GPT-5.4 Nano vs Mistral Small 3.2 24B

Across our 12-test suite GPT-5.4 Nano wins the majority: structured output 5 vs 4 (A tied for 1st of 54, B rank 26/54), strategic analysis 5 vs 2 (A tied for 1st of 54, B rank 44/54), creative problem solving 4 vs 2 (A rank 9/54, B rank 47/54), long context 5 vs 4 (A tied for 1st of 55, B rank 38/55), safety calibration 3 vs 1 (A rank 10/55, B rank 32/55), persona consistency 5 vs 3 (A tied for 1st of 53, B rank 45/53), and multilingual 5 vs 4 (A tied for 1st of 55, B rank 36/55). Five tests tie: constrained rewriting (4/4, both rank 6/53), tool calling (4/4, both rank 18/54), faithfulness (4/4, both rank 34/55), classification (3/3, both rank 31/53), and agentic planning (4/4, both rank 16/54). No test in our suite shows Mistral outright beating GPT-5.4 Nano. Practical implications: GPT-5.4 Nano’s 5/5 long context and structured output scores mean it’s more reliable for tasks requiring schema-compliant outputs and retrieval over 30K+ tokens; its strategic analysis and creative problem solving advantages matter for nuanced tradeoff reasoning and generating non-obvious, feasible ideas. Mistral matches GPT-5.4 Nano on tool calling, constrained rewriting and faithfulness, so lower-cost production systems that rely on accurate function selection or tight rewrites without heavy long-context needs will see similar behavior. Additionally, GPT-5.4 Nano scores 87.8% on AIME 2025 (Epoch AI) in the payload, ranking 8th of 23 on that external math benchmark — useful evidence for math/algorithmic performance where present.

Price data from the payload: GPT-5.4 Nano charges $0.20 per input mTok and $1.25 per output mTok; Mistral Small 3.2 24B charges $0.075 per input mTok and $0.20 per output mTok (payload priceRatio = 6.25). Using a common 50/50 input-output token split (1M total tokens = 500k input + 500k output): GPT-5.4 Nano ≈ $725 per 1M tokens, Mistral ≈ $137.50 per 1M tokens. Scale those: 10M tokens → GPT-5.4 Nano ≈ $7,250 vs Mistral ≈ $1,375; 100M tokens → GPT-5.4 Nano ≈ $72,500 vs Mistral ≈ $13,750. The output-rate gap (1.25 vs 0.20) drives most of the difference; at high volumes (10M–100M tokens/month) teams that care about unit economics should prefer Mistral unless GPT-5.4 Nano’s superior benchmark performance justifies the extra $ per token for specific tasks.

Choose GPT-5.4 Nano if you need: long-context retrieval (tied for 1st), strict structured outputs (tied for 1st), higher strategic-analysis and creative-problem-solving (scores 5 and 4 vs 2 and 2), or the stronger persona consistency and multilingual performance shown in our tests — and you can absorb higher per-token costs. Choose Mistral Small 3.2 24B if you need a budget option that still ties on tool calling, constrained rewriting, faithfulness, classification, and agentic planning, and where reducing monthly token spend (see pricing analysis) is the priority for production-scale deployments.