GPT-5.1 vs Mistral Large 3 2512

All claims below are from our 12-test suite. Wins/ties summary: GPT-5.1 wins 7 tests, Mistral wins 1, and 4 tests are ties. Detailed walk-through:

• Strategic analysis: GPT-5.1 5 vs Mistral 4 — GPT-5.1 ties for 1st (rank: tied for 1st with 25 others of 54) while Mistral is mid-pack (rank 27/54). This matters for tasks needing nuanced trade-off reasoning with numbers.
• Constrained rewriting: GPT-5.1 4 vs Mistral 3 — GPT-5.1 ranks 6/53, so it handles strict compression and character limits better in our tests.
• Creative problem solving: GPT-5.1 4 vs Mistral 3 — GPT-5.1 ranks 9/54; expect more specific feasible ideas from GPT-5.1.
• Classification: GPT-5.1 4 vs Mistral 3 — GPT-5.1 tied for 1st (29 other models), so routing/labeling is more reliable in our runs.
• Long-context: GPT-5.1 5 vs Mistral 4 — GPT-5.1 tied for 1st (36 other models) vs Mistral rank 38/55; GPT-5.1 better at retrieval/accuracy beyond 30K tokens.
• Safety calibration: GPT-5.1 2 vs Mistral 1 — GPT-5.1 ranks 12/55 vs Mistral 32/55; GPT-5.1 is more likely to calibrate safety requests correctly in our tests.
• Persona consistency: GPT-5.1 5 vs Mistral 3 — GPT-5.1 tied for 1st (36 others) while Mistral is low (rank 45/53), so GPT-5.1 holds character and resists injection better.
• Structured output: Mistral 5 vs GPT-5.1 4 — Mistral ties for 1st with 24 others (GPT-5.1 rank 26/54). Pick Mistral when strict JSON/schema compliance is primary.
• Tool calling: tie 4/4 — both rank 18/54; expect similar function selection and sequencing in our tests.
• Faithfulness: tie 5/5 — both tied for 1st (32 others); both stick closely to source material in our runs.
• Agentic planning: tie 4/4 — both rank 16/54; comparable at goal decomposition and recovery.
• Multilingual: tie 5/5 — both tied for 1st (34 others); comparable non‑English quality. External benchmarks (supplementary): GPT-5.1 scores 68% on SWE-bench Verified and 88.6% on AIME 2025 according to Epoch AI; those external results place GPT-5.1 at rank 7 on both listed external sets in our payload. Mistral Large 3 2512 has no external benchmarks in this payload. Overall, GPT-5.1 is stronger on high‑value reasoning, long context and classification; Mistral is the leader for rigid structured-output workloads and offers a much lower per-token cost.

Per the payload, GPT-5.1 costs $1.25 input / $10.00 output per million tokens; Mistral Large 3 2512 costs $0.50 input / $1.50 output per million. Price ratio is 6.6667. Using a 50/50 input/output token split as a simple real-world proxy: per 1M tokens GPT-5.1 ≈ $5.625 vs Mistral ≈ $1.00; per 10M tokens GPT-5.1 ≈ $56.25 vs Mistral ≈ $10.00; per 100M tokens GPT-5.1 ≈ $562.50 vs Mistral ≈ $100.00. If your workload is output-heavy the gap widens (output-only: 1M tokens = $10.00 vs $1.50). Teams with millions of monthly tokens (chatbots, high-throughput APIs) should care — Mistral cuts token bills by ~80–85% at scale, while GPT-5.1 is justified when its higher scores materially improve downstream value or reduce human review costs.

Choose GPT-5.1 if: you need top-tier long-context retrieval, strategic numeric reasoning, stronger persona consistency, or higher classification and creative-problem-solving quality where small accuracy gains avoid significant human review costs. Example tasks: legal/financial analysis, long-document assistants, strategy reports, or apps where hallucination risk must be minimized. Choose Mistral Large 3 2512 if: you need cost-efficient production at scale, strict JSON/schema compliance, or schema-driven pipelines (data extraction, form filling, deterministic outputs). Example tasks: high-volume API chat with structured responses, automated data ingestion, or any workload where token cost dominates decisioning.