Gemini 2.5 Pro vs Mistral Large 3 2512

Wins and ties (our 12-test suite): Gemini wins: creative_problem_solving 5 vs 3 (practical idea generation), tool_calling 5 vs 4 (better function selection and arguments), classification 4 vs 3 (more accurate routing), long_context 5 vs 4 (superior retrieval at 30K+ tokens), persona_consistency 5 vs 3 (keeps character and resists injection). Ties: structured_output 5/5 (both top-ranked for JSON/schema compliance), strategic_analysis 4/4, constrained_rewriting 3/3, faithfulness 5/5, safety_calibration 1/1, agentic_planning 4/4, multilingual 5/5. Rankings add context: Gemini’s long_context is tied for 1st (tied with 36 others out of 55) while Mistral’s long_context ranks 38 of 55, so Gemini’s edge in very long contexts is meaningful for multi-file or 1M+ token prompts. Gemini’s tool_calling score is tied for 1st (with 16 others), Mistral ranks 18 of 54 on tool_calling — Gemini is likelier to pick and sequence functions correctly in our tests. On structured outputs both tie and are tied for 1st, so if your priority is strict schema compliance either model is acceptable. External benchmarks (Epoch AI): Gemini scores 57.6% on SWE-bench Verified and 84.2% on AIME 2025 — these external results corroborate Gemini’s strength on coding and advanced math tasks; Mistral has no external SWE/AIME scores in the payload.

Pricing per model (per 1M tokens): Gemini 2.5 Pro input $1.25/mTok → $1,250; output $10/mTok → $10,000. Mistral Large 3 2512 input $0.50/mTok → $500; output $1.50/mTok → $1,500. Combined cost for 1M input + 1M output = Gemini $11,250 vs Mistral $2,000. At 10M tokens (1M in+1M out ×10): Gemini $112,500 vs Mistral $20,000. At 100M tokens: Gemini $1,125,000 vs Mistral $200,000. The payload’s priceRatio is 6.67, driven by Gemini’s $10 vs Mistral’s $1.5 output rate. If you run high-volume consumer-facing services or batch pipelines, Mistral’s lower per-token price materially reduces monthly bills; if you need the larger context window or higher scores on certain benchmarks, Gemini may justify the premium for targeted workloads.

Choose Gemini 2.5 Pro if you need: very large context (1,048,576 tokens), top results in creative problem solving, tool calling, long-context retrieval, and persona consistency — and you can accept a substantial price premium. Choose Mistral Large 3 2512 if you need: strong structured-output and faithfulness at a fraction of the cost (Apache 2.0 license noted in the model description), multilingual parity, and production-scale, high-volume deployments where per-token cost dominates.