Devstral 2 2512 vs Llama 4 Maverick

Across our 12-test suite, Devstral 2 2512 wins 8 benchmarks, Llama 4 Maverick wins 2, and they tie on 2. Here's what those scores mean in practice:

Devstral 2 2512 wins:

• Constrained rewriting (5 vs 3): Devstral ties for 1st among 53 models tested; Maverick ranks 31st. For tasks requiring tight compression — summaries within character limits, copy editing with strict constraints — this is a substantial gap.
• Structured output (5 vs 4): Devstral ties for 1st among 54 models; Maverick ranks 26th. In production pipelines that depend on valid JSON or schema-adherent output, Devstral is more reliable.
• Long context (5 vs 4): Devstral ties for 1st among 55 models; Maverick ranks 38th. Devstral also has a larger context window (262K vs 1M tokens — notably, Maverick's 1M window is larger, but Devstral retrieves more accurately at 30K+ tokens in our testing).
• Strategic analysis (4 vs 2): Devstral ranks 27th of 54; Maverick ranks 44th. For nuanced tradeoff reasoning with real numbers, Devstral is considerably sharper.
• Agentic planning (4 vs 3): Devstral ranks 16th of 54; Maverick ranks 42nd. This is meaningful for multi-step workflows requiring goal decomposition and failure recovery.
• Creative problem solving (4 vs 3): Devstral ranks 9th of 54; Maverick ranks 30th.
• Tool calling (4 vs N/A): Devstral scored 4/5. Maverick's tool calling test hit a 429 rate limit during our testing on 2026-04-13 and produced no score — likely a transient infrastructure issue, not a capability flaw. No conclusion should be drawn about Maverick's tool calling ability from this absence.
• Multilingual (5 vs 4): Devstral ties for 1st among 55 models; Maverick ranks 36th.

Llama 4 Maverick wins:

• Safety calibration (2 vs 1): Maverick ranks 12th of 55; Devstral ranks 32nd. Both scores are below the field median of 2, but Maverick is meaningfully better at refusing harmful requests while permitting legitimate ones. For consumer-facing applications, this matters.
• Persona consistency (5 vs 4): Maverick ties for 1st among 53 models; Devstral ranks 38th. For chatbots, roleplay, or branded assistant experiences, Maverick holds character more reliably.

Ties:

• Faithfulness (4 vs 4): Both rank 34th of 55 — identical performance on sticking to source material without hallucinating.
• Classification (3 vs 3): Both rank 31st of 53 — below the field median of 4. Neither model is a standout for routing or categorization tasks.

Devstral 2 2512 costs $0.40/M input and $2.00/M output tokens. Llama 4 Maverick costs $0.15/M input and $0.60/M output tokens — making Maverick roughly 2.7x cheaper on input and 3.3x cheaper on output. At real-world volumes, the gap compounds fast: at 1M output tokens/month, you pay $2.00 for Devstral vs $0.60 for Maverick — a $1.40 difference that's easy to absorb. At 10M output tokens/month, that's $20 vs $6 — $14/month saved, meaningful for a side project. At 100M output tokens/month, Devstral costs $200 vs Maverick's $60 — a $140/month gap that becomes a real line item for production systems. Developers running high-volume pipelines where Devstral's advantages in agentic planning or strategic analysis aren't needed should strongly consider Maverick. Conversely, if you're building an agentic coding assistant or analytical tool where Devstral's benchmark edge translates to measurably better outputs, the premium is likely justifiable. Note that Llama 4 Maverick also supports image input (text+image->text), which Devstral 2 2512 does not — for multimodal use cases, Maverick is the only option here regardless of price.

Choose Devstral 2 2512 if: You're building agentic coding workflows, multi-step automation pipelines, or document-heavy applications requiring accurate long-context retrieval. Its scores of 4 on agentic planning and tool calling, 5 on structured output and long context, and 5 on constrained rewriting make it the stronger technical workhorse. It's also the better choice for multilingual production use cases (5 vs 4). Accept the $2.00/M output token price as a cost of doing business.

Choose Llama 4 Maverick if: Cost efficiency is a priority and you don't need top-tier agentic or analytical reasoning. At $0.60/M output tokens, it saves real money at scale. It's the only option here for multimodal (image+text) input. Its 5/5 persona consistency makes it well-suited for character-driven chat products or brand voice assistants. Its better safety calibration score (2 vs 1) makes it more appropriate for consumer-facing deployments where refusal behavior matters.