Devstral 2 2512 vs Ministral 3 8B 2512

Neither model has been run through our full 12-test benchmark suite with a computed average score, so there is no single numeric summary to compare. What the data does show is a clear directional advantage for Devstral 2 2512 across more dimensions.

Devstral 2 2512 wins (6 benchmarks):

• Structured output: 5 vs 4 — Devstral 2 2512 ties for 1st among 54 models on JSON schema compliance; Ministral 3 8B 2512 ranks 26th. This matters significantly for API-driven workflows and tool integrations.
• Long context: 5 vs 4 — Devstral 2 2512 ties for 1st among 55 models on retrieval at 30K+ tokens; Ministral 3 8B 2512 ranks 38th. Both share the same 262K context window, but Devstral 2 2512 uses it more effectively in our testing.
• Multilingual: 5 vs 4 — Devstral 2 2512 ties for 1st among 55 models; Ministral 3 8B 2512 ranks 36th. A meaningful gap for global deployments.
• Agentic planning: 4 vs 3 — Devstral 2 2512 ranks 16th of 54 (among 26 tied); Ministral 3 8B 2512 ranks 42nd. Goal decomposition and failure recovery is a key capability for autonomous coding agents, which aligns with Devstral 2 2512's stated specialization.
• Strategic analysis: 4 vs 3 — Devstral 2 2512 ranks 27th of 54; Ministral 3 8B 2512 ranks 36th. Both are below the field median at this score level, but Devstral 2 2512 has the edge.
• Creative problem solving: 4 vs 3 — Devstral 2 2512 ranks 9th of 54; Ministral 3 8B 2512 ranks 30th. A notable gap on generating non-obvious, feasible ideas.

Ministral 3 8B 2512 wins (2 benchmarks):

• Classification: 4 vs 3 — Ministral 3 8B 2512 ties for 1st among 53 models; Devstral 2 2512 ranks 31st. For routing and categorization tasks, Ministral 3 8B 2512 is genuinely superior in our testing.
• Persona consistency: 5 vs 4 — Ministral 3 8B 2512 ties for 1st among 53 models; Devstral 2 2512 ranks 38th. Chatbot and character-maintenance use cases favor the smaller model here.

Ties (4 benchmarks): Constrained rewriting (both 5/5, both tied for 1st), tool calling (both 4/5, both rank 18th of 54), faithfulness (both 4/5, both rank 34th of 55), and safety calibration (both 1/5, both rank 32nd of 55). The safety calibration tie at the bottom of the field is worth flagging — both models score at the 25th percentile or below on refusing harmful requests while permitting legitimate ones. Neither should be deployed in sensitive contexts without additional guardrails.

The cost gap here is substantial. Devstral 2 2512 costs $0.40 per million input tokens and $2.00 per million output tokens. Ministral 3 8B 2512 costs $0.15 per million tokens for both input and output — a flat, symmetric rate. At 1M output tokens/month, Devstral 2 2512 costs $2.00 vs $0.15 for Ministral 3 8B 2512 — a $1.85 difference that's easy to absorb. Scale to 10M output tokens and the gap becomes $18.50/month; at 100M tokens, you're looking at $1,850/month in additional spend for Devstral 2 2512. Developers building high-throughput pipelines — chatbots, classification systems, document processing — should take that seriously. Where Ministral 3 8B 2512 scores are competitive (tool calling, constrained rewriting, faithfulness all tie), the cost argument for the smaller model is strong. For agentic coding workflows or tasks requiring deep strategic reasoning, Devstral 2 2512's performance premium may justify the spend.

Choose Devstral 2 2512 if: You are building agentic coding systems, long-document pipelines, or multilingual applications where its scores of 4–5 on agentic planning, long context, structured output, and multilingual benchmarks in our testing translate directly to task quality. Its 262K context window is actually exploited more effectively in our testing (rank 1 vs rank 38 on long context). Budget is secondary to capability, and your volumes are moderate enough that the $2.00/M output cost is manageable.

Choose Ministral 3 8B 2512 if: You need vision input (text+image modality), are running high-volume classification or routing workloads where it ties for 1st in our testing, or need strong persona consistency for chat applications (ties for 1st vs Devstral 2 2512's rank 38). At $0.15/M for both input and output, it is dramatically cheaper — at 100M output tokens/month, you save $1,850 vs Devstral 2 2512. It also supports logprobs and top_logprobs parameters not available in Devstral 2 2512, useful for probabilistic applications. For most cost-sensitive or vision-enabled workloads, Ministral 3 8B 2512 delivers competitive quality at a fraction of the price.