Mistral AIMistral Small 4VSZ.ai (Zhipu AI)GLM-4.6
Our Take
We recommend Mistral Small 4 for practical local execution on standard developer hardware, or GLM-4.6 if you need peak reasoning and have the VRAM (or a cloud API) to host it. While GLM-4.6 offers frontier capability, its massive parameter size makes it extremely difficult to host locally. Choose Mistral Small 4 for standard local setups, or GLM-4.6 for peak reasoning.
▶WHY?
Benchmark Calculations & Evidence:
Model Size: GLM-4.6 is a massive 357B parameter model requiring heavy GPU infrastructure, while Mistral Small 4 is a 119B parameter model.
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Benchmarks & Scores
Coding (live-code-bench)
63.6%scripting single-file apps or clearly defined functions
Reasoning (gpqa-diamond)
71.2%graduate-level science QA
Cost & Context
Cost (per 1M tokens)3.8x cheaper
$0.26Input: $0.15 | Output: $0.60Context WindowLarger
262.14k tokensBenchmarks & Scores
Coding (swe-bench-verified)
68%multi-file code and clearly defined tasks
Reasoning (gpqa-diamond)Winner (+11.7%)
82.9%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
$1.00Input: $0.60 | Output: $2.20Context Window
202.75k tokensFrequently Asked Questions about Mistral Small 4 vs GLM-4.6
Mistral Small 4 is cheaper than GLM-4.6. Mistral Small 4 has a blended cost of $0.26/1M tokens, which is about 3.8x cheaper than GLM-4.6 at $1.00/1M tokens.
For coding tasks, Mistral Small 4 scores 63.6% on live-code-bench (scripting single-file apps or clearly defined functions), while GLM-4.6 scores 68% on swe-bench-verified (multi-file code and clearly defined tasks).
Related Matchups
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