GPT-4.1 Nano vs Mistral Large 3 2512

We tested 12 tasks. Summary from our data: 3 wins for GPT-4.1 Nano (constrained rewriting 4 vs 3; safety calibration 2 vs 1; persona consistency 4 vs 3), 3 wins for Mistral Large 3 2512 (strategic analysis 4 vs 2; creative problem solving 3 vs 2; multilingual 5 vs 4), and 6 ties (structured output 5/5; tool calling 4/4; faithfulness 5/5; classification 3/3; long context 4/4; agentic planning 4/4). Detailed walk-through: structured output — tie at 5/5, both tied for 1st (tied with 24 others), meaning both are reliable for JSON/schema outputs. tool calling — 4/4 tie (rank 18/54), so both select and sequence functions competently but are not in the absolute top tier. faithfulness — 5/5 tie (tied for 1st with 32 others), so both stick to source material in our tests. classification and long context are ties (3/3 and 4/4 respectively), indicating comparable routing accuracy and 30K+ retrieval. agentic planning is a tie at 4, so both decompose goals similarly. GPT-4.1 Nano outscored Mistral on constrained rewriting (4 vs 3; rank 6/53 for Nano), which matters for precise compressions and strict character limits, and on safety calibration (2 vs 1), meaning in our testing Nano refused or allowed requests more appropriately per the safety benchmark. Persona_consistency (4 vs 3) favors Nano for maintaining a character and resisting injection. Mistral wins strategic analysis (4 vs 2; rank 27 vs Nano rank 44), so it delivers better nuanced tradeoff reasoning and numerical analysis in our tests. Mistral also wins creative problem solving (3 vs 2) and multilingual (5 vs 4; tied for 1st), so it produces more non-obvious feasible ideas and stronger non-English parity. Supplementary external math measures are present only for GPT-4.1 Nano: MATH Level 5 = 70 and AIME_2025 = 28.9 (GPT-4.1 Nano ranks 11/14 and 20/23 on those respective tests in our dataset), which indicates modest capability on advanced competition math in our testing; Mistral has no external math scores in the payload. In short: both models tie on many engineering-critical tasks (structured output, faithfulness, tool calling), GPT-4.1 Nano is cheaper and stronger on safety and constrained rewriting, and Mistral is stronger on multilingual, strategy, and creative ideation in our benchmarks.

Per the payload, GPT-4.1 Nano charges $0.10/mtok input and $0.40/mtok output; Mistral Large 3 2512 charges $0.50/mtok input and $1.50/mtok output. Assuming 1M output tokens (1,000 mTok): output-only cost is $400/month for GPT-4.1 Nano vs $1,500/month for Mistral. At 10M output tokens: $4,000 vs $15,000. At 100M output tokens: $40,000 vs $150,000. If you pay for equal input and output volume, GPT-4.1 Nano total = ($0.10+$0.40)= $0.50/mtok → $500/month for 1M tokens; Mistral total = ($0.50+$1.50)= $2.00/mtok → $2,000/month for 1M tokens (scale linearly). The gap matters most for high-volume services, startups on tight budgets, and latency-sensitive applications where cost-per-token compounds; teams prioritizing multilingual or higher creative/strategic quality should weigh the higher spend for Mistral.

Choose GPT-4.1 Nano if you need the best price-to-performance for production chat, schema compliance, faithful output, safety calibration, persona consistency, or strict constrained rewriting — or if you process millions of tokens monthly and want to cut costs (Nano output $0.40/mtok). Choose Mistral Large 3 2512 if your priority is multilingual parity, nuanced strategic analysis, or higher creative problem-solving and you can accept its higher cost ($1.50/mtok output). If you need both sets of strengths, test both on your real prompts; they tie on structured output, tool calling, faithfulness, classification, long-context, and agentic planning in our suite.