Mistral Medium 3.5 128B
Mistral Medium 3.5 128B is a dense 128B model developed by Mistral, exceling at handling instruction-following, reasoning, and coding tasks in a unified set of weights. Its notable technical trait is a large context window of 262,144 tokens, allowing it to process extensive inputs.
Input
Output
Context
262K
Max Output
-
Parameters
127.7B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Estimated GPU requirements for Mistral Medium 3.5 128B
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 Medium 3.5 128B
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Mistral Medium 3.5 128B advertises a context window of 262,144 tokens. 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 Medium 3.5 128B is an open-weight model released under the Other license, so the weights can be downloaded and self-hosted within that license's terms.
Mistral Medium 3.5 128B accepts Text and Image 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 Medium 3.5 128B 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 Medium 3.5 128B 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 123.9 GB at INT8 for the weights and a default context, from the catalog's 127.7B 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: Aug 28, 2026
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