Mistral-Small-Instruct-2409
22B
Model parameter quantity
Mistral AI
Affiliated organization
Open Source
License Type
September 17, 2024
Release time
Related figures
Model Introduction
With 22 billion parameters, Mistral Small v24.09 offers customers a convenient mid-point between Mistral NeMo 12B and Mistral Large 2, providing a cost-effective solution that can be deployed across various platforms and environments.
Comprehensive score
Language dialogue
Knowledge reserve
Reasoning association
Mathematical calculation
Code writing
Command following
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Language comprehension ability
Often makes semantic misjudgments, leading to obvious logical disconnects in responses.
4.5
Knowledge coverage scope
Has significant knowledge blind spots, often showing factual errors and repeating outdated information.
5.5
Reasoning ability
Unable to maintain coherent reasoning chains, often causing inverted causality or miscalculations.
4.5
Model comparison
Mistral-Small-Instruct-2409 vs Qwen2.5-7B-Instruct
Like Qwen2, the Qwen2.5 language models support up to 128K tokens and can generate up to 8K tokens. They also maintain multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
Mistral-Small-Instruct-2409 vs GPT-4o-mini-20240718
GPT-4o-mini is an API model produced by OpenAI, with the specific version number being gpt-4o-mini-2024-07-18.
Mistral-Small-Instruct-2409 vs GPT-4o-mini-20240718
GPT-4o-mini is an API model produced by OpenAI, with the specific version number being gpt-4o-mini-2024-07-18.
Mistral-Small-Instruct-2409 vs Gemini-2.5-Pro-Preview-05-06
Gemini 2.5 Pro is a model released by Google DeepMind artificial intelligence research team, using version number Gemini-2.5-Pro-Preview-05-06.
Mistral-Small-Instruct-2409 vs DeepSeek-V2-Chat-0628
DeepSeek-V2 is a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token. Compared with DeepSeek 67B, DeepSeek-V2 achieves stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times.
Related model
Mistral-Large-Instruct-2411
Mistral-Large-Instruct-2411 is an advanced dense Large Language Model (LLM) of 123B parameters with state-of-the-art reasoning, knowledge and coding capabilities extending Mistral-Large-Instruct-2407 with better Long Context, Function Calling and System Prompt.
Mistral-Large-Instruct-2411
Mistral-Large-Instruct-2411 is an advanced dense Large Language Model (LLM) of 123B parameters with state-of-the-art reasoning, knowledge and coding capabilities extending Mistral-Large-Instruct-2407 with better Long Context, Function Calling and System Prompt.
Mistral-Small-Instruct-2409
With 22 billion parameters, Mistral Small v24.09 offers customers a convenient mid-point between Mistral NeMo 12B and Mistral Large 2, providing a cost-effective solution that can be deployed across various platforms and environments.
Ministral-8B-Instruct-2410
The Ministral-8B-Instruct-2410 Language Model is an instruct fine-tuned model significantly outperforming existing models of similar size, released under the Mistral Research License.
Mixtral-8x22B-Instruct-v0.1
Mixtral 8x22B is a sparse Mixture-of-Experts (SMoE) model that uses only 39B active parameters out of 141B, offering unparalleled cost efficiency for its size.
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