Context Window

Compare

Command R+ (08-2024) vs Meta Llama4 Maverick 17b Instruct

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 R+ (08-2024)

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Meta

Model

Meta Llama4 Maverick 17b Instruct

Tool calling

Context window

128K

128,000 tokens · ~96K 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 R+ (08-2024)128K
Meta Llama4 Maverick 17b Instruct128K

Same context window size for both models.

Token calculator →Memory savings →

Command R+ (08-2024) and Meta Llama4 Maverick 17b Instruct have identical context windows (128K tokens). Meta Llama4 Maverick 17b Instruct is 90% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Meta Llama4 Maverick 17b Instruct. Input tokens are 90% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecCommand R+ (08-2024)Meta Llama4 Maverick 17b Instruct
Context window128,000 tokens (128K)128,000 tokens (128K)
Max output tokens4,096 tokens (4K)4,096 tokens (4K)
Speed tierBalancedFast
VisionNoNo
Function callingYesYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateAug 2024N/A

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 R+ (08-2024) inCommand R+ (08-2024) outMeta Llama4 Maverick 17b Instruct inMeta Llama4 Maverick 17b Instruct out
Aws Bedrock——$0.240/M$0.970/M
Cohere$2.50/M$10.00/M——

Frequently asked questions

Meta Llama4 Maverick 17b Instruct has a larger context window: 128K tokens vs 128K. 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.

Calculate your savingsLearn about Mem0

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