TokenMix Research Lab · 2026-04-22

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.

Confirmed vs Speculation: GLM-5.1's Claims
What SWE-Bench Pro Measures vs Verified
GLM-5.1 Specs vs the Frontier Field
MIT License + MoE: Why This Matters for Self-Hosters
Cost Math: GLM-5.1 vs Claude Opus 4.7 at Scale
Where GLM-5.1 Falls Short
How to Use GLM-5.1 Today
FAQ

Confirmed vs Speculation: GLM-5.1's Claims

Claim	Status	Source
GLM-5.1 released April 7, 2026	Confirmed	Z.ai official
#1 on SWE-Bench Pro	Confirmed	Scale SWE-Bench leaderboard
744B total params, 40B active (MoE)	Confirmed	Z.ai model card
MIT license	Confirmed	Hugging Face repo
Beats Claude Opus 4.6, GPT-5.4 on SWE-Bench Pro	Confirmed	Benchmark leaderboard
8-hour autonomous coding claim	Marketing, unverified by third party	Z.ai demo only
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

For most teams, the break-even against hosted API is 200-500M tokens/month on GLM-5.1. Below that, use TokenMix.ai's hosted GLM-5.1 at $0.45/ .80.

Cost Math: GLM-5.1 vs Claude Opus 4.7 at Scale

Coding workload, 80% input / 20% output, 500M input + 125M output tokens/month:

Model	Input cost	Output cost	Total/month	vs GLM-5.1
Claude Opus 4.7	$2,500	$3,125	$5,625	9.9×
GPT-5.4	,250	,875	$3,125	5.5×
Gemini 3.1 Pro	,000	,500	$2,500	4.4×
GLM-5.1 (hosted)	$225	$225	$450	—
GLM-5.1 (self-host)	~$200-800 infra	$0 marginal	$200-800	0.4-1.8×

For an agentic coding startup running 500M+ input tokens/month, switching from Claude Opus 4.7 to GLM-5.1 saves ~$5,175/month — $62,100/year.

Where GLM-5.1 Falls Short

The benchmark win doesn't mean it's best everywhere:

Reasoning (GPQA Diamond): GLM-5.1 ~90% vs Gemini 3.1 Pro 94.3%. Behind on graduate science.
Multilingual output polish: weaker than Claude Opus 4.7 for European languages
Vision/multimodal: no native vision support yet (text-only API as of April 2026)
Ecosystem: fewer SDKs, plugins, and framework integrations than OpenAI/Anthropic
Reliability: Z.ai hosted API has had two 2+ hour outages in April 2026 (Apr 10, Apr 18)

How to Use GLM-5.1 Today

Three paths:

Option A — Hosted via TokenMix.ai gateway:

from openai import OpenAI
client = OpenAI(
    base_url="https://api.tokenmix.ai/v1",
    api_key="your_tokenmix_key"
)
response = client.chat.completions.create(
    model="z-ai/glm-5.1",
    messages=[{"role": "user", "content": "Refactor this code..."}]
)

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.

Sources

By TokenMix Research Lab · Updated 2026-04-22