GPT-5.2 vs Mistral Small 4
Side-by-side benchmark comparison across coding, math, reasoning, speed, and pricing.
GPT-5.2 by OpenAI and Mistral Small 4 by Mistral are evenly matched across 6 benchmarks. This head-to-head comparison covers coding, math, reasoning, speed, and pricing metrics from our benchmark data.
Category-by-Category Breakdown
Reasoning: In reasoning, GPT-5.2 scores 87.9% on GPQA Diamond compared to Mistral Small 4's 75.8%, while GPT-5.2 scores 73.3% on τ²-Bench Airline compared to Mistral Small 4's 43.7%.
Context: In context, GPT-5.2 scores 400K on Context Length compared to Mistral Small 4's 262K.
Pricing Comparison
GPT-5.2 costs $1.8/1M input tokens and $14.0/1M output tokens, while Mistral Small 4 costs $0.15/1M input and $0.60/1M output. Mistral Small 4 is the more affordable option for API usage.
Verdict
Mistral Small 4 leads across the board in affordability, making it the stronger overall choice in this comparison.
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GPT-5.2 vs Mistral Small 4 — FAQ
Which is better, GPT-5.2 or Mistral Small 4?
GPT-5.2 and Mistral Small 4 are evenly matched across benchmarks. The best choice depends on your specific priorities such as coding ability, speed, or pricing.
How does GPT-5.2 compare to Mistral Small 4 for coding?
SWE-bench Verified data is not available for both models. Check the detailed comparison charts above for other coding-related metrics.
Is GPT-5.2 cheaper than Mistral Small 4?
Yes, Mistral Small 4 is cheaper. GPT-5.2 costs $1.8/1M input and $14.0/1M output tokens. Mistral Small 4 costs $0.15/1M input and $0.60/1M output tokens.
Which is faster, GPT-5.2 or Mistral Small 4?
Output speed data is not available for both models. Check the speed section of the comparison above for available performance data.
What benchmarks does the GPT-5.2 vs Mistral Small 4 comparison cover?
This comparison covers 6 benchmarks including GPQA Diamond, Input Cost, Output Cost, Context Length, Cached Input Cost, τ²-Bench Airline. Metrics span general intelligence, coding, math, reasoning, speed, and cost categories.