Compare

Command A 03 2025 vs North Mini Code (free)

This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.

Cohere

Model

Command A 03 2025

Tool calling

Context window

256K

256,000 tokens · ~192K words

Model page
Cohere

Model

North Mini Code (free)

Tool calling

Context window

256K

256,000 tokens · ~192K words

Model page

Context window · side by side

Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.

Command A 03 2025256K
North Mini Code (free)256K

Same context window size for both models.

Command A 03 2025 and North Mini Code (free) have identical context windows (256K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long output (reports, code files)

    Use North Mini Code (free). Its 64K max output lets you generate complete artifacts in one request.

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecCommand A 03 2025North Mini Code (free)
Context window256,000 tokens (256K)256,000 tokens (256K)
Max output tokens8,000 tokens (8K)64,000 tokens (64K)
Speed tierBalancedFast
VisionNoNo
Function callingYesYes
Extended thinkingNoYes
Prompt cachingNoNo
Batch APINoNo
Release dateN/AJun 2026

Pricing matrix

Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.

ProviderCommand A 03 2025 inCommand A 03 2025 outNorth Mini Code (free) inNorth Mini Code (free) out
Cohere$2.50/M$10.00/M

Frequently asked questions

North Mini Code (free) has a larger context window: 256K tokens vs 256K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

Powered by Mem0

Use a smaller model.
Get better results.

Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.

Example: a multi-turn chat session

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
With Mem0~20K tokens sent
Key memories
Current turn

80% less to send — works with any model