CohereNorth Small Translate 1.0
North Small Translate is an open-weights research release by Cohere and Cohere Labs, specialized for machine translation across 50 languages. It is a sparse Mixture-of-Experts model with 25 billion active parameters and 218 billion total parameters.
Input
Output
Context
33K
Max Output
16K
Parameters
218.3B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Resources & Links
Estimated GPU requirements for North Small Translate 1.0
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 North Small Translate 1.0
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
North Small Translate 1.0 advertises a context window of 32,768 tokens, with a maximum output of 16,384 tokens in a single response. The figure is the creator's published maximum; a given provider may serve less, and the gateway routes on what each provider actually serves.
Yes. North Small Translate 1.0 is an open-weight model released under the Cc by nc 4.0 license, so the weights can be downloaded and self-hosted within that license's terms.
North Small Translate 1.0 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 North Small Translate 1.0 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; North Small Translate 1.0 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 214.9 GB at INT8 for the weights and a default context, from the catalog's 218.3B 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, provider APIs and benchmark publishers, each tagged with its source.
Last updated: Nov 4, 2026
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