GPT-4o-mini vs Mistral Small 4

Summary of wins in our 12-test suite: Mistral Small 4 wins 7 tests, GPT-4o-mini wins 2, and 3 are ties (constrained rewriting, tool calling, long context). Detailed breakdown (scores are our internal 1–5 proxies unless noted):

• structured output: Mistral 5 vs GPT-4o-mini 4 — Mistral ties for 1st (tied with 24 others out of 54). This means Mistral is more reliable producing strict JSON/schema-compliant outputs in our tests.
• strategic analysis: Mistral 4 vs GPT-4o-mini 2 — Mistral ranks 27 of 54 vs GPT-4o-mini rank 44; useful when tasks need nuanced tradeoff reasoning with numbers.
• creative problem solving: Mistral 4 vs GPT-4o-mini 2 — Mistral ranks 9 of 54 (tied with many) while GPT-4o-mini ranks 47; Mistral produced more feasible, non-obvious ideas in our prompts.
• faithfulness: Mistral 4 vs GPT-4o-mini 3 — Mistral has a higher faithfulness score and ranks 34 of 55 vs GPT-4o-mini's rank 52, indicating fewer source-hallucination failures in our tests.
• persona consistency: Mistral 5 vs GPT-4o-mini 4 — Mistral ties for 1st with 36 others; it better maintains character and resists injection across dialogues in our suite.
• agentic planning: Mistral 4 vs GPT-4o-mini 3 — Mistral ranks 16 of 54 vs GPT-4o-mini rank 42; better at goal decomposition and recovery in our scenarios.
• multilingual: Mistral 5 vs GPT-4o-mini 4 — Mistral ties for 1st with 34 others out of 55; expect stronger non-English parity in our tests.

GPT-4o-mini wins:

• classification: GPT-4o-mini 4 vs Mistral 2 — GPT-4o-mini is tied for 1st with 29 others out of 53 tested, so it performed best at routing/categorization tasks in our runs.
• safety calibration: GPT-4o-mini 4 vs Mistral 2 — GPT-4o-mini ranks 6 of 55 (tied with 3 others), meaning it refused harmful requests and permitted legitimate ones more reliably in our experiments.

Ties and near-ties:

• tool calling: both 4 — both rank 18 of 54 (tied with many); function selection and argument sequencing behaved similarly in our tests.
• long context: both 4 — both rank 38 of 55 (tied); both handled 30k+ token retrieval cases comparably.
• constrained rewriting: both 3 — equal performance compressing within tight character limits.

External/math benchmarks (Epoch AI): GPT-4o-mini scores 52.6% on MATH Level 5 and 6.9% on AIME 2025 (these external scores are from Epoch AI and supplement our internal proxies). Those percentages suggest GPT-4o-mini is modest on advanced contest math in those specific public tests.

Operational notes from payload: GPT-4o-mini offers a 128,000-token context window and supports text+image+file->text; Mistral Small 4 supports a 262,144-token context window and text+image->text. Supported parameters differ (e.g., GPT-4o-mini exposes web_search_options, logprobs; Mistral exposes include_reasoning/reasoning and top_k), which can affect integration choices.

Both models have identical published rates in the payload: $0.15 per 1k input tokens and $0.60 per 1k output tokens. At 1M tokens (1,000 mTok): pure-input cost = $150, pure-output cost = $600, and a 50/50 split = $375. At 10M tokens: pure-input = $1,500, pure-output = $6,000, 50/50 = $3,750. At 100M tokens: pure-input = $15,000, pure-output = $60,000, 50/50 = $37,500. Because price is equal (priceRatio = 1), the cost decision is moot — teams should focus on accuracy, safety, context window, and supported parameters instead of per-token pricing.

Choose Mistral Small 4 if you need: structured, schema-compliant outputs, better creative problem solving, stronger multilingual and persona consistency, or a larger context window (262,144 tokens). Choose GPT-4o-mini if you need: safer default refusals and the strongest classification/routing behavior (tied for 1st in our tests), or file input support and the 128,000-token context window. Pricing is identical; pick based on the capability tradeoffs above.