GPT-4.1 Mini vs Mistral Medium 3.1

Across our 12-test internal suite, Mistral Medium 3.1 wins 4 benchmarks outright, GPT-4.1 Mini wins none, and 8 are tied. Here's the test-by-test breakdown:

Where Mistral Medium 3.1 wins:

• Strategic analysis (5 vs 4): Mistral Medium 3.1 ties for 1st among 54 models (with 25 others); GPT-4.1 Mini ranks 27th. For tasks requiring nuanced tradeoff reasoning with real numbers — business analysis, financial modeling, risk assessment — Mistral Medium 3.1 is the demonstrably stronger choice.
• Constrained rewriting (5 vs 4): Mistral Medium 3.1 ties for 1st among 53 models (with 4 others); GPT-4.1 Mini ranks 6th. This matters for copy compression, summarization under hard character limits, and editorial tasks.
• Classification (4 vs 3): Mistral Medium 3.1 ties for 1st among 53 models (with 29 others); GPT-4.1 Mini ranks 31st. A full point gap in categorization and routing tasks — relevant to content moderation, support ticket triage, and intent classification pipelines.
• Agentic planning (5 vs 4): Mistral Medium 3.1 ties for 1st among 54 models (with 14 others); GPT-4.1 Mini ranks 16th. Better goal decomposition and failure recovery makes Mistral Medium 3.1 the stronger pick for multi-step agentic workflows.

Where the models tie (8 benchmarks):

• Long context (both 5/5): Both share the top score among 55 tested models, though GPT-4.1 Mini's 1M-token window vs Mistral Medium 3.1's 131K is a meaningful practical advantage our score doesn't fully capture.
• Tool calling (both 4/5): Both rank 18th of 54, sharing the score with 28 other models. Neither has an edge for function-calling reliability.
• Structured output (both 4/5): Tied at rank 26 of 54. JSON schema compliance is equivalent.
• Faithfulness (both 4/5): Tied at rank 34 of 55. Both stay close to source material at a similar rate.
• Multilingual (both 5/5): Both share the top score with 34 other models across 55 tested. Neither has a non-English advantage.
• Persona consistency (both 5/5): Both share 1st place with 36 other models. Character maintenance is equivalent.
• Creative problem solving (both 3/5): Both rank 30th of 54 — a relative weak spot for both models.
• Safety calibration (both 2/5): Both rank 12th of 55. Neither model distinguishes itself here; both sit below the field median.

External benchmarks (GPT-4.1 Mini only — Mistral Medium 3.1 has no external scores in this dataset): GPT-4.1 Mini scores 87.3% on MATH Level 5 (rank 9 of 14 models with this score, Epoch AI) and 44.7% on AIME 2025 (rank 18 of 23, Epoch AI). Both scores sit below the median for models we have external data on (p50: 94.15% for MATH Level 5, 83.9% for AIME 2025), suggesting GPT-4.1 Mini is a mid-tier math performer among models tested on those benchmarks. No equivalent external benchmark data is available for Mistral Medium 3.1 in this dataset.

Both models charge identical input costs at $0.40 per 1M tokens. The gap appears on output: GPT-4.1 Mini runs $1.60/1M output tokens versus Mistral Medium 3.1's $2.00/1M — a $0.40 difference per million output tokens. In practice:

• At 1M output tokens/month: you pay $1.60 (GPT-4.1 Mini) vs $2.00 (Mistral Medium 3.1) — a $0.40 monthly difference, negligible for most teams.
• At 10M output tokens/month: $16 vs $20 — a $4 gap, still minor for mid-scale applications.
• At 100M output tokens/month: $160 vs $200 — a $40 difference that starts mattering for high-throughput pipelines.

The pricing gap only becomes a meaningful factor at 100M+ output tokens per month. For most API consumers, the $0.40/1M premium for Mistral Medium 3.1 is unlikely to drive a decision. High-volume production workloads — classification pipelines, document processing, agentic systems running millions of tool calls — should factor in the 25% output cost premium and weigh it against Mistral Medium 3.1's benchmark advantages in those exact use cases.

Choose Mistral Medium 3.1 if:

• Your workload is classification-heavy (content routing, triage, intent detection) — it scores a full point higher in our testing.
• You're building agentic pipelines that require robust multi-step planning and failure recovery — it ties for 1st on agentic planning vs GPT-4.1 Mini's rank 16.
• Your outputs involve strategic analysis or constrained writing — both are outright wins for Mistral Medium 3.1.
• Output volume is under 100M tokens/month and the $0.40/1M premium is not a constraint.

Choose GPT-4.1 Mini if:

• You need a context window beyond 131K — GPT-4.1 Mini's 1M-token window is the only option here.
• You're processing very large documents, long conversation histories, or book-length inputs in a single request.
• You're running at 100M+ output tokens/month and the 25% output cost premium compounds into a real budget line.
• Your use case is math-intensive and you want external benchmark data to validate the choice — GPT-4.1 Mini has published MATH Level 5 (87.3%) and AIME 2025 (44.7%) scores (Epoch AI); Mistral Medium 3.1 does not in this dataset.
• You need to pass files (not just images) in your requests — GPT-4.1 Mini supports text+image+file input; Mistral Medium 3.1 supports text+image only.