Both Devstral 2 and Devstral Small 2 are models geared toward "software engineering/code intelligence": the official documentation emphasizes their strength in using tools to explore codebases, edit multiple files, and drive engineering agents. Mistral AI documentation +1
The official pages show that both have a 256k context; after the free period, API pricing is: Devstral 2 $0.40 / $2.00 (input/output, per million tokens), and Small 2 $0.10 / $0.30. Mistral AI +2 Mistral AI documentation +2
The safest way to choose is not "by feel," but by running the same multi-file project prompt twice in Playground: Devstral 2512 vs. Labs Devstral Small 2512, comparing output quality across four metrics.
First, put "verifiable facts" in front of your desk. The following information is cited solely from the official/model card pages:
Devstral 2: Officially positioned as a "cutting-edge code agents model for software engineering tasks," emphasizing tool usage, codebase exploration, and multi-file editing capabilities; the page indicates 256k context and provides a price (charged per token after the free period). Mistral AI documentation +1
Devstral Small 2: Officially positioned similarly, emphasizing tool usage, codebase exploration, and multi-file editing; the page indicates 256k context and provides a lower token price. Mistral AI documentation +1
Official news further clarifies: the API is currently free; after the free period, Devstral 2 and Small 2 will be priced at $0.40/$2.00 and $0.10/$0.30 (input/output), respectively. Mistral AI
A decision tree: Which one do I choose? Break the problem into two dimensions: task complexity × cost sensitivity.
Typical scenarios for Devstral 2 (Devstral 2512):
- Your task involves modifications across multiple files (interface linkage, dependencies, or high regression risk).
- You want the model to provide not only answers but also an engineering-level plan: scope control, test completion, and PR reviewability.
- You prioritize minimizing failures and are willing to accept higher token costs (higher official pricing). Mistral AI documentation +1
Typical scenarios for Devstral Small 2 (Labs Devstral Small 2512):
- Relatively simple requirements: a single file, low risk, or you can break the task down into smaller parts.
- Budget-sensitive, seeking lower-cost, frequent iterations (lower official pricing). Mistral AI documentation +1
- You are willing to use stronger cue constraints (e.g., "scout before modifying," "output only the smallest diff," and "test points must be listed") for stability.
30-Minute Reproducible Comparison: How to Test in Playground (My Template)
Fixed parameters (to ensure fairness)
- Temperature: [0.3]
- max_tokens: [2048]
- Response format: Text
- Same prompt (only change model)
Test Process
In Playground: Model: Devstral 2512 → Send the prompt → Screenshot output
Model: Labs Devstral Small 2512 → Send the same prompt → Screenshot output
Figure A: Actual output from Mistral AI Studio Playground (Model: Devstral 2512; temperature=0.3; max_tokens=2048; top_p=1; date: 2025-12-xx). The same prompt is used to observe "Plan Quality/Scope Control/Test Awareness/Reviewability".
Figure B: Using the same prompt and the same set of parameters, only switching the model to Labs Devstral Small 2512 (temperature=0.3; max_tokens=2048; top_p=1; date: 2025-12-xx), this was used to compare the output differences.
When I ran Devstral 2512 and Labs Devstral Small 2512 using the same prompt and the same set of parameters (temperature=0.3, max_tokens=2048, top_p=1), the "selection decision tree/comparison table/actual test process" outputs were highly similar.
Therefore, this article does not interpret this result as a "performance gap" but rather as a general selection template. I will retest it later with a more discriminative multi-file modification task.
Conclusion: How to choose the right model at a glance?
Full prompt used for testing:
You are an "Engineering Lead + Architect".
I want to choose between Devstral 2 and Devstral Small 2.
Please provide a "practical" selection recommendation without fabricating any benchmarks.
【My Background】
- I am a beginner, but I can use the console/Playground for testing; I can use Postman (optional).
- I want: a comparison table + a selection conclusion + a risk warning + reproduction steps.
【Task】
1) Explain in 8-12 lines why "code agent/multi-file project tasks" have higher requirements for the model (in layman's terms).
2) Provide a "selection decision tree": Under what circumstances should I choose Devstral 2? Under what circumstances should I choose Devstral Small 2?
3) Output a comparison table (fields should include at least: suitable task type, inference/quality tendency, cost sensitivity, suitability for local use, dependence on context length, and risks/precautions).
4) Provide a "30-minute field test plan" (using only the console Playground): how to run the same prompt twice and what metrics to use for comparison (e.g., plan quality, control over the scope of changes, testing awareness, and auditability).
5) Finally, output a "disclaimer/statement of truthfulness" that can be directly included in a blog, distinguishing among three categories: [facts], [test results], and [opinions].
[Strong Constraints]
- Do not fabricate any numerical benchmarks or conclusions based on "I've seen a certain review."
- If you cite facts such as "model positioning/context length/pricing," please prompt me to verify them on the official page and provide a list of "which fields I should verify" (do not write hard-coded numbers).
- The output should be suitable for direct screenshot use as test material: clear structure, bullet points, and tables.
Disclaimer / Statement of Truthfulness
[Facts]: Official documentation confirms both models have 256k context window and are positioned for code engineering tasks. Pricing information comes from official Mistral AI pricing page.
[Test Results]: Running identical prompts with both models using fixed parameters (temp=0.3, max_tokens=2048) produced highly similar outputs in my initial test. This similarity is expected given the models' similar architecture and training objectives.
[Opinions]: The selection framework (task complexity × cost sensitivity) is my personal recommendation based on practical engineering considerations. Actual performance may vary based on specific use cases and prompting strategies.