GPT-4o-mini vs Mistral Large 3 2512

Summary of wins (our 12-test suite): Mistral Large 3 2512 wins 6 tests, GPT-4o-mini wins 3, and 3 tests tie. Detailed walk-through:

• Structured output: Mistral 5 vs GPT-4o-mini 4. Mistral is tied for 1st (tied with 24 others out of 54) while GPT-4o-mini ranks 26 of 54. This indicates Mistral is more reliable for strict JSON/schema tasks (schema compliance and format adherence).

• Faithfulness: Mistral 5 vs GPT-4o-mini 3. Mistral ties for 1st on faithfulness (rank 1 of 55 tied with 32), meaning it sticks to source material and risks fewer hallucinations in our tests.

• Multilingual: Mistral 5 vs GPT-4o-mini 4. Mistral is tied for 1st (rank 1 of 55 tied with 34), so expect better parity across non-English outputs.

• Agentic planning & strategic analysis: Mistral leads (agentic planning 4 vs 3; strategic analysis 4 vs 2). For goal decomposition, tradeoff reasoning, and recovery strategies Mistral scored higher and ranks better (agentic planning rank 16 of 54). GPT-4o-mini’s lower scores suggest more limited multi-step planning in our tests.

• Creative problem solving: Mistral 3 vs GPT-4o-mini 2 — Mistral produced more specific feasible ideas in our creative tasks (rank 30 vs 47).

• Classification & Safety calibration: GPT-4o-mini wins classification (4 vs 3) and safety calibration (4 vs 1). GPT-4o-mini ties for 1st in classification (tied with 29 others) and ranks 6 of 55 on safety, while Mistral ranks 32 on safety. In practice GPT-4o-mini is better at routing/categorization and more conservative/precise on refusals in our tests.

• Persona consistency: GPT-4o-mini 4 vs Mistral 3 — GPT-4o-mini maintains character better in our suite.

• Ties: constrained rewriting (3/3), tool calling (4/4, both rank 18 of 54), and long context (4/4, both rank 38 of 55). Tool calling is equally capable at function selection and sequencing in our tests; both models handle long contexts similarly in retrieval tasks.

• External benchmarks (Epoch AI): GPT-4o-mini scores 52.6% on MATH Level 5 and 6.9% on AIME 2025 (Epoch AI). Mistral Large 3 2512 has no MATH Level 5 or AIME 2025 scores provided in the payload. These external numbers are supplementary and attributed to Epoch AI.

Operational notes from the payload: GPT-4o-mini has a 128,000-token context window and supports text+image+file->text modalities; Mistral Large 3 2512 exposes a 262,144-token window and text+image->text modality. Cost ratio in the payload is ~0.40 (GPT-4o-mini costs ~40% of Mistral per token).

Pricing in the payload is per 1k tokens (mtok). Using a 50/50 input:output token split, GPT-4o-mini costs $0.15 + $0.60 = $0.75 per 1k tokens ($750 per 1M tokens). Mistral Large 3 2512 costs $0.50 + $1.50 = $2.00 per 1k tokens ($2,000 per 1M tokens). At scale that gap grows: 10M tokens/month = $7,500 (GPT-4o-mini) vs $20,000 (Mistral); 100M = $75,000 vs $200,000. If your usage is output-heavy (small prompts), compare output-only: $600/M for GPT-4o-mini vs $1,500/M for Mistral. The cost gap matters for high-volume consumer apps, analytics pipelines, or multi-tenant APIs; teams focused on accuracy, schema compliance, or non-English quality may justify Mistral’s higher per-token price, while startups and high-volume builders will prefer GPT-4o-mini for cost efficiency.

Choose Mistral Large 3 2512 if: you need top-tier structured output, faithfulness, multilingual parity, or stronger agentic/strategic reasoning — and you can absorb ~$2.00 per 1k tokens (about $2,000 per 1M under a 50/50 input/output split). Choose GPT-4o-mini if: you need the lowest production cost ($0.75 per 1k under a 50/50 split, $750 per 1M), better safety calibration and classification in our tests, or are optimizing for high-volume apps where token cost dominates. If you require both extremes (high accuracy + low cost), consider using Mistral for schema-critical paths and GPT-4o-mini for bulk classification/guardrails.