Compare

Codellama 34b Instruct vs Granite Ttm 512 96 R2

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
Ibm

Model

Granite Ttm 512 96 R2

Context window

1K

512 tokens · ~384 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
Granite Ttm 512 96 R21K

Codellama 34b Instruct has about 8× the context window of the other in this pair.

Codellama 34b Instruct has 700% more context capacity (4K vs 0K tokens). Granite Ttm 512 96 R2 is 62% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Codellama 34b Instruct. Its 4K context fits entire documents without chunking (vs 0K).

  • RAG / high-volume retrieval

    Use Granite Ttm 512 96 R2. Input tokens are 62% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Codellama 34b Instruct. Its 4K 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.

SpecCodellama 34b InstructGranite Ttm 512 96 R2
Context window4,096 tokens (4K)512 tokens (0K)
Max output tokens4,096 tokens (4K)512 tokens (0K)
Speed tierBalancedBalanced
VisionNoNo
Function callingNoNo
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateN/AN/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.

ProviderCodellama 34b Instruct inCodellama 34b Instruct outGranite Ttm 512 96 R2 inGranite Ttm 512 96 R2 out
Anyscale$1.00/M$1.00/M
Ibm Watsonx$0.380/M$0.380/M

Frequently asked questions

Codellama 34b Instruct has a larger context window: 4K tokens vs 0K. 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