Mistral 7B Instruct V0.3
Mistral 7B Instruct V 0.3 is a chat-focused large language model developed by the Mistral AI team, fine-tuned for instruct following and function calling. It is particularly suited for handling complex conversations and generating text based on user input.
Input
Output
Context
33K
Max Output
0K
Parameters
7.2B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Estimated GPU requirements for Mistral 7B Instruct V0.3
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
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Frequently asked questions about Mistral 7B Instruct V0.3
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Mistral 7B Instruct V0.3 advertises a context window of 32,768 tokens, with a maximum output of 256 tokens in a single response. The figure is the creator's published maximum; a given host may serve less, and the gateway routes on what each host actually serves.
Yes. Mistral 7B Instruct V0.3 is an open-weight model released under the Apache 2.0 license, so the weights can be downloaded and self-hosted within that license's terms.
Mistral 7B Instruct V0.3 accepts Text and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
No published benchmark result for Mistral 7B Instruct V0.3 is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Not on the managed pool today; Mistral 7B Instruct V0.3 is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 7.4 GB at INT8 for the weights and a default context, from the catalog's 7.2B parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, host APIs and benchmark publishers, each tagged with its source.
Last updated: Sep 13, 2026
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