Claude Opus 4.6 vs Mistral Large 3 2512

Across our 12-test suite (scores 1–5), Claude Opus 4.6 wins the majority: strategic_analysis 5 vs 4 (Claude tied for 1st of 54; Mistral rank 27), creative_problem_solving 5 vs 3 (Claude tied for 1st), agentic_planning 5 vs 4 (Claude tied for 1st; Mistral rank 16), tool_calling 5 vs 4 (Claude tied for 1st; Mistral rank 18), long_context 5 vs 4 (Claude tied for 1st; Mistral rank 38), safety_calibration 5 vs 1 (Claude tied for 1st; Mistral rank 32), and persona_consistency 5 vs 3 (Claude tied for 1st; Mistral rank 45). Mistral wins structured_output 5 vs 4 (Mistral tied for 1st of 54; Claude ranks 26). Ties: constrained_rewriting 3/3, faithfulness 5/5 (both tied for 1st), classification 3/3, multilingual 5/5 (both tied for 1st). Practically: Claude’s 5/5 results on tool_calling, long_context, agentic_planning and safety_calibration mean it handles multi-step workflows, long documents (30K+ token retrieval), and safer policy alignment better in our tests — valuable for coding agents, complex analysis, and production assistants. Mistral’s structured_output 5/5 (tied for 1st) indicates it more reliably adheres to JSON/schema constraints, which matters for strict API output and ingestion pipelines. On external benchmarks, Claude Opus 4.6 scores 78.7% on SWE-bench Verified (Epoch AI) and 94.4% on AIME 2025 (Epoch AI) in our data; Mistral has no external scores reported in this payload.

Per the payload, Claude Opus 4.6 charges $5 input / $25 output per mTok; Mistral Large 3 2512 charges $0.50 input / $1.50 output per mTok. Price ratio (output): 25 / 1.5 = 16.67×. Example costs for 1M total tokens (1,000 mTok):

• All-output scenario: Claude = $25,000; Mistral = $1,500.
• All-input scenario: Claude = $5,000; Mistral = $500.
• 50/50 input/output split: Claude = $15,000; Mistral = $1,000. Scale these linearly: 10M tokens = 10×, 100M tokens = 100×. That means 100M tokens (50/50) cost Claude ≈ $1.5M vs Mistral ≈ $100k. Developers and businesses with high-volume inference (millions+ tokens/month) should care deeply about this gap; startups or prototypes with low volume may prefer Claude for its higher benchmark performance, while cost-sensitive production deployments typically favor Mistral for its ~16–17× lower output price.

Choose Claude Opus 4.6 if you need best-in-class agentic behavior, long-context accuracy, tool-calling correctness, safety calibration, or top coding performance in our tests — and you can absorb significantly higher inference cost. Choose Mistral Large 3 2512 if you need production-grade, low-cost inference at scale or require strict structured outputs/JSON compliance (it wins structured_output 5 vs Claude’s 4) and want drastically lower per-token spend ($1.50 vs $25/mTok output).