GLM-5.1: First Open-Source Model to Top SWE-bench Pro (2026)

What Makes GLM-5.1 Different: The Long-Horizon Architecture

Z.ai describes GLM-5.1 as designed for “long-horizon agentic tasks” — work that requires maintaining context and coherence across hours of autonomous execution, not just a single code edit. The model supports sustained 8-hour autonomous execution windows, which is directly relevant for production coding agents handling multi-file refactors, end-to-end feature implementations, or complex debugging sessions without human checkpoints.

Key architectural capabilities:

• 754 billion parameters at full precision, with quantized variants for local deployment
• Long context window: supports extended token sequences for full codebase context
• Native function calling: tool use is first-class, not retrofitted
• Thinking mode: extended reasoning chains for complex multi-step problems
• Structured outputs: JSON mode and schema-constrained generation
• Context caching: significantly reduces costs on repeated similar queries

The OpenAI-compatible API surface means you can drop GLM-5.1 into any existing integration without changing your code — just swap the base URL and model name.

How to Access GLM-5.1

Option 1: Z.ai API (Fastest Setup)

The simplest path is the Z.ai managed API, which is OpenAI-compatible. You can be running it in under two minutes:

1. Create an account at z.ai and generate an API key from the dashboard
2. Point your existing OpenAI client to Z.ai’s base URL: https://api.z.ai/api/paas/v4/
3. Set the model name to glm-5.1

In Python:

from openai import OpenAI

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

response = client.chat.completions.create(
    model="glm-5.1",
    messages=[{"role": "user", "content": "Refactor this function to handle edge cases."}]
)
print(response.choices[0].message.content)

The same pattern works with any OpenAI-compatible client: the TypeScript SDK, LangChain, LlamaIndex, or a raw HTTP call. You do not need any Z.ai-specific library.

Option 2: OpenRouter

GLM-5.1 is available on OpenRouter as z-ai/glm-5.1, which gives you a unified API key across multiple models. Useful if you are already using OpenRouter for multi-model routing or do not want another vendor account to manage.

Using GLM-5.1 With Coding Agents

Z.ai has explicitly designed GLM-5.1 to work as the intelligence layer behind popular coding agents. Supported integrations include Claude Code, OpenCode, Kilo Code, Roo Code, Cline, and Droid — the primary agentic coding tools used by professional development teams in 2026.

Claude Code Integration

To route Claude Code sessions through GLM-5.1, add these environment variables to your shell profile or Claude Code configuration. Z.ai provides an Anthropic-compatible adapter endpoint that translates the Claude API format to GLM-5.1:

export ANTHROPIC_DEFAULT_SONNET_MODEL="glm-5.1"
export ANTHROPIC_DEFAULT_OPUS_MODEL="glm-5.1"

With these set, Claude Code routes its model calls through Z.ai using GLM-5.1 as the backend. This is the fastest way to benchmark GLM-5.1’s agentic coding performance on your actual codebase without changing your editor or workflow. Session length and tool-use patterns remain identical to a standard Claude Code session — only the underlying model changes.

Cline, Roo Code, and Kilo Code

For VS Code-based agents like Cline, Roo Code, and Kilo Code, configure the model provider in the extension settings:

• Set Provider to “OpenAI Compatible”
• Set Base URL to https://api.z.ai/api/paas/v4/
• Set API Key to your Z.ai key
• Set Model to glm-5.1

All three agents support this configuration path natively. GLM-5.1’s native function-calling capability means tool use — file reads, shell commands, browser calls — works reliably without special prompt engineering.

Local Deployment: vLLM and Ollama

For teams with data residency requirements or high-volume workloads where self-hosting is more economical, GLM-5.1 weights are available on Hugging Face at zai-org/GLM-5.1 under the MIT license. Two inference paths are production-ready:

vLLM (Recommended for Production)

vLLM is the standard choice for high-throughput production deployments. Serving GLM-5.1 follows the standard vLLM pattern:

vllm serve zai-org/GLM-5.1   --tensor-parallel-size 8   --max-model-len 32768

Hardware requirements at full precision are substantial: 754 billion parameters require significant VRAM spread across multiple GPUs. Z.ai provides GPTQ and AWQ quantized variants that reduce memory requirements to a range accessible on smaller multi-GPU setups. The quantized models see approximately 2-3 percentage points of SWE-bench degradation, which still places them above GPT-5.4 and Claude Opus 4.6.

Ollama (Recommended for Development)

For local development and evaluation, Ollama provides the simplest deployment path. Check the zai-org/GLM-5.1 Hugging Face repository for the latest Ollama-compatible model files. Local deployment is practical for teams doing workflow evaluation or testing privately before committing to cloud inference costs at scale.

Pricing: GLM-5.1 vs. GPT-5.4 vs. Claude Opus 4.7

Cost is where GLM-5.1 is unambiguously ahead. Z.ai prices the model at approximately $0.95 per million input tokens and $3.15 per million output tokens, with cached inputs at $0.26 per million. Context caching matters significantly for coding agents that repeatedly load large codebases into context across long sessions.

Compared to frontier proprietary models:

• GLM-5.1 (Z.ai API): ~$0.95 input / $3.15 output per million tokens
• GPT-5.4 (OpenAI): ~$5.00 input / $15.00 output per million tokens
• Claude Opus 4.7 (Anthropic): ~$7.50 input / $24.00 output per million tokens

At current pricing, GLM-5.1 is roughly 5x cheaper on input tokens and 5-8x cheaper on output tokens compared to the leading proprietary frontier models. For high-volume coding agent workloads where a single agentic session consumes millions of tokens, this cost difference is the deciding factor for many teams. An 8-hour autonomous coding session consuming 10 million output tokens costs approximately $240 at Opus 4.7 pricing and about $31 at GLM-5.1 pricing. That is not a marginal difference; it is a budget category change.

Who Should Make the Switch?

The right candidates for GLM-5.1 are clear:

• Teams running high-volume agentic coding pipelines: If your team runs Cline, Roo Code, or similar agents for multiple developers, the per-token savings accumulate into meaningful budget relief within weeks.
• Organizations with data residency requirements: Self-hosted GLM-5.1 means code never leaves your environment. The MIT license removes any legal ambiguity around deployment or fine-tuning.
• Security and vulnerability research teams: GLM-5.1’s #1 ranking on CyberGym suggests specific strength on security reasoning. Teams doing defensive security work may find it outperforms frontier models on domain-specific tasks.
• Developers evaluating open-source model quality: If you have assumed open-source models are categorically behind proprietary frontier models, GLM-5.1 is the most persuasive counterexample to date. Running it on your own codebases is now a practical exercise, not an academic one.

Who should not switch without testing: teams where mathematical reasoning is a primary workload, or where Claude Opus 4.7’s current SWE-bench lead translates to meaningfully better output on your specific tasks. Run your own evals on representative samples before making a production change. Aggregate benchmarks are the starting point for evaluation, not the ending point.

Conclusion

GLM-5.1 is a genuine milestone. A free, MIT-licensed, self-hostable model that held the top spot on the most rigorous coding benchmark for nine days — beating GPT-5.4 and Claude Opus 4.6 — represents a structural shift in what open-source AI can deliver. The cost advantage over proprietary models is not marginal; it is 5-8x. The training-on-Ascend story matters beyond geopolitics: it demonstrates that frontier AI quality is no longer exclusive to NVIDIA-based clusters.

Claude Opus 4.7 currently leads on SWE-bench Pro at 64.3%, and proprietary models retain an advantage in pure mathematical reasoning. But the gap between the best open-source and best proprietary coding models has collapsed from roughly 20 percentage points to under 6 in twelve months. At this trajectory, the question in 2027 will not be whether open-source models can compete, but which open-source model your team runs.

For developers and teams building AI-assisted engineering workflows today: GLM-5.1 is ready for production evaluation. Download the weights, call the API, or plug it into your coding agent. The model earned its place in the frontier tier — and at a fraction of the cost.