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Chronos Hermes 13b vs Codellama 34b 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.

Nous Research

Model

Chronos Hermes 13b

Context window

4K

4,096 tokens · ~3K words

Model page
Meta

Model

Codellama 34b Instruct

Context window

4K

4,096 tokens · ~3K 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.

Chronos Hermes 13b4K
Codellama 34b Instruct4K

Same context window size for both models.

Chronos Hermes 13b and Codellama 34b Instruct have identical context windows (4K tokens). Chronos Hermes 13b is 80% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Chronos Hermes 13b. Input tokens are 80% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecChronos Hermes 13bCodellama 34b Instruct
Context window4,096 tokens (4K)4,096 tokens (4K)
Max output tokens4,096 tokens (4K)4,096 tokens (4K)
Speed tierFastBalanced
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.

ProviderChronos Hermes 13b inChronos Hermes 13b outCodellama 34b Instruct inCodellama 34b Instruct out
Anyscale$1.00/M$1.00/M
Fireworks$0.200/M$0.200/M

Frequently asked questions

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

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