Mistral Small 3.1

The pre-trained base model version is Mistral Small 3.1. Compared with Mistral Small 3, it has improved text performance, multimodal understanding ability, multilingual support, and the context window is extended to 128k tokens. It is designed for fine-tuning.

Intelligence(Relatively Weak)

Speed(Medium)

Input Supported Modalities

Yes

Is Reasoning Model

128,000

Context Window

128,000

Maximum Output Tokens

Knowledge Cutoff

Go Compare

Pricing

￥0.72 /M tokens

Input

￥2.16 /M tokens

Output

￥1.08 /M tokens

Blended Price

Quick Simple Comparison

Input

Output

Devstral

￥0.1

Devstral Medium

￥0.4

Devstral Small (May '25)

￥0.1

Basic Parameters

Mistral Small 3.1Technical Parameters

Parameter Count

24,000.0M

Context Length

128.00k tokens

Training Data Cutoff

Open Source Category

Open Weights (Permissive License)

Multimodal Support

Text, Image

Throughput

137

Release Date

2025-03-17

Response Speed

148.93,848 tokens/s

Benchmark Scores

Below is the performance of Mistral Small 3.1 in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.

Intelligence Index

Large Language Model Intelligence Level

35.3

Large Language Model Intelligence Level

Coding Index

Indicator of AI model performance on coding tasks

23.83

Indicator of AI model performance on coding tasks

Math Index

Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks

MMLU Pro

Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video

65.9

Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video

GPQA

Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions

45.4

Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions

HLE

The model's comprehensive average score on the Hugging Face Open LLM Leaderboard

4.8

The model's comprehensive average score on the Hugging Face Open LLM Leaderboard

LiveCodeBench

Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems

21.2

Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems

SciCode

The model's capability in code generation for scientific computing or specific scientific domains

26.5

The model's capability in code generation for scientific computing or specific scientific domains

HumanEval

Score achieved by the AI model on the specific HumanEval benchmark test set

85.9

Score achieved by the AI model on the specific HumanEval benchmark test set

Math 500 Score

Score on the first 500 larger, more well-known mathematical benchmark tests

70.7

Score on the first 500 larger, more well-known mathematical benchmark tests

AIME Score

An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)

9.3

An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)

Output tokens/million

Output tokens/million

Output tokens/million

Output tokens/million

Output tokens/million

131k

Context Length

Gemini 2.0 Flash Lite (Preview)

Output tokens/million

Output tokens/million

33k

Context Length

Qwen2.5 Coder Instruct 32B

Output tokens/million

131k

Context Length

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