Mistral AI: Devstral 2
mistralai/devstral-2512
Access Devstral 2 from Mistral AI using Puter.js AI API.
Get Started// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "mistralai/devstral-2512"
}).then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms", {
model: "mistralai/devstral-2512"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
# pip install openai
from openai import OpenAI
client = OpenAI(
base_url="https://api.puter.com/puterai/openai/v1/",
api_key="YOUR_PUTER_AUTH_TOKEN",
)
response = client.chat.completions.create(
model="mistralai/devstral-2512",
messages=[
{"role": "user", "content": "Explain quantum computing in simple terms"}
],
)
print(response.choices[0].message.content)
curl https://api.puter.com/puterai/openai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_PUTER_AUTH_TOKEN" \
-d '{
"model": "mistralai/devstral-2512",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
Devstral 2 is a 123B parameter dense transformer coding model achieving 72.2% on SWE-bench Verified with 256K context. Released under modified MIT license, it's the state-of-the-art open model for code agents, 7x more cost-efficient than Claude Sonnet.
Context Window 262K
tokens
Max Output 262K
tokens
Input Cost $0.4
per million tokens
Output Cost $2
per million tokens
Input text
modalities
Tool Use Yes
Knowledge Cutoff Dec 2025
Release Date Dec 9, 2025
Output Speed 47
tokens / sec
Latency 0.57s
time to first token
Model Playground
Try Devstral 2 instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How Devstral 2 performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 59.4% |
| Humanity's Last Exam Cross-domain reasoning | 3.6% |
| LiveCodeBench Recent coding problems | 44.8% |
| SciCode Scientific programming | 33.1% |
| AIME 2025 Advanced math exam | 36.7% |
| IFBench Instruction following | 38.1% |
| LCR Long-context reasoning | 30.0% |
| Terminal-Bench Hard Agentic terminal tasks | 18.9% |
| τ²-Bench Tool use / agents | 24.9% |
Scores sourced from Artificial Analysis.
Find other Mistral AI models →
Mistral Medium 3.5
Mistral Medium 3.5 is a dense 128-billion-parameter multimodal model from Mistral AI that unifies instruction-following, reasoning, and coding into a single set of weights. It features a 256k-token context window, native function calling, structured JSON output, and vision capabilities via a custom-trained encoder that handles variable image sizes. A per-request reasoning_effort parameter lets you toggle between fast responses and deeper chain-of-thought processing, making the same model suitable for quick chat replies and complex agentic workflows. On benchmarks, it scores 77.6% on SWE-Bench Verified and 91.4% on τ³-Telecom. It replaces Mistral's previous Medium 3.1, Magistral, and Devstral 2 models. Priced at $1.50 per million input tokens and $7.50 per million output tokens, it's a strong fit for developers building tool-calling agents, long-horizon coding tasks, and multi-step automation pipelines.
ChatMistral Small 4
Mistral Small 4 is a 119B-parameter open-source Mixture-of-Experts model (6B active per token) released under Apache 2.0, unifying instruction-following, reasoning, multimodal (text + image), and agentic coding into a single deployment. It features 128 experts, a 256k context window, and configurable reasoning effort that lets developers toggle between fast responses and deep step-by-step reasoning per request. Compared to its predecessor Mistral Small 3, it delivers 40% lower latency and 3x higher throughput while matching or surpassing GPT-OSS 120B on key benchmarks.
ChatMinistral 14B
Ministral 14B is part of the Ministral 3 family, a 14B parameter multimodal model with vision capabilities under Apache 2.0. It offers advanced capabilities for local deployment with instruct, base, and reasoning variants achieving 85% on AIME'25.
Frequently Asked Questions
You can access Devstral 2 by Mistral AI through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add Devstral 2 to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.
| Price per 1M tokens | |
|---|---|
| Input | $0.4 |
| Output | $2 |
Devstral 2 was created by Mistral AI and released on Dec 9, 2025.
Devstral 2 supports a context window of 262K tokens. For reference, that is roughly equivalent to 524 pages of text.
Devstral 2 can generate up to 262K tokens in a single response.
Devstral 2 has a knowledge cutoff date of Dec 2025. This means the model was trained on data available up to that date.
Devstral 2 accepts the following input types: text. It produces: text.
Yes, Devstral 2 supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
Devstral 2 scores 22.0 on the Artificial Analysis Intelligence Index, outperforming 54% of tracked models. On coding, it scores 23.7 (outperforms 57% of models). On math, it scores 36.7 (outperforms 36% of models).
Yes — the Devstral 2 API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
Get started with Puter.js
Add Devstral 2 to your app without worrying about API keys or setup.
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