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Ministral 8B
Ministral-8B-Instruct-2410 is a model fine-tuned for instructions in local intelligence, device-side computing, and edge applications. Its performance significantly surpasses existing models of the same scale.
Intelligence(Relatively Weak)
Speed(Fast)
Input Supported Modalities
No
Is Reasoning Model
128,000
Context Window
128,000
Maximum Output Tokens
-
Knowledge Cutoff
Pricing
￥0.72 /M tokens
Input
￥0.72 /M tokens
Output
￥0.72 /M tokens
Blended Price
Quick Simple Comparison
Devstral
￥0.1
Devstral Medium
￥0.4
Devstral Small (May '25)
￥0.1
Basic Parameters
Ministral 8BTechnical Parameters
Parameter Count
8,019.8M
Context Length
128.00k tokens
Training Data Cutoff
Open Source Category
Open Weights (License Required for Commercial Use)
Multimodal Support
Text Only
Throughput
0
Release Date
2024-10-16
Response Speed
206.71,396 tokens/s
Benchmark Scores
Below is the performance of Ministral 8B in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
22.33
Large Language Model Intelligence Level
Coding Index
11.34
Indicator of AI model performance on coding tasks
Math Index
30.37
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
38.9
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
27.6
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
4.9
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
11.2
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
11.5
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
76.7
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
57.1
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
3.7
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
GPT 5 Mini
openai
￥1.8
Input tokens/million
￥14.4
Output tokens/million
400k
Context Length
GPT 5 Standard
openai
￥63
Input tokens/million
￥504
Output tokens/million
400k
Context Length
GPT 5 Nano
openai
￥0.36
Input tokens/million
￥2.88
Output tokens/million
400k
Context Length
GPT 5
openai
￥9
Input tokens/million
￥72
Output tokens/million
400k
Context Length
GLM 4.5
chatglm
￥0.43
Input tokens/million
￥1.01
Output tokens/million
131k
Context Length
Gemini 2.0 Flash Lite (Preview)
google
￥0.58
Input tokens/million
￥2.16
Output tokens/million
1M
Context Length
Gemini 1.0 Pro
google
￥3.6
Input tokens/million
￥10.8
Output tokens/million
33k
Context Length
Qwen2.5 Coder Instruct 32B
alibaba
￥0.65
Input tokens/million
￥0.65
Output tokens/million
131k
Context Length