GLM-5.1 Beats Claude on SWE-Bench Pro: Open Source AI Coup 2026
GLM-5.1 from Z.ai took the #1 spot on SWE-Bench Pro on April 7, 2026, surpassing Claude Opus 4.6 and GPT-5.4. It is open source under MIT license, free to self-host, and ships with 744B total parameters (40B active) in a Mixture-of-Experts architecture. Key specs: 8-hour autonomous coding runs, native tool-use, 128K context. For the first time a fully open-weight model holds an agentic coding SOTA — and Anthropic's Opus 4.7 response (published April 16 at 87.6% SWE-bench Verified) targets a different benchmark, leaving SWE-Bench Pro wide open. TokenMix.ai hosts GLM-5.1 at $0.45/
.80 per million tokens, roughly 90% cheaper than Claude Opus 4.7 for coding-equivalent workloads.
Matches Claude Opus 4.7 (87.6% SWE-bench Verified)
No, different benchmark
Apples-to-oranges
Faster than Opus 4.7
Generally yes for equal hardware
Community benchmarks
Bottom line: The SWE-Bench Pro win is real. The framing "beats Claude" is partially true — it beats 4.6 but competes on a different benchmark than 4.7.
What SWE-Bench Pro Measures vs Verified
Two benchmarks, two different things:
Benchmark
What it tests
Current SOTA
Winner
SWE-Bench Verified
Manually-vetted GitHub issue fixes across popular Python repos
87.6%
Claude Opus 4.7
SWE-Bench Pro
Enterprise-grade coding including long repos, multi-file fixes, complex dependency chains
~67-72% (est)
GLM-5.1
HumanEval
Single-function Python generation
95%+
Saturated
SWE-Bench Pro is harder than Verified — it includes larger codebases, multi-file refactors, and tasks Claude/GPT historically struggled with. GLM-5.1 winning there means it handles big-repo engineering, which is exactly what enterprise buyers care about.
GLM-5.1 Specs vs the Frontier Field
Model
Params
Context
Open
License
API $/M in
API $/M out
GLM-5.1
744B MoE (40B active)
128K
Yes
MIT
$0.45
.80
Claude Opus 4.7
Undisclosed
200K
No
Commercial
$5.00
$25.00
GPT-5.4
Undisclosed
272K
No
Commercial
$2.50
5.00
Gemini 3.1 Pro
Undisclosed
1M
No
Commercial
$2.00
2.00
DeepSeek V3.2
671B MoE (37B active)
128K
Yes
DeepSeek License
$0.14
$0.28
Llama 4 Maverick
400B MoE (17B active)
10M
Yes
Llama Community
Self-host only
Self-host only
Gemma 4 31B
31B Dense
128K
Yes
Apache 2.0
Self-host only
Self-host only
Cheapest hosted frontier coding model is DeepSeek V3.2, but it trails GLM-5.1 by ~7-10 points on SWE-Bench Pro. Cheapest SOTA is GLM-5.1.
MIT License + MoE: Why This Matters for Self-Hosters
Most open models ship under restrictive licenses:
Llama 4 uses Llama Community License with anti-competitive restrictions (can't use output to train competing models, 700M+ user trigger)
Qwen uses Qwen License with some commercial restrictions
DeepSeek uses DeepSeek License (restrictions on specific use cases)
GLM-5.1 is MIT. Use it however you want, including training derivative commercial models, redistributing, or building a competing API service. This is strategically significant because:
Startups can fine-tune on proprietary data without lawyer review of license terms
Fork-and-train ecosystem will emerge within 60 days
Frontier labs lose leverage when free-to-modify weights match their benchmarks
MoE hardware reality
744B total, 40B active per forward pass. Minimum viable self-host:
8× H100 80GB (640GB total VRAM) for fp8
4× H200 or 4× B200 for fp8 (recommended)
Cost: $8K-25K/month on rented GPU infrastructure, or
80K-500K one-time for owned hardware
Option B — Z.ai direct:platform.z.ai, OpenAI-compatible endpoint, first-party pricing but fewer redundancy options.
Option C — Self-host from Hugging Face: 8× H100 minimum, vLLM or SGLang inference server. Recommended only at >500M tokens/month.
For routing strategy combining GLM-5.1 (coding), Gemini 3.1 Pro (reasoning), and GPT-5.4 (chat), see our GPT-5.5 migration checklist — Step 5 covers multi-model routing that works identically for open+closed model mixes.
FAQ
Is GLM-5.1 really better than Claude Opus 4.7 for coding?
On SWE-Bench Pro, yes. On SWE-Bench Verified, Opus 4.7 at 87.6% likely still leads. For enterprise multi-file refactors and long-repo work, GLM-5.1 wins. For single-file code generation, Opus 4.7 remains top.
What does "MIT license" mean for my startup?
You can use GLM-5.1's weights, outputs, and modifications in any commercial product without restriction — including training derivative models, redistributing weights, or building a paid API service on top. Compared to Llama Community License or Qwen License, MIT is the most permissive option available for a frontier model.
How much does it cost to self-host GLM-5.1?
Minimum viable is 8× H100 80GB (about
5-25/hour rented on Lambda/Vast, or $200K capex). For small teams, hosted API via TokenMix.ai at $0.45/
.80 per MTok is cheaper until you exceed ~500M input tokens/month.
Is GLM-5.1 trained on distilled output from Claude or GPT?
Anthropic's February 2026 distillation allegations named DeepSeek, Moonshot, and MiniMax — not Z.ai. GLM-5.1 has not been accused of adversarial distillation. Z.ai publishes its training data mix in the technical report.
What's the rate limit on GLM-5.1 via Z.ai direct?
Free tier: 10 req/min. Pro tier ($20/month): 120 req/min. Enterprise: custom. Via TokenMix.ai, rate limits pool across providers for higher effective throughput.
Should I wait for GLM-6 or use GLM-5.1 now?
Use GLM-5.1 now. Z.ai has not announced GLM-6. Release cadence of open-source Chinese labs is 2-4 months between major versions — GLM-5.2 is more likely than GLM-6 within the next quarter.