Devstral Small 1.1 vs Gemini 2.5 Flash

Across our 12-test benchmark suite, Gemini 2.5 Flash wins 9 benchmarks, Devstral Small 1.1 wins 1, and they tie on 2.

Where Gemini 2.5 Flash leads:

• Tool calling: 5 vs 4. Gemini 2.5 Flash ties for 1st among 54 models; Devstral Small 1.1 ranks 18th. For agentic workflows that depend on accurate function selection and argument passing, this is a meaningful gap.
• Agentic planning: 4 vs 2. Gemini 2.5 Flash ranks 16th of 54; Devstral Small 1.1 ranks 53rd of 54 — near the bottom. Goal decomposition and failure recovery are serious weaknesses for Devstral Small 1.1 despite its software-engineering focus.
• Persona consistency: 5 vs 2. Gemini 2.5 Flash ties for 1st among 53 models; Devstral Small 1.1 ranks 51st. Customer-facing chatbots or character-driven applications should not use Devstral Small 1.1.
• Safety calibration: 4 vs 2. Gemini 2.5 Flash ranks 6th of 55 (4 models share this score); Devstral Small 1.1 ranks 12th but with 20 models sharing its score. In absolute terms this is the widest safety gap: Gemini 2.5 Flash is substantially better at refusing harmful requests while permitting legitimate ones in our testing.
• Creative problem solving: 4 vs 2. Gemini 2.5 Flash ranks 9th of 54; Devstral Small 1.1 ranks 47th. For brainstorming, ideation, or open-ended problem solving, Devstral Small 1.1 falls well short.
• Strategic analysis: 3 vs 2. Both score below the field median here (p50 = 4), but Gemini 2.5 Flash ranks 36th vs Devstral Small 1.1's 44th.
• Constrained rewriting: 4 vs 3. Gemini 2.5 Flash ranks 6th of 53; Devstral Small 1.1 ranks 31st. For compression tasks with hard character limits, Gemini 2.5 Flash is noticeably more reliable.
• Long context: 5 vs 4. Gemini 2.5 Flash ties for 1st among 55 models; Devstral Small 1.1 ranks 38th. Gemini 2.5 Flash also has a dramatically larger context window: 1,048,576 tokens vs 131,072 tokens. For retrieval over very long documents, this is not a close comparison.
• Multilingual: 5 vs 4. Both score well, but Gemini 2.5 Flash ties for 1st among 55 models while Devstral Small 1.1 ranks 36th.

Where Devstral Small 1.1 leads:

• Classification: 4 vs 3. Devstral Small 1.1 ties for 1st among 53 models (30 models share this score); Gemini 2.5 Flash ranks 31st. For routing, labeling, and categorization pipelines, Devstral Small 1.1 has a genuine edge.

Ties:

• Structured output: Both score 4, both rank 26th of 54. JSON schema compliance is equivalent.
• Faithfulness: Both score 4, both rank 34th of 55. Neither model stands out for grounded, source-faithful retrieval tasks.

Devstral Small 1.1 costs $0.10/MTok input and $0.30/MTok output. Gemini 2.5 Flash costs $0.30/MTok input and $2.50/MTok output. On output tokens — where most cost accumulates — Gemini 2.5 Flash is 8.3x more expensive.

At 1M output tokens/month: Devstral Small 1.1 costs $0.30 vs Gemini 2.5 Flash's $2.50 — a $2.20 difference that's negligible for most teams.

At 10M output tokens/month: $3 vs $25 — a $22 gap that starts to matter for lean startups.

At 100M output tokens/month: $300 vs $2,500 — a $2,200/month difference that is a serious budget line item for any product team.

Developers running high-throughput coding agents or classification pipelines should model their actual token volumes carefully. At scale, the cost gap alone could justify accepting Devstral Small 1.1's weaker general-purpose scores if the use case fits its strengths.

Choose Devstral Small 1.1 if:

• Your primary workload is classification, routing, or labeling at high volume — it ties for 1st on this benchmark and costs a fraction of Gemini 2.5 Flash.
• You are running cost-sensitive pipelines at 10M+ output tokens/month and can live with weaker agentic planning and persona consistency.
• Your use case is narrowly scoped to structured code tasks where its software-engineering fine-tuning is relevant and general-purpose scores matter less.
• You need structured JSON output and your context fits within 128K tokens — both models score identically here, and Devstral Small 1.1 is much cheaper.

Choose Gemini 2.5 Flash if:

• You need a capable all-around model: it wins on tool calling, agentic planning, long context, creative problem solving, multilingual output, persona consistency, and safety calibration.
• You are building agentic or multi-step workflows — its 4 vs 2 agentic planning score and 1st-tier tool calling make it far better suited.
• Your application is customer-facing and requires consistent persona or safe content handling.
• You need to process documents or conversations exceeding 131K tokens — its 1M token context window is in a different class.
• Your team's monthly output volume is under 10M tokens, where the cost difference stays under $22/month and is unlikely to be a deciding factor.