GPT-4.1 Mini vs Mistral Small 3.2 24B

Head-to-head across our 12-test suite (scores shown are from our testing):

• Wins for GPT-4.1 Mini (A):
  • long context: A=5 vs B=4 — A is tied for 1st (tied with 36 others of 55), so expect top-tier retrieval and reasoning across 30K+ tokens in real tasks; B ranks 38/55.
  • multilingual: A=5 vs B=4 — A tied for 1st (34 others), meaning stronger non-English parity in generation.
  • persona consistency: A=5 vs B=3 — A tied for 1st, so better at maintaining character and resisting injection.
  • creative problem solving: A=3 vs B=2 — measurable edge for non-obvious, feasible idea generation (A ranks 30/54).
  • strategic analysis: A=4 vs B=2 — A ranks 27/54, indicating noticeably better nuanced trade-off reasoning.
  • safety calibration: A=2 vs B=1 — A ranks 12/55 vs B 32/55, so A more reliably refuses harmful prompts while permitting legit ones.
• Ties (effectively equal in our tests): structured output (4/4), constrained rewriting (4/4), tool calling (4/4), faithfulness (4/4), classification (3/3), agentic planning (4/4). For these tasks you can expect similar behavior from both models in our suite (e.g., JSON schema adherence, function selection, and sticking to sources).
• Notable external benchmark (Epoch AI): GPT-4.1 Mini scores 87.3% on MATH Level 5 and 44.7% on AIME 2025 (our testing); MATH Level 5 places GPT-4.1 Mini 9th of 14 tested in that set. Mistral Small 3.2 24B has no MATH Level 5/AIME results in the payload. Practical interpretation: GPT-4.1 Mini is the stronger, more consistent choice when you need long-context coherence, multilingual parity, persona stability, and better math/strategic reasoning. Mistral Small 3.2 24B matches or ties GPT-4.1 Mini on structured outputs, tool calling, faithfulness, constrained rewriting and classification — making it a cheaper alternative for those workloads where absolute top-tier reasoning or long context is not required.

Pricing in the payload is per mtok (assume mtok = 1,000 tokens). GPT-4.1 Mini: input $0.40/mtok, output $1.60/mtok (combined $2.00/mtok). Mistral Small 3.2 24B: input $0.075/mtok, output $0.20/mtok (combined $0.275/mtok). That matches a priceRatio ≈ 8 in the payload. Example costs (1 mtok = 1,000 tokens):

• If all tokens are outputs: 1M tokens = 1,000 mtoks → GPT-4.1 Mini = $1,600; Mistral = $200. 10M tokens → GPT = $16,000; Mistral = $2,000. 100M → GPT = $160,000; Mistral = $20,000.
• If you assume a 50/50 input/output split: 1M tokens → GPT = $1,000 (500 mtok input=$200 + 500 mtok output=$800); Mistral = $137.50 (input $37.50 + output $100). 10M → GPT = $10,000; Mistral = $1,375. 100M → GPT = $100,000; Mistral = $13,750. Who should care: teams with heavy throughput (millions of tokens/month), embedded assistants, or multi-tenant APIs will feel the cost gap immediately and should strongly consider Mistral for baseline serving. Product teams prioritizing best-in-class long-context, multilingual quality, or higher math/analysis accuracy should budget for GPT-4.1 Mini despite the higher cost.

Choose GPT-4.1 Mini if you need: long-context retrieval and reasoning (tied for 1st on long context), strong multilingual quality (5/5, tied for 1st), better persona consistency (5/5), higher math accuracy (MATH Level 5 = 87.3%), or safer refusal behavior (safety calibration 2 vs 1). Choose Mistral Small 3.2 24B if you need: the lowest serving cost (input $0.075/mtok, output $0.20/mtok) while keeping parity on structured outputs, tool calling, faithfulness, constrained rewriting, classification, and agentic planning — ideal for large-volume, cost-sensitive production workloads that do not require GPT-4.1 Mini’s long-context or math edges.