GPT-5.4 vs Mistral Small 3.2 24B
GPT-5.4 is the pick for high‑stakes, long‑context, and math-heavy workflows — it wins 9 of 12 benchmarks in our testing and posts strong external math/coding scores. Mistral Small 3.2 24B is the sensible choice when cost is the binding constraint: it ties on several measures but is far cheaper per token.
openai
GPT-5.4
Benchmark Scores
External Benchmarks
Pricing
Input
$2.50/MTok
Output
$15.00/MTok
modelpicker.net
mistral
Mistral Small 3.2 24B
Benchmark Scores
External Benchmarks
Pricing
Input
$0.075/MTok
Output
$0.200/MTok
modelpicker.net
Benchmark Analysis
Across our 12-test suite (1–5 internal scoring), GPT-5.4 wins 9 tests, Mistral Small 3.2 24B wins none, and three are ties. In our testing: safety calibration 5 vs 1 (GPT-5.4 wins; GPT-5.4 is "tied for 1st with 4 other models out of 55 tested"), faithfulness 5 vs 4 (GPT-5.4 wins; "tied for 1st with 32 other models out of 55"), long context 5 vs 4 (GPT-5.4 wins and is "tied for 1st with 36 other models" — reflects the 1M+ token context window), agentic planning 5 vs 4 (GPT-5.4 wins; "tied for 1st with 14 other models out of 54 tested"), structured output 5 vs 4 (GPT-5.4 wins; "tied for 1st with 24 other models"), strategic analysis 5 vs 2 (GPT-5.4 wins and ranks "tied for 1st with 25 other models"), creative problem solving 4 vs 2 (GPT-5.4 wins; rank 9 of 54), persona consistency 5 vs 3 (GPT-5.4 wins; "tied for 1st with 36 other models"), and multilingual 5 vs 4 (GPT-5.4 wins; "tied for 1st with 34 other models"). The three ties are constrained rewriting 4/4 (tie; rank 6 of 53 for both), tool calling 4/4 (tie; both rank 18 of 54), and classification 3/3 (tie). Practically, GPT-5.4’s advantages mean fewer hallucinations, better behavior on safety-sensitive prompts, higher fidelity to source material, stronger multi-language parity, superior performance when you must reason across very large contexts, and better results on nuanced numeric tradeoffs. Mistral matches GPT-5.4 on function selection/argument accuracy (tool calling) and constrained rewriting, so it can be a cost-effective substitute where those are the critical needs. Beyond our internal scores, GPT-5.4 scores 76.9% on SWE-bench Verified and 95.3% on AIME 2025 (both from Epoch AI), supporting its strength on coding and math tasks relative to models lacking these external scores in the payload.
Pricing Analysis
Pricing per million tokens (payload rates): GPT-5.4 charges $2.50 input / $15.00 output per M-token; Mistral Small 3.2 24B charges $0.075 input / $0.20 output per M-token. To illustrate, assuming a 50/50 split of input vs output tokens: 1M total tokens costs $8.75 on GPT-5.4 vs $0.1375 on Mistral; 10M costs $87.50 vs $1.375; 100M costs $875 vs $13.75. That aligns with the payload priceRatio of 75 — GPT-5.4 is roughly 75× more expensive per-token in typical balanced usage. Teams with heavy, high-throughput inference (logs, analytics, or high-volume chat) should care about the gap; at 100M tokens/month the delta is $861.25 — material for startups and products with tight margins. Organizations prioritizing safety, long-context, or math/analysis may accept the higher cost; cost-sensitive, high-volume deployments should prefer Mistral.
Real-World Cost Comparison
Bottom Line
Choose GPT-5.4 if you need: safety-calibrated outputs, highest faithfulness, long-context retrieval (1M+ token context), strong math/coding performance (76.9% on SWE-bench Verified, 95.3% on AIME 2025 in external tests), or advanced agentic planning. Choose Mistral Small 3.2 24B if you need: extremely low per-token cost (payload rates $0.075 input / $0.20 output per M-token) for high-throughput production, or you only require tied capabilities like tool calling and constrained rewriting without the premium for long context or top-tier safety.
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.