Devstral 2 2512 vs Llama 4 Scout

Across our 12-test suite, Devstral 2 2512 outscores Llama 4 Scout on 7 tests, loses on 2, and ties on 3.

Where Devstral 2 2512 wins:

• Constrained rewriting (5 vs 3): Devstral 2 2512 ties for 1st with 4 other models out of 53 tested; Llama 4 Scout ranks 31st. This matters for any task requiring compression within hard character limits — ad copy, SMS, titles.
• Structured output (5 vs 4): Devstral 2 2512 ties for 1st out of 54 models; Llama 4 Scout ranks 26th. JSON schema compliance is foundational for any API-driven or tool-augmented workflow.
• Multilingual (5 vs 4): Devstral 2 2512 ties for 1st out of 55; Llama 4 Scout ranks 36th. A meaningful gap for non-English deployments.
• Agentic planning (4 vs 2): This is the starkest gap. Devstral 2 2512 ranks 16th of 54; Llama 4 Scout ranks 53rd of 54 — near the bottom. For goal decomposition, multi-step task execution, and failure recovery, Llama 4 Scout is a poor fit.
• Strategic analysis (4 vs 2): Devstral 2 2512 ranks 27th of 54; Llama 4 Scout ranks 44th. Nuanced tradeoff reasoning with real numbers favors Devstral 2 2512 significantly.
• Creative problem solving (4 vs 3): Devstral 2 2512 ranks 9th of 54; Llama 4 Scout ranks 30th.
• Persona consistency (4 vs 3): Devstral 2 2512 ranks 38th of 53; Llama 4 Scout ranks 45th. Neither is elite here, but Devstral 2 2512 edges ahead.

Where Llama 4 Scout wins:

• Classification (4 vs 3): Llama 4 Scout ties for 1st with 29 other models out of 53; Devstral 2 2512 ranks 31st. For categorization and routing tasks, Llama 4 Scout matches the field's best.
• Safety calibration (2 vs 1): Llama 4 Scout ranks 12th of 55; Devstral 2 2512 ranks 32nd. Both scores are below the field median of 2, but Llama 4 Scout is more calibrated at refusing harmful requests while permitting legitimate ones.

Ties (both models perform equally):

• Tool calling (4 vs 4): Both rank 18th of 54, sharing that position with 28 other models. Adequate for function selection and argument accuracy, but not top-tier.
• Faithfulness (4 vs 4): Both rank 34th of 55. Solid but not exceptional at sticking to source material.
• Long context (5 vs 5): Both tie for 1st out of 55 models. Retrieval accuracy at 30K+ tokens is strong for both — no reason to pick one over the other on this dimension.

Devstral 2 2512 is priced at $0.40/M input and $2.00/M output tokens. Llama 4 Scout comes in at $0.08/M input and $0.30/M output — making it 5x cheaper on input and 6.7x cheaper on output. At 1M output tokens/month, Devstral 2 2512 costs $2.00 versus $0.30 for Llama 4 Scout — a $1.70 difference that's negligible. At 10M output tokens/month, that gap grows to $17.00 versus $3.00 — still manageable for most teams. At 100M output tokens/month, the difference becomes $200 versus $30: a $170/month delta that starts to matter for high-throughput production systems. Developers running classification pipelines, retrieval-augmented generation, or general-purpose routing — where Llama 4 Scout's benchmark scores are competitive — should weigh whether Devstral 2 2512's broader benchmark wins justify that cost multiple at scale.

Choose Devstral 2 2512 if your workflow involves agentic coding, multi-step planning, structured outputs for APIs, multilingual content, or strategic analysis. Its 123B-parameter architecture with 256K context and top-tier scores on constrained rewriting (1st of 53), structured output (1st of 54), and agentic planning (16th vs Llama 4 Scout's near-last 53rd of 54) make it the clear technical choice for developer tooling, autonomous agents, and production pipelines that demand reliable format adherence. Budget $2.00/M output tokens for that capability.

Choose Llama 4 Scout if your primary tasks are classification, routing, or retrieval-augmented generation — where it ties for 1st on classification and matches Devstral 2 2512 on long context and tool calling. At $0.30/M output tokens, it's 6.7x cheaper. Llama 4 Scout also supports image input (text+image->text modality) and a 327K context window, making it a reasonable choice for multimodal ingestion pipelines. It is not suited for agentic workflows given its near-bottom ranking on agentic planning.