GPT-4.1 Nano vs Mistral Medium 3.1

Summary from our 12-test suite: Mistral Medium 3.1 wins 8 tests, GPT-4.1 Nano wins 2, and 2 tie (tool calling and safety calibration). Detailed walk-through:

• Multilingual: Mistral 5 vs GPT-4.1 Nano 4. Mistral is tied for 1st ("tied for 1st with 34 other models out of 55 tested"). For non-English production apps this translates to more reliable parity across languages.

• Agentic planning: Mistral 5 vs GPT-4.1 Nano 4. Mistral is tied for 1st ("tied for 1st with 14 other models out of 54"). That means better goal decomposition and failure recovery in our testing.

• Long_context: Mistral 5 vs GPT-4.1 Nano 4. Mistral ranks tied for 1st ("tied for 1st with 36 other models out of 55"); in practice this is higher retrieval accuracy at 30K+ tokens per our benchmark description. Note: GPT-4.1 Nano reports a much larger context_window in the payload (1,047,576 tokens) but scored 4 on our long context benchmark and ranks 38 of 55, indicating context_window size alone didn't yield top rank on the test.

• Strategic_analysis: Mistral 5 vs GPT-4.1 Nano 2. Mistral is tied for 1st ("tied for 1st with 25 other models out of 54"), showing clearer tradeoff reasoning with numbers in our scenarios.

• Constrained_rewriting: Mistral 5 vs GPT-4.1 Nano 4. Mistral tied for 1st ("tied for 1st with 4 other models out of 53"), better at hard character-limit compression tasks.

• Creative_problem_solving: Mistral 3 vs GPT-4.1 Nano 2. Mistral wins this one (score 3 vs 2), ranking 30 of 54 vs GPT's 47 of 54; expect more specific feasible ideas from Mistral in our tests.

• Classification: Mistral 4 vs GPT-4.1 Nano 3. Mistral ties for 1st ("tied for 1st with 29 other models out of 53"), so routing and categorization were more accurate in our runs.

• Persona_consistency: Mistral 5 vs GPT-4.1 Nano 4. Mistral tied for 1st ("tied for 1st with 36 other models out of 53"), better at maintaining character and resisting injection.

• Structured_output: GPT-4.1 Nano 5 vs Mistral 4. GPT-4.1 Nano is tied for 1st ("tied for 1st with 24 other models out of 54"), showing superior JSON/schema compliance in our tests — important where strict format adherence is required.

• Faithfulness: GPT-4.1 Nano 5 vs Mistral 4. GPT-4.1 Nano ties for 1st ("tied for 1st with 32 other models out of 55"), meaning it better sticks to source material in our evaluation.

• Tool_calling: tie at 4 for both; both rank 18 of 54 ("rank 18 of 54 (29 models share this score)"). Our tool calling test judged function selection and sequencing — neither had a clear edge.

• Safety_calibration: tie at 2 for both; both rank 12 of 55 ("rank 12 of 55 (20 models share this score)").

Additional math notes: GPT-4.1 Nano includes math exam scores in the payload — MATH Level 5 = 70 and AIME 2025 = 28.9; those place GPT-4.1 Nano at rank 11 of 14 for MATH Level 5 and 20 of 23 for AIME in our recorded rankings. Mistral Medium 3.1 has no MATH Level 5 / AIME entries in the payload.

Practical takeaway: Mistral dominates broad capability categories we measured (8 wins) — especially long-context, multilingual, and agentic planning. GPT-4.1 Nano beats Mistral when strict structured outputs or faithfulness are the top priorities.

Costs in the payload are per mtok (per 1,000 tokens). Per 1M tokens: GPT-4.1 Nano input = $0.1 * 1000 = $100, output = $0.4 * 1000 = $400. Mistral Medium 3.1 input = $0.4 * 1000 = $400, output = $2 * 1000 = $2,000. Under a simple 50/50 input-output split per 1M total tokens, GPT-4.1 Nano costs ~$250 while Mistral costs ~$1,200. At scale: 10M tokens (50/50) = GPT ~$2,500 vs Mistral ~$12,000; 100M tokens = GPT ~$25,000 vs Mistral ~$120,000. The cost gap matters if you serve high-volume APIs, realtime chat with heavy token usage, or multi-tenant enterprise deployments — GPU/hosting-neutral teams and startups should care most. If you run low-volume or high-value tasks where multilingual, agentic planning, or long-context top performance matters, the higher Mistral bill may be justified.

Choose GPT-4.1 Nano if you need: strict JSON/schema compliance and highest faithfulness (both scored 5 in our tests), very low per-token costs (input $0.1/mtok, output $0.4/mtok), and a huge declared context_window. Use cases: billing-sensitive production APIs that require exact structured outputs (data extraction, invoicing, strict schema generation), or teams needing lower run costs.

Choose Mistral Medium 3.1 if you need: the best multi-task capability across multilingual, agentic planning, long-context retrieval, constrained rewriting and classification (it won 8 of 12 tests, with 5s in those categories). Use cases: multilingual assistants, complex workflow automation, long-document analysis, and agentic multi-step pipelines where tradeoff reasoning and failure recovery matter more than raw cost.