GLM-5.1: The Open-Weight Model That Tops SWE-bench Pro

TL;DR — GLM-5.1 is Zhipu AI’s open-weight model that grabbed the top SWE-bench Pro score among open models in 2026. SWE-bench Pro is the harder, contamination-resistant cousin of SWE-bench Verified, so the result means more than a vanilla leaderboard win. If your agent’s job is pure coding and you want open weights, GLM-5.1 belongs on your shortlist next to Kimi K2.6.

What SWE-bench Pro Actually Measures

A leaderboard win only matters if you know what the leaderboard tests. SWE-bench Pro is the stricter variant: harder real-world GitHub issues, with safeguards against the training-data contamination that inflates scores on the original SWE-bench. A model can’t memorize its way to a good Pro score the way it sometimes can on easier evals.

So when Zhipu says GLM-5.1 tops SWE-bench Pro among open-weight models, the honest reading is: on hard, unseen coding tasks, it resolves more issues end-to-end than any other model you can download and run yourself. That’s a narrower and more useful claim than “best open model,” and it’s the one worth caring about if you’re building a coding agent.

Where GLM-5.1 Fits

I think about the open-weight coding models as a small set of specialists, not a ranking:

Model	Strongest at	Reach for it when
GLM-5.1	Hard coding tasks (SWE-bench Pro)	The agent’s core job is resolving code issues
Kimi K2.6	Agentic tool-use across long horizons	The loop is tool-heavy and multi-step
DeepSeek V4	Huge context, lowest cost	Context-heavy or high-volume work

GLM-5.1’s sweet spot is the agent whose loop is dominated by “read this code, figure out the bug, write the fix, verify.” If that’s your workload and you need open weights, it’s a strong default. If your loop is more about orchestrating many tools or chewing through giant context, the other two may serve you better.

Running It as a Coding Agent

GLM-5.1 is available through SandBase in the OpenAI Chat Completions format. Here’s a minimal fix-the-bug loop:

from openai import OpenAI

client = OpenAI(base_url="https://api.sandbase.ai/v1", api_key="sk-er-...")

TOOLS = [
    {"type": "function", "function": {
        "name": "run_tests",
        "description": "Run the repo test suite and return output",
        "parameters": {"type": "object", "properties": {}, "required": []},
    }},
    # ... read_file, write_file, etc.
]

messages = [
    {"role": "system", "content": "You are a coding agent. Fix the failing test. Edit minimally, then run tests."},
    {"role": "user", "content": "test_auth.py::test_expired_token is failing. Fix it."},
]

resp = client.chat.completions.create(
    model="zhipu/glm-5.1",
    messages=messages,
    tools=TOOLS,
    tool_choice="auto",
)
# Execute returned tool_calls, append results, loop until tests pass.

Because GLM-5.1 is genuinely strong at the resolve-the-issue task, the loop tends to converge in fewer iterations on coding problems — which, since each iteration re-sends growing context, is also a quiet cost win. Run the code it produces in an isolated sandbox, not on your dev machine.

Open Weights, Same Story

The case for GLM-5.1 being open-weight is the same one that applies to the whole open-source model class: your code doesn’t leave your network if you self-host, you control the cost curve, and the model can’t be deprecated out from under your agent. For a coding agent that touches proprietary code, those are the deciding factors more often than a couple of benchmark points.

The realistic path: prototype against the SandBase API, confirm GLM-5.1 actually wins on your repos (benchmarks aren’t your codebase), then decide if self-hosting the weights is worth the GPU ops.

FAQ

Q: Is SWE-bench Pro harder than SWE-bench Verified?

Yes. Pro uses harder issues and contamination safeguards, so scores are lower across the board and harder to game. A top Pro result is more meaningful than a top Verified result.

Q: Is GLM-5.1 better than Kimi K2.6?

For pure hard-coding tasks, GLM-5.1’s SWE-bench Pro lead says yes. For tool-heavy, long-horizon agent loops, K2.6’s tool-use consistency may matter more. Different specialties — match to your loop.

Q: Can I self-host GLM-5.1?

Yes, it’s open-weight. Many teams prototype on the SandBase API first and self-host once they’ve confirmed it wins on their actual code.

Q: How does it compare to closed models like Claude Opus 4.7?

Opus 4.7 still leads the overall coding frontier, but it’s API-only. GLM-5.1 is the open-weight option that gets closest on hard coding tasks.

Q: Does it work with the OpenAI SDK?

Yes. Through SandBase it’s Chat Completions — same SDK, base_url=https://api.sandbase.ai/v1, model zhipu/glm-5.1.

See Zhipu AI for official model details, and the SWE-bench leaderboard for benchmark context.