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Model details

Mistral Small 3.1

Mistral Small 3.1 extends its predecessor's text capabilities by adding robust multimodal understanding, enabling the model to reason about images alongside written content. Its design emphasizes versatility across programming, mathematical reasoning, document comprehension, and conversational tasks, targeting developers who need strong AI assistance without the resource demands of larger systems. The expanded context window allows it to process and maintain coherence across lengthy documents and extended exchanges.

The model demonstrates competitive benchmark performance against similarly-sized offerings, matching or surpassing results on evaluations like MMLU, GPQA, and HumanEval according to Mistral's testing. Its permissive Apache 2.0 licensing differentiates it from many commercial alternatives, making it attractive for teams building production applications with flexibility for commercial deployment. Inference speeds around 150 tokens per second make it practical for real-time use cases, positioning it as a capable foundation for a wide range of AI-powered workflows.

Azure Cognitive Servicesmistral-small-2503mistral-small

Quick Info

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Provider
Azure Cognitive Services
Model key
mistral-small-2503
Release date
Mar 1, 2025
Last updated
Mar 1, 2025
Knowledge cutoff
2024-09
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.10
Output token cost
$0.30

Limits

Output tokens
32,768 tokens
Context window
128,000 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Latest news about Mistral Small 3.1

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Coverage

The Hugging Face model card for Mistral-Small-3.1-24B-Instruct-2503 confirms that Mistral Small 3.1 (2503) builds on Mistral Small 3 (2501) by adding state-of-the-art vision understanding and extending long-context capabilities to 128k tokens while preserving text performance. The 24-billion-parameter model is position The card lists an Apache 2.0 license, a 128k context window, a Tekken tokenizer with 131k vocabulary size, and support for dozens of languages. It also notes that the model can run locally on a single RTX 4090 or a 32GB RAM MacBook once quantized, making it suitable for local inference and fine-tuning in sensitive-data

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