Claude Opus 4.7 vs Devstral Small 1.1

Claude Opus 4.7 wins 9 of 12 benchmarks in our testing. Devstral Small 1.1 wins 1 (classification), and they tie on 2 (structured output and multilingual).

Where Opus 4.7 leads decisively:

• Agentic planning: 5/5 vs 2/5. Opus 4.7 ties for 1st with 15 other models out of 55 tested; Devstral Small 1.1 ranks 54th of 55. This is the starkest gap — goal decomposition and failure recovery are clearly outside Devstral Small 1.1's strengths, which matters enormously for multi-step autonomous agents.

• Strategic analysis: 5/5 vs 2/5. Opus 4.7 ties for 1st among 55 models; Devstral Small 1.1 ranks 45th. Nuanced tradeoff reasoning with real numbers is a core Opus 4.7 strength.

• Creative problem solving: 5/5 vs 2/5. Opus 4.7 ties for 1st among 9 models out of 55; Devstral Small 1.1 ranks 48th of 55. Generating non-obvious, feasible ideas is where the gap shows up in real product ideation and design tasks.

• Tool calling: 5/5 vs 4/5. Opus 4.7 ties for 1st among 18 models out of 55; Devstral Small 1.1 ranks 19th. Both score competently, but Opus 4.7 has an edge in function selection, argument accuracy, and sequencing — relevant for any agentic or API-orchestration workflow.

• Faithfulness: 5/5 vs 4/5. Opus 4.7 ties for 1st among 34 models out of 56; Devstral Small 1.1 ranks 35th. Sticking to source material without hallucinating is a meaningful difference for summarization and document Q&A.

• Long context: 5/5 vs 4/5. Opus 4.7 ties for 1st among 38 models out of 56; Devstral Small 1.1 ranks 39th. Opus 4.7 also has a dramatically larger context window: 1,000,000 tokens vs Devstral Small 1.1's 131,072 tokens — a practical limit for very long document workflows.

• Persona consistency: 5/5 vs 2/5. Opus 4.7 ties for 1st among 38 models out of 55; Devstral Small 1.1 ranks 53rd. Critical for chatbots, roleplay, or any product where the AI must maintain a stable identity and resist prompt injection.

• Constrained rewriting: 4/5 vs 3/5. Opus 4.7 ranks 6th of 55; Devstral Small 1.1 ranks 32nd.

• Safety calibration: 3/5 vs 2/5. Opus 4.7 ranks 10th of 56; Devstral Small 1.1 ranks 13th. Neither model leads the field here — the median score across all 56 tested models is 2/5 — but Opus 4.7 is the stronger of the two.

Where Devstral Small 1.1 holds its own or wins:

• Classification: 4/5 vs 3/5. Devstral Small 1.1 ties for 1st among 30 models out of 54 tested; Opus 4.7 ranks 31st. This is Devstral Small 1.1's clearest win and a meaningful one for routing, labeling, and categorization pipelines.

• Structured output: 4/5 vs 4/5 — a tie. Both rank 26th of 55. JSON schema compliance and format adherence are equivalent between them.

• Multilingual: 4/5 vs 4/5 — a tie. Both rank 36th of 56. Neither dominates on non-English output quality.

The pattern is clear: Opus 4.7 is the stronger general-purpose model by a wide margin, particularly for reasoning-heavy and agentic tasks. Devstral Small 1.1 is a specialized software engineering model (a 24B parameter model fine-tuned from Mistral Small 3.1) that punches above its weight on classification but struggles with open-ended reasoning and planning.

The cost difference here is extreme. Claude Opus 4.7 runs $5 per million input tokens and $25 per million output tokens. Devstral Small 1.1 runs $0.10 per million input tokens and $0.30 per million output tokens — an 83x difference on output costs.

At 1 million output tokens per month, that's $25 for Opus 4.7 vs $0.30 for Devstral Small 1.1 — a gap of $24.70 you'd barely notice. At 10 million output tokens, it's $250 vs $3, a $247 difference that starts to matter for early-stage products. At 100 million output tokens — a realistic volume for production APIs — you're looking at $2,500 vs $30 per month. That $2,470 monthly delta is a meaningful infrastructure line item.

Developers building agentic pipelines, code review bots, or classification systems at scale should model their expected token volumes carefully. If your use case falls in Devstral Small 1.1's stronger zones — classification and structured output — the quality-per-dollar argument for the smaller model becomes hard to ignore at 10M+ tokens per month. If you need Opus 4.7's advantages in agentic planning, strategic reasoning, or creative problem solving, the premium is likely justified — but you should know what you're paying for.

Choose Claude Opus 4.7 if:

• You're building autonomous agents that need to plan, recover from failures, and orchestrate tools — it scores 5/5 on agentic planning (vs Devstral Small 1.1's 2/5) and 5/5 on tool calling.
• Your application requires strategic reasoning, nuanced analysis, or creative problem solving — Opus 4.7 leads on all three.
• You need consistent personas or reliable character stability — Opus 4.7 scores 5/5, Devstral Small 1.1 scores 2/5.
• You're working with very long documents — Opus 4.7's 1,000,000-token context window is roughly 7.5x larger than Devstral Small 1.1's 131,072 tokens.
• Faithfulness to source material matters — Opus 4.7 scores 5/5 vs 4/5.
• Budget is secondary to capability.

Choose Devstral Small 1.1 if:

• Classification and routing are your primary task — it ties for 1st among 30 models and beats Opus 4.7 outright on this benchmark.
• You're running high-volume workloads where cost is a first-order constraint — at $0.30 per million output tokens vs $25, the savings at 100M tokens/month are approximately $2,470.
• Your use case is structured output generation — both models tie at 4/5, so why pay more?
• You need API-level control with explicit parameter support (frequency penalty, seeding, response format, structured outputs, tool choice) — these are documented in the payload for Devstral Small 1.1.
• You're building a narrowly scoped software engineering agent where the model's 24B coding focus is a better fit than a general-purpose frontier model.