Cohere: North Mini Code (free)
North Mini Code (free) is a reasoning model from Cohere released on Jun 17, 2026. It reads text and writes text, with a 256K token context window and up to 64K tokens per reply.
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It costs $0 per million input tokens and $0 per million output tokens.
It supports tool calling, and its weights are open, so you can host it yourself.
- Modalities
- In / out price
- $0 / $0 per 1M
- Context
- 256K
- Max output
- 64K
- Released
- Jun 17, 2026
- Knowledge cutoff
- n/a
- Tool calling
- Structured output
- Reasoning
- Prompt caching
- Open weights
- Free to use
North Mini Code (free) 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 Mini Code (free) 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 Mini Code (free) through an OpenAI compatible API.
- Tools
tools - Reasoning
reasoning - Temperature
temperature
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North Mini Code (free) questions
How much does the North Mini Code (free) 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 Mini Code (free)?
256,000 tokens of input, and it can write up to 64,000 tokens in one reply. As a rough guide, a million tokens is around 750,000 English words.
Does North Mini Code (free) support tool calling?
Yes. It accepts tool definitions, which is what an agent needs to call your APIs.
Is North Mini Code (free) 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 Mini Code (free) released?
Jun 17, 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 Mini Code (free)?
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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