 tokens |
| Architecture | Autoregressive transformer, GLM family |
| License | MIT, open weights |
| Inference frameworks | vLLM, SGLang |
| Model weights | Hugging Face, zai-org |
Availability and pricing
GLM-5.1 is available through three main channels.
1. BigModel API
BigModel API is the primary developer API at bigmodel.cn.
Use the model name:
glm-5.1
The API uses a quota system rather than per-token billing. According to Z.AI:
- GLM-5.1 consumes 3x quota during peak hours.
- GLM-5.1 consumes 2x quota during off-peak hours.
- Through the end of April 2026, off-peak usage is billed at 1x as a limited-time promotion.
- Peak hours are 14:00–18:00 UTC+8 daily.
2. GLM Coding Plan
The Z.AI Coding Plan is for developers using AI coding assistants. GLM-5.1 is available to Coding Plan subscribers.
Supported tools include:
- Claude Code
- Cline
- Kilo Code
- Roo Code
- OpenCode
- Droid
To switch a compatible coding assistant, update the model name in its config to glm-5.1.
Pricing starts at $10/month.
3. Local deployment
The model weights are available on Hugging Face at:
zai-org/GLM-5.1
Supported inference frameworks include:
- vLLM
- SGLang
Deployment docs are available in the official GitHub repository.
How to call GLM-5.1 through the API
The BigModel API is OpenAI-compatible. That means you can usually point existing OpenAI-style clients at the BigModel endpoint and change the model name.
Endpoint from the published API guidance:
https://open.bigmodel.cn/api/paas/v4/chat/completions
Example request with curl:
curl https://open.bigmodel.cn/api/paas/v4/chat/completions \
-H "Authorization: Bearer $BIGMODEL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "glm-5.1",
"messages": [
{
"role": "system",
"content": "You are a coding assistant. Return concise implementation steps."
},
{
"role": "user",
"content": "Write a Python function that validates an API response schema."
}
]
}'
A minimal OpenAI-compatible client flow looks like this:
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.BIGMODEL_API_KEY,
baseURL: "https://open.bigmodel.cn/api/paas/v4"
});
const response = await client.chat.completions.create({
model: "glm-5.1",
messages: [
{
role: "user",
content: "Generate a test plan for an API workflow with retries and streaming."
}
]
});
console.log(response.choices[0].message.content);
When testing agent workflows, validate more than the final response. For long-running coding agents, you should also test:
- timeout handling
- retry behavior
- streaming chunks
- tool-call ordering
- malformed tool arguments
- partial failures
- idempotency across repeated calls
- log size and context growth
GLM-5.1 vs GLM-5: what changed
GLM-5 was already a strong coding model. GLM-5.1 improves it mainly by extending how long the model remains useful during autonomous work.
The difference is not only first-pass quality. On most benchmarks, GLM-5.1 leads GLM-5 by roughly 3–7 points. The bigger difference appears when both models are given extended time.
Reported examples:
- On the vector search benchmark, GLM-5 plateaued around 8,000–10,000 QPS with extended time.
- GLM-5.1 reached 21,500 QPS.
- On GPU kernel optimization, GLM-5 finished lower and earlier than GLM-5.1.
- On the Linux desktop task, GLM-5 produced a skeleton and stopped.
For implementation, GLM-5.1 is the better fit when your system depends on the model continuing to make decisions without manual redirection.
GLM-5.1 vs competitors
GLM-5.1 vs Claude Opus 4.6
GLM-5.1 leads Claude Opus 4.6 on:
- SWE-Bench Pro: 58.4 vs 57.3
- CyberGym: 68.7 vs 66.6
Claude Opus 4.6 leads on:
- NL2Repo: 49.8 vs 42.7
- GPU kernel optimization
- BrowseComp
For high-volume agent loops, GLM-5.1 through BigModel API or the Coding Plan is positioned for developers who want sustained coding-agent usage without relying only on closed frontier models.
GLM-5.1 vs GPT-5.4
GPT-5.4 leads on:
- Terminal-Bench 2.0: 75.1 vs 69.0
- most reasoning benchmarks
GLM-5.1 leads on:
- SWE-Bench Pro: 58.4 vs 57.7
- MCP-Atlas: 71.8 vs 67.2
For developers in China or those building on Chinese AI infrastructure, BigModel API access to GLM-5.1 is notably easier than GPT-5.4 access.
GLM-5.1 vs Gemini 3.1 Pro
Gemini 3.1 Pro leads on:
- AIME 2026
- GPQA-Diamond
- BrowseComp
GLM-5.1 leads on:
- SWE-Bench Pro
- Terminal-Bench 2.0
- CyberGym
For code-first use cases, GLM-5.1 is the stronger choice based on the published results. For general reasoning and document analysis, Gemini has the edge.
Use cases GLM-5.1 is best suited for
Autonomous coding agents
Use GLM-5.1 when the agent needs to:
- inspect a repository
- propose a patch
- run tests
- read failures
- revise the patch
- repeat until the task is complete
The 200K context window helps with long sessions where the model needs access to code, logs, and previous attempts.
AI coding assistants
GLM-5.1 is supported in the Z.AI Coding Plan for tools including Claude Code, Cline, Kilo Code, Roo Code, and OpenCode.
A typical setup change is simply updating the configured model name:
{
"model": "glm-5.1"
}
Exact config shape depends on the tool you use.
Software engineering automation
GLM-5.1 is a strong candidate for SWE-Bench-style pipelines:
- GitHub issue resolution
- bug fix generation
- pull request drafting
- regression test repair
- repository-level refactoring
Competitive programming and performance optimization
The long-horizon behavior is useful for workloads where the model can run experiments and adapt:
- GPU kernel tuning
- benchmark-driven optimization
- algorithm refinement
- vector search optimization
- performance regression investigation
Less suitable use cases
GLM-5.1 is not primarily positioned for:
- general-purpose chatbots
- creative writing
- document Q&A where reasoning quality matters more than code output
- tasks where GPT-5.4 or Gemini 3.1 Pro lead on reasoning benchmarks
How to try GLM-5.1 today
Option 1: Use the Z.AI chat interface
The fastest path is the Z.AI chat interface at:
z.ai
It runs GLM-5.1 by default and does not require an API key for the chat interface.
Option 2: Use the BigModel API
- Create an account at
bigmodel.cn. - Generate an API key.
- Use the OpenAI-compatible endpoint.
- Set the model to
glm-5.1.
Minimal request shape:
{
"model": "glm-5.1",
"messages": [
{
"role": "user",
"content": "Review this patch and suggest test cases."
}
]
}
Option 3: Deploy locally
For local deployment:
- Download the weights from Hugging Face under
zai-org/GLM-5.1. - Choose vLLM or SGLang.
- Follow the official setup instructions from the GitHub repository.
- Run a small completion test before connecting it to an agent loop.
Testing checklist for GLM-5.1 agent integrations
Before shipping a GLM-5.1-powered coding agent, test the integration like any other production API workflow.
Use this checklist:
- [ ] Can the client handle long-running responses?
- [ ] Are streaming chunks parsed correctly?
- [ ] Are tool calls validated before execution?
- [ ] Are failed tool calls retried safely?
- [ ] Are repeated tool calls idempotent where needed?
- [ ] Is context truncated or summarized safely before hitting limits?
- [ ] Are benchmark logs stored for later inspection?
- [ ] Are API timeouts and quota errors handled?
- [ ] Can the agent resume after interruption?
- [ ] Are generated code changes tested before merge?
For API workflow validation, you can model these flows with Apidog: create chained requests, simulate retries, and verify that each step behaves correctly before connecting the workflow to production automation.
Conclusion
GLM-5.1 is a significant upgrade over GLM-5 for long-running agentic coding work. Its SWE-Bench Pro ranking and the 600-iteration vector search demonstration make it a serious option for autonomous coding workflows.
It does not lead every benchmark. Claude Opus 4.6 and GPT-5.4 are stronger on some reasoning, GPU optimization, and agentic tasks. But for developers who want sustained coding agents, open weights, and MIT-licensed deployment options, GLM-5.1 is worth evaluating.
The MIT license is especially important: you can run GLM-5.1 locally, fine-tune it, and deploy it in your own infrastructure without usage restrictions from the model license.
FAQ
What does GLM stand for?
GLM stands for General Language Model. It is the model architecture Zhipu AI has been developing since 2021, based on autoregressive blank infilling rather than the decoder-only approach used by GPT-family models.
Is GLM-5.1 open source?
Yes. The model weights are released under the MIT License on Hugging Face at zai-org/GLM-5.1. MIT is a permissive license that allows commercial use, fine-tuning, and redistribution.
What context window does GLM-5.1 support?
GLM-5.1 supports a 200,000-token context window, with a maximum output of 163,840 tokens.
How does GLM-5.1 compare to DeepSeek-V3.2?
Z.AI's benchmarks show GLM-5.1 leading DeepSeek-V3.2 on software engineering tasks. On reasoning benchmarks, DeepSeek-V3.2 is competitive. For coding agents specifically, GLM-5.1 is the stronger choice based on the published data.
Can I use GLM-5.1 with Claude Code or Cursor?
Yes. The Z.AI Coding Plan supports Claude Code, Cline, Kilo Code, Roo Code, and OpenCode through the BigModel API. You update the model name in your coding assistant config file. Plans start at $10/month.
How do I access GLM-5.1 via API?
Create an account at bigmodel.cn, generate an API key, and use model name glm-5.1 in requests to:
https://open.bigmodel.cn/api/paas/v4/chat/completions
Is GLM-5.1 available for free?
The Z.AI chat interface at z.ai is free to use. API access through BigModel uses a quota system with paid plans. Off-peak usage is billed at 1x quota through the end of April 2026 as a promotional rate.
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