Mistral AI

Mistral AI: Mistral Saba

mistralai/mistral-saba

Access Mistral Saba 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/mistral-saba"
}).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/mistral-saba"
        }).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/mistral-saba",
    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/mistral-saba",
    "messages": [
      {"role": "user", "content": "Explain quantum computing in simple terms"}
    ]
  }'

Model Card

Mistral Saba is a 24B parameter regional model trained for Arabic and South Asian languages including Tamil and Malayalam. It outperforms models 5x its size on Arabic benchmarks while providing culturally relevant responses.

Context Window 33K

tokens

Max Output N/A

tokens

Input Cost $0.2

per million tokens

Output Cost $0.6

per million tokens

Release Date Feb 17, 2025

 

Model Playground

Try Mistral Saba instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.

Chat mistralai/mistral-saba
Mistral AI
Chat with Mistral Saba
Powered by Puter.js

Benchmarks

How Mistral Saba performs on standard evaluations.

Artificial Analysis
Intelligence Index
12.1
Better than 21% of tracked models
BenchmarkScore
GPQA Diamond Graduate-level science Q&A
42.4%
Humanity's Last Exam Cross-domain reasoning
4.1%
SciCode Scientific programming
24.1%
MATH-500 Competition math
67.7%
AIME 2024 Advanced math exam
13.0%

Scores sourced from Artificial Analysis.

Find other Mistral AI models →

Chat

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.

 Chat

Mistral 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.

 Chat

Ministral 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

How do I use Mistral Saba?

You can access Mistral Saba 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.

Is Mistral Saba free?

Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add Mistral Saba to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.

What is the pricing for Mistral Saba?
Mistral Saba costs $0.2 per 1M input tokens and $0.6 per 1M output tokens.
Price per 1M tokens
Input$0.2
Output$0.6
Who created Mistral Saba?

Mistral Saba was created by Mistral AI and released on Feb 17, 2025.

What is the context window of Mistral Saba?

Mistral Saba supports a context window of 33K tokens. For reference, that is roughly equivalent to 66 pages of text.

How does Mistral Saba perform on benchmarks?

Mistral Saba scores 12.1 on the Artificial Analysis Intelligence Index, outperforming 21% of tracked models.

Does it work with React / Vue / Vanilla JS / Node / etc.?

Yes — the Mistral Saba 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 Mistral Saba to your app without worrying about API keys or setup.

Read the Docs View Tutorials