Claude Opus 4.6 vs Devstral Medium

Summary of head-to-head results in our 12-test suite (scores are our internal 1–5 scale unless otherwise noted). Claude Opus 4.6 wins on strategic_analysis (5 vs 2; tied for 1st of 54 in our rankings), creative_problem_solving (5 vs 2; tied for 1st of 54), agentic_planning (5 vs 4; tied for 1st of 54), tool_calling (5 vs 3; tied for 1st of 54), faithfulness (5 vs 4; tied for 1st of 55), long_context (5 vs 4; tied for 1st of 55), safety_calibration (5 vs 1; tied for 1st of 55), persona_consistency (5 vs 3; tied for 1st of 53), and multilingual (5 vs 4; tied for 1st of 55). Devstral Medium wins classification (4 vs 3) and is tied for 1st on that test in our rankings (tied with 29 others out of 53). Structured_output is a tie (4/4; rank 26 of 54 for both) and constrained_rewriting is also a tie (3/3). Practical meaning: Claude’s 5/5 results on tool_calling and agentic_planning indicate reliable function selection, argument accuracy and sequencing for multi-step agent workflows; its long_context 5 means better retrieval and coherence across 30K+ token contexts. Claude also leads on safety_calibration and faithfulness, so it will more reliably refuse harmful prompts and stick to source material. Devstral’s classification 4 (tied for 1st) makes it the cheaper, strong choice for routing/categorization tasks. External benchmarks: beyond our internal suite, Claude Opus 4.6 scores 78.7% on SWE-bench Verified (Epoch AI) — rank 1 of 12 in our records — and 94.4% on AIME 2025 (Epoch AI), giving independent evidence for its coding/math strengths; Devstral has no SWE-bench/AIME external scores in the payload.

Pricing (per 1,000 tokens): Claude Opus 4.6 input $5 / output $25; Devstral Medium input $0.4 / output $2. Assuming a 1:1 input:output token ratio, monthly costs are: for 1M tokens — Claude $30,000 vs Devstral $2,400; 10M — Claude $300,000 vs Devstral $24,000; 100M — Claude $3,000,000 vs Devstral $240,000. The payload's priceRatio is 12.5×, which matches the per‑1M comparison. Who should care: startups, SaaS products, and anyone with high-volume inference (10M+ tokens/month) will feel the gap immediately; teams doing smaller-scale experimentation (<1M tokens) can tolerate Claude's higher cost for the quality gains, while ops/edge services should prefer Devstral for predictable, low per-token spend.

Choose Claude Opus 4.6 if you need: production-grade coding and agentic workflows, very long-context retrieval (30K+ tokens), high safety calibration, or maximum faithfulness — its wins on tool_calling, long_context, safety_calibration and swebench_verified (78.7% on SWE-bench Verified, Epoch AI) support that. Choose Devstral Medium if you need: the lowest per-token cost, high-throughput classification or routing (classification 4, tied for 1st), and a budget-friendly model for large-volume inference where top-tier agent tooling or extreme long-context performance is not required.