Mixtral-8x22B-Instruct-v0.1
141B
Model parameter quantity
Mistral AI
Affiliated organization
Open Source
License Type
April 17, 2024
Release time
Model Introduction
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.
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.
4.1
Knowledge coverage scope
Has a basic encyclopedic knowledge base, but lacks depth and suffers from outdated information.
6.0
Reasoning ability
Unable to maintain coherent reasoning chains, often causing inverted causality or miscalculations.
3.0
Model comparison
Mixtral-8x22B-Instruct-v0.1 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.
Mixtral-8x22B-Instruct-v0.1 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.
Mixtral-8x22B-Instruct-v0.1 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.
Mixtral-8x22B-Instruct-v0.1 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.
Mixtral-8x22B-Instruct-v0.1 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.
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.
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