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Codestral (Jan '25)
A code generation model with 22B parameters, trained on over 80 programming languages, including Python, Java, C, C++, JavaScript, and Bash. It supports instruction following and Fill-in-the-Middle (FIM) capabilities, and can be used for code completion and generation tasks.
Intelligence(Relatively Weak)
Speed(Relatively Fast)
Input Supported Modalities
No
Is Reasoning Model
256,000
Context Window
32,768
Maximum Output Tokens
-
Knowledge Cutoff
Pricing
￥1.44 /M tokens
Input
￥4.32 /M tokens
Output
￥3.24 /M tokens
Blended Price
Quick Simple Comparison
Devstral
￥0.1
Devstral Medium
￥0.4
Devstral Small (May '25)
￥0.1
Basic Parameters
Codestral (Jan '25)Technical Parameters
Parameter Count
22,200.0M
Context Length
256.00k tokens
Training Data Cutoff
Open Source Category
Proprietary
Multimodal Support
Text Only
Throughput
42
Release Date
2025-01-13
Response Speed
178.21,214 tokens/s
Benchmark Scores
Below is the performance of Codestral (Jan '25) in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
27.64
Large Language Model Intelligence Level
Coding Index
24.5
Indicator of AI model performance on coding tasks
Math Index
32.5
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
44.6
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
31.2
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
4.5
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
24.3
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
24.7
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
84.8
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
60.7
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
4.3
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