TL;DR
GLM-5.1 is Z.AI's next-generation flagship model, released April 2026. It's purpose-built for agentic engineering: long-running coding, autonomous optimization, and complex software projects needing hundreds of iterations. It leads major coding benchmarks (SWE-Bench Pro #1 at 58.4), outperforms GLM-5, and is available as open weights under the MIT License.
Introduction
Most AI models stall after a few dozen tool calls on coding tasks—early progress, then diminishing returns. You end up micromanaging the agent or settling for subpar results.
GLM-5.1 is designed to break through that plateau. Released by Zhipu AI in April 2026, it’s optimized for agentic tasks: sustained progress across 600+ iterations, 8+ hours, and thousands of tool calls. The focus isn’t just on first-pass scores, but long-horizon effectiveness.
💡 Tip: If you're integrating AI APIs or testing multi-step agent workflows, you need to know if your stack can handle GLM-5.1’s async outputs, tool sequences, and streaming responses. Apidog’s Test Scenarios let you define and verify these chains before production.
What is GLM-5.1?
GLM-5.1 is a large language model from Zhipu AI, released on the Z.AI developer platform in April 2026. "GLM" stands for General Language Model—an architecture focused on agentic engineering since 2021.
GLM-5.1 succeeds GLM-5 (late 2025) and is engineered for agentic capabilities: working autonomously on long, iterative tasks without frequent human intervention.
- Not a general chatbot or creative writing model.
- Designed for: software engineering, optimization loops, multi-iteration code writing/execution.
You can run GLM-5.1 locally (vLLM/SGLang), or via API on BigModel or Z.AI. Weights are open on Hugging Face under the MIT License.
GLM-5.1 Benchmark Performance
Z.AI benchmarked GLM-5.1 vs. GLM-5, GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro, across engineering, reasoning, and agentic tasks.
Software Engineering
| 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 | — |
- GLM-5.1 is #1 on SWE-Bench Pro (autonomous engineering).
- On Terminal-Bench 2.0, GPT-5.4 leads, but GLM-5.1 outpaces GLM-5.
- NL2Repo: Claude Opus 4.6 wins, but GLM-5.1 beats GLM-5 by 6.8 points.
Reasoning
| 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 but not dominant in reasoning.
Agentic Tasks
| 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 | — | — | — |
- GLM-5.1 leads MCP-Atlas and improves agentic scores over GLM-5.
What Makes GLM-5.1 Different: Long-Horizon Optimization
Benchmarks only tell part of the story. The key: GLM-5.1 maintains progress over long, multi-iteration runs, not just quick wins.
Scenario 1: Vector Database Optimization (600+ Iterations)
GLM-5.1 optimized vector search (SIFT-1M dataset) in Rust, maximizing QPS >95% recall over 600+ iterations.
- Best single-pass result: 3,547 QPS (Opus 4.6)
- GLM-5.1, after 600+ iterations/6,000+ tool calls: 21,500 QPS (6x better)
- Key: Model made structural changes after analyzing its own logs, adapting strategy at several milestones.
Scenario 2: GPU Kernel Optimization (1,000+ Turns)
GLM-5.1, GLM-5, and Opus 4.6 tuned PyTorch code to faster CUDA kernels.
- GLM-5.1: 3.6x speedup (over baseline)
- Opus 4.6 led at 4.2x; GLM-5 plateaued and finished lower.
- GLM-5.1 sustains improvement longer than GLM-5.
Context Window and Technical Specs
A 200K token context window enables long agentic runs—key for large histories and codebases.
| Spec | Value |
|---|---|
| Context window | 200,000 tokens |
| Max output | 163,840 tokens |
| Architecture | Autoregressive transformer |
| License | MIT (open weights) |
| Inference frameworks | vLLM, SGLang |
| Model weights | HuggingFace (zai-org) |
Availability and Pricing
GLM-5.1 is accessible via:
-
BigModel API (bigmodel.cn): Use
glm-5.1as model name. Pricing is quota-based (3x quota peak, 2x off-peak; 1x off-peak promo until end April 2026). Peak: 14:00-18:00 UTC+8. - GLM Coding Plan (Z.AI): Subscription for AI coding assistants (Claude Code, Cline, etc.), starting at $10/month. Enable by updating model name in config.
-
Local Deployment: Weights on HuggingFace (
zai-org/GLM-5.1). Run with vLLM/SGLang. Deployment docs on GitHub.
GLM-5.1 vs GLM-5: What's Changed
GLM-5.1 extends the useful work window—not just better first-pass performance (3-7 points higher on most benchmarks), but more sustained progress:
- GLM-5: Plateaued around 8,000–10,000 QPS (vector search).
- GLM-5.1: Reached 21,500 QPS.
- On GPU kernel and Linux desktop tasks, GLM-5.1 continues where GLM-5 stalls.
- Still, other models (Opus 4.6) lead on some tasks.
GLM-5.1 vs. Competitors
vs. Claude Opus 4.6
- GLM-5.1 leads on SWE-Bench Pro (58.4 vs 57.3) and CyberGym (68.7 vs 66.6).
- Opus 4.6 leads on NL2Repo, GPU kernel, and BrowseComp.
- Claude API is pricier; GLM-5.1 offers developer-friendly pricing.
vs. GPT-5.4
- GPT-5.4 leads Terminal-Bench 2.0 (75.1 vs 69.0) and reasoning.
- GLM-5.1 leads SWE-Bench Pro (58.4 vs 57.7), MCP-Atlas (71.8 vs 67.2).
- GLM-5.1 is easier to access via Chinese infrastructure.
vs. Gemini 3.1 Pro
- Gemini leads on reasoning, BrowseComp.
- GLM-5.1 leads for code-first use cases (SWE-Bench Pro, Terminal-Bench 2.0).
- Gemini is stronger for general reasoning and document analysis.
Use Cases: Where GLM-5.1 Excels
- Autonomous coding agents: Long-running, self-directed code generation, testing, and iteration. See how AI agent memory works.
- AI coding assistants: Integrated with Claude Code, Cline, etc., via the Z.AI Coding Plan.
- Software engineering automation: Automated GitHub issue resolution, PR generation, bug fixes (SWE-Bench Pro #1).
- Competitive programming/optimization: GPU kernel tuning and algorithm optimization via iterative runs.
- Not ideal for: General chatbots, creative writing, or pure document Q&A (Gemini/GPT-5.4 excel there).
How to Try GLM-5.1 Today
- Web chat: Use z.ai — runs GLM-5.1 by default, no API key needed.
-
API access: Create an account at bigmodel.cn, generate an API key, and use the OpenAI-compatible endpoint with
glm-5.1as model name. - Local deployment: Download weights from HuggingFace (zai-org), follow setup in official GitHub.
- API walkthrough: See the GLM-5.1 API guide for code examples and integration.
Conclusion
GLM-5.1 meaningfully extends long-horizon AI engineering: it outpaces GLM-5 on sustained, autonomous coding tasks, and leads SWE-Bench Pro and vector search optimization. While not #1 on every benchmark (Claude Opus 4.6, GPT-5.4 lead on reasoning/GPU), it’s the strongest open-weights option for extensive agentic workflows.
The open MIT License means you can run and fine-tune GLM-5.1 locally, with no usage restrictions, and deploy in your own stack.
FAQ
What does GLM stand for?
General Language Model, Zhipu AI’s architecture since 2021, using autoregressive blank infilling (not decoder-only).
Is GLM-5.1 open source?
Yes. MIT License on HuggingFace. Commercial use, fine-tuning, and redistribution are allowed.
What’s the context window?
200,000 tokens (about 150,000 words); max output 163,840 tokens.
How does GLM-5.1 compare to DeepSeek-V3.2?
GLM-5.1 leads on software engineering; DeepSeek-V3.2 is competitive on reasoning. For coding agents, GLM-5.1 wins per published data.
Can I use GLM-5.1 with Claude Code or Cursor?
Yes. Supported via Z.AI Coding Plan for Claude Code, Cline, Kilo Code, Roo Code, OpenCode. Update the model name in your assistant’s config; plans start at $10/month.
How do I access GLM-5.1 via API?
Sign up at bigmodel.cn, get an API key, and use model glm-5.1 at https://open.bigmodel.cn/api/paas/v4/chat/completions. Full walkthrough in the GLM-5.1 API guide.
Is GLM-5.1 free?
z.ai chat is free. API access is quota-based with paid plans. Off-peak usage is 1x quota until end of April 2026 (promo rate).
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