GPT-4.1 Mini vs Mistral Small 3.1 24B

We compare the two models across our 12-test suite (scores are from our testing unless noted). Wins, ties and ranks come from the payload.

• Tool calling: GPT-4.1 Mini scores 4 vs Mistral 1 in our tests. GPT-4.1 Mini ranks 18 of 54; Mistral ranks 53 of 54 and has a quirk: no_tool calling = true. Practical impact: GPT-4.1 Mini can select and sequence functions reliably; Mistral cannot perform tool-calling workflows.
• Multilingual: GPT-4.1 Mini scores 5 vs Mistral 4. GPT-4.1 Mini is tied for 1st among 55 models; Mistral ranks 36 of 55. For non-English production outputs, GPT-4.1 Mini gives higher parity.
• Persona consistency: GPT-4.1 Mini 5 vs Mistral 2 — GPT-4.1 Mini tied for 1st of 53 models, Mistral ranks 51 of 53. GPT-4.1 Mini resists instruction injection and keeps character more reliably.
• Safety calibration: GPT-4.1 Mini 2 vs Mistral 1 (GPT-4.1 Mini rank 12 of 55, Mistral rank 32 of 55). GPT-4.1 Mini refuses harmful prompts more often in our tests.
• Strategic analysis: GPT-4.1 Mini 4 vs Mistral 3 (GPT-4.1 Mini rank 27/54; Mistral 36/54). GPT-4.1 Mini provides better nuanced tradeoff reasoning with numbers.
• Constrained rewriting: GPT-4.1 Mini 4 vs Mistral 3 (GPT-4.1 Mini rank 6/53; Mistral 31/53). GPT-4.1 Mini compresses to hard limits more reliably.
• Creative problem solving: GPT-4.1 Mini 3 vs Mistral 2 (GPT-4.1 Mini rank 30/54; Mistral 47/54). GPT-4.1 Mini generates more feasible, non-obvious ideas in our tests.
• Agentic planning: GPT-4.1 Mini 4 vs Mistral 3 (GPT-4.1 Mini rank 16/54; Mistral 42/54). GPT-4.1 Mini better decomposes goals and handles failure recovery.
• Classification: both score 3 (tie). Both are rank 31 of 53 in our tests, so neither has a clear edge for basic routing/categorization.
• Structured output: both score 4 (tie). Both rank 26 of 54, showing similar JSON/schema reliability.
• Faithfulness: both score 4 (tie). Both rank 34 of 55, meaning similar adherence to source material in our tests.
• Long-context: both score 5 (tie). Both tied for 1st with 36 other models out of 55 — both are top choices for 30K+ token retrieval tasks. Additional external data: Beyond our internal tests, GPT-4.1 Mini scores 87.3% on MATH Level 5 and 44.7% on AIME 2025 (Epoch AI), which supports its relative math competence on those external benchmarks. Overall: GPT-4.1 Mini wins 8 of 12 internal benchmarks; Mistral wins none and ties 4 categories — its main technical advantage is much lower price and parity on long-context, structured output and faithfulness.

Costs in the payload are per mTok (per 1,000 tokens). Output-only cost per 1M tokens: GPT-4.1 Mini = $1.60 × 1000 = $1,600; Mistral = $0.56 × 1000 = $560. Input-only per 1M: GPT-4.1 Mini = $0.40 × 1000 = $400; Mistral = $0.35 × 1000 = $350. If you assume 1:1 input:output tokens, combined monthly costs are: for 1M tokens — GPT-4.1 Mini ≈ $2,000 vs Mistral ≈ $910; for 10M — GPT-4.1 Mini ≈ $20,000 vs Mistral ≈ $9,100; for 100M — GPT-4.1 Mini ≈ $200,000 vs Mistral ≈ $91,000. At these volumes the ~2.86× price ratio (priceRatio = 2.857) matters: teams with heavy token throughput (10M+ tokens/month) should prioritize Mistral to cut costs, while teams that need the extra capabilities (tool calling, stronger safety/persona behavior, multilingual) may justify GPT-4.1 Mini’s premium.

Choose GPT-4.1 Mini if you need: tool calling or agentic workflows, strong multilingual quality, tight persona consistency, better safety calibration, or stronger strategic and creative reasoning — and you can accept higher token costs (output $1.60/mTok). Choose Mistral Small 3.1 24B if you need: the lowest per-token cost (output $0.56/mTok), top-tier long-context handling, reliable structured output or faithfulness at scale, and you do not require tool calling or strong persona/safety behavior. Example picks: pick GPT-4.1 Mini for production chat agents integrating external APIs; pick Mistral for high-volume retrieval, summarization, or batch transformation workloads where cost is the primary constraint.