CohereNorth Mini Code 1.0 Eagle
North Mini Code 1.0 Eagle is a code generation model built by Cohere. It is designed for speculative decoding, serving as a draft model compatible with fp8/w4a16 target model checkpoints.
Input
Output
Context
5M
Max Output
64K
Parameters
79.2M
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Resources & Links
Estimated GPU requirements for North Mini Code 1.0 Eagle
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 Mini Code 1.0 Eagle
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
North Mini Code 1.0 Eagle advertises a context window of 5,000,000 tokens, with a maximum output of 64,000 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 Mini Code 1.0 Eagle 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.
North Mini Code 1.0 Eagle 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 Mini Code 1.0 Eagle 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 Mini Code 1.0 Eagle 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 0.5 GB at INT8 for the weights and a default context, from the catalog's 79.2M 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: Oct 14, 2026
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