Context Window

Compare

Codellama 34b Instruct vs Command R (08-2024)

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.

Meta

Model

Codellama 34b Instruct

Context window

4K

4,096 tokens · ~3K words

Model page
Cohere

Model

Command R (08-2024)

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.

Codellama 34b Instruct4K
Command R (08-2024)128K

Command R (08-2024) has about 31.3× the context window of the other in this pair.

Token calculator →Memory savings →

Command R (08-2024) has 3025% more context capacity (128K vs 4K tokens). Command R (08-2024) is 85% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Command R (08-2024). Its 128K context fits entire documents without chunking (vs 4K).

  • RAG / high-volume retrieval

    Use Command R (08-2024). Input tokens are 85% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecCodellama 34b InstructCommand R (08-2024)
Context window4,096 tokens (4K)128,000 tokens (128K)
Max output tokens4,096 tokens (4K)4,096 tokens (4K)
Speed tierBalancedBalanced
VisionNoNo
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateN/AAug 2024

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.

ProviderCodellama 34b Instruct inCodellama 34b Instruct outCommand R (08-2024) inCommand R (08-2024) out
Anyscale$1.00/M$1.00/M——
Cohere——$0.150/M$0.600/M

Frequently asked questions

Command R (08-2024) has a larger context window: 128K tokens vs 4K. 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