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Mistral Large (Feb '24)
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Mistral Large (Feb '24)

February 2024 release of Mistral AI's large model, representing company's early achievements in large-scale language models. Features powerful reasoning capabilities and multilingual support with excellent complex task processing performance. This version established Mistral's position in high-performance AI model field, making important contributions to European AI technology development, suitable for professional applications requiring high-performance AI capabilities.
Intelligence(Relatively Weak)
Speed(Slow)
Input Supported Modalities
Yes
Is Reasoning Model
32,768
Context Window
-
Maximum Output Tokens
-
Knowledge Cutoff
Go Compare

Pricing

- /M tokens
Input
- /M tokens
Output
￥43.2 /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 Large (Feb '24)Technical Parameters
Parameter Count
Not Announced
Context Length
32.77k tokens
Training Data Cutoff
Open Source Category
Proprietary
Multimodal Support
Text Only
Throughput
Release Date
2024-02-26
Response Speed
0 tokens/s

Benchmark Scores

Below is the performance of Mistral Large (Feb '24) in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
26.41
Large Language Model Intelligence Level
Coding Index
19.31
Indicator of AI model performance on coding tasks
Math Index
26.33
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
51.5
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
35.1
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
3.4
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
17.8
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
20.8
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
70.6
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
52.7
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)
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