GPT-5 Mini vs Mistral Small 4
GPT-5 Mini is the better pick for high-accuracy, long-context and safety-sensitive tasks — it wins 6 of 12 benchmarks in our suite. Mistral Small 4 is the cheaper alternative and beats GPT-5 Mini on tool calling; choose it when tool orchestration and lower inference cost matter.
openai
GPT-5 Mini
Benchmark Scores
External Benchmarks
Pricing
Input
$0.250/MTok
Output
$2.00/MTok
modelpicker.net
mistral
Mistral Small 4
Benchmark Scores
External Benchmarks
Pricing
Input
$0.150/MTok
Output
$0.600/MTok
modelpicker.net
Benchmark Analysis
Across our 12-test suite GPT-5 Mini wins 6 tasks, Mistral Small 4 wins 1, and 5 are ties. Detailed walk-through (score out of 5 unless noted):
- Tool calling: Mistral Small 4 wins (4 vs GPT-5 Mini 3). Ranking: Mistral rank 18 of 54 vs GPT-5 Mini rank 47 of 54 — choose Mistral for reliable function selection and argument sequencing.
- Structured output: tie (both 5). Both models tie for 1st (tied with 24 others) on JSON/schema compliance. Expect production-grade format adherence from either model.
- Constrained rewriting: GPT-5 Mini wins (4 vs 3). GPT-5 Mini ranks 6 of 53 vs Mistral rank 31 — better when compressing text into tight character limits.
- Safety calibration: GPT-5 Mini wins (3 vs 2). GPT-5 Mini rank 10 of 55 vs Mistral rank 12 — safer refusals and permissions in our tests.
- Strategic analysis: GPT-5 Mini wins (5 vs 4). GPT-5 Mini is tied for 1st with many models, showing stronger nuanced tradeoff reasoning for numeric/strategic tasks.
- Faithfulness: GPT-5 Mini wins (5 vs 4). GPT-5 Mini tied for 1st (rank 1 of 55) while Mistral sits at rank 34 — GPT-5 Mini sticks to source material more reliably in our evaluation.
- Classification: GPT-5 Mini wins (4 vs 2). GPT-5 Mini tied for 1st (rank 1 of 53) while Mistral ranks 51 of 53 — use GPT-5 Mini for routing/categorization tasks.
- Long context: GPT-5 Mini wins (5 vs 4). GPT-5 Mini tied for 1st (rank 1 of 55) vs Mistral rank 38 — superior retrieval and coherence over 30K+ tokens.
- Persona consistency, creative problem solving, agentic planning, multilingual: ties (both perform equally by our scores). Both models maintain character, generate feasible creative ideas, decompose goals and work across languages at parity in our tests. Supplementary external benchmarks (Epoch AI): GPT-5 Mini scores 64.7% on SWE-bench Verified, 97.8% on MATH Level 5, and 86.7% on AIME 2025 — cite Epoch AI. These reinforce GPT-5 Mini's math and coding/problem-solving strengths (e.g., MATH Level 5 = 97.8%). Overall, GPT-5 Mini is stronger on safety, faithfulness, long-context, classification and strategic reasoning; Mistral Small 4 is the clear winner for tool calling and cost-efficiency.
Pricing Analysis
Per-million-token costs (input + output): GPT-5 Mini = $0.25 + $2.00 = $2.25 per M tokens; Mistral Small 4 = $0.15 + $0.60 = $0.75 per M tokens. At scale that means: 1M tokens/month → $2.25 vs $0.75; 10M → $22.50 vs $7.50; 100M → $225 vs $75. GPT-5 Mini is ~3.33× more expensive (priceRatio 3.3333). High-volume apps (10M–100M+ tokens/month), cost-sensitive products and startups should favor Mistral Small 4 to reduce infrastructure spend; teams that need top faithfulness, long-context handling and stronger strategic reasoning may justify GPT-5 Mini despite the higher bill.
Real-World Cost Comparison
Bottom Line
Choose GPT-5 Mini if you need: high faithfulness and safety, robust long-context retrieval (30K+ tokens), strong classification and numeric/strategic reasoning, or top math performance (MATH Level 5 97.8% in Epoch AI data). Choose Mistral Small 4 if you need: the lowest inference cost (≈$0.75 per M tokens vs $2.25 for GPT-5 Mini), better tool-calling behavior, or are building high-volume, cost-sensitive pipelines where every dollar of inference matters.
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.