Cohere: North Small Translate
North Small Translate is a language model from Cohere released on Sep 9, 2026. It reads text and writes text, with a 33K token context window and up to 16K tokens per reply.
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It costs $0 per million input tokens and $0 per million output tokens.
Its weights are open, so you can host it yourself.
- Modalities
- In / out price
- $0 / $0 per 1M
- Context
- 33K
- Max output
- 16K
- Released
- Sep 9, 2026
- Knowledge cutoff
- n/a
- Tool calling
- Structured output
- Reasoning
- Prompt caching
- Open weights
- Free to use
North Small Translate pricing
USD, as listed on Oct 15, 2026. Most bills are input heavy, so the cached input rate matters as much as the headline price.
| Input | $0 / 1M tokens | |
|---|---|---|
| Output | $0 / 1M tokens |
What North Small Translate costs per month
Pick a workload close to yours or type your own volumes.
Supported request parameters
What you can set when you call North Small Translate through an OpenAI compatible API.
- Temperature
temperature
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North Small Translate questions
How much does the North Small Translate API cost?
$0 per million input tokens and $0 per million output tokens. The estimator on this page turns that into a monthly bill for your own workload.
What is the context window of North Small Translate?
32,768 tokens of input, and it can write up to 16,000 tokens in one reply. As a rough guide, a million tokens is around 750,000 English words.
Does North Small Translate support tool calling?
Not through the standard tools parameter. For an agent that calls your APIs, pick a model from the alternatives below that does.
Is North Small Translate open source?
Its weights are published, so you can run it on your own hardware or a cloud of your choice. Read the license before commercial use.
When was North Small Translate released?
Sep 9, 2026.
Catalog data from models.dev (MIT licensed), refreshed daily, last on Oct 15, 2026. The summary is written from these facts.
Building on North Small Translate?
I test your real tasks on it and the two closest alternatives, then ship the agent on whichever wins on cost and accuracy.
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