Context Window

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Codellama 34b Instruct vs Qwen3 8b Fp8

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
Alibaba

Model

Qwen3 8b Fp8

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
Qwen3 8b Fp8128K

Qwen3 8b Fp8 has about 31.3× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen3 8b Fp8 has 3025% more context capacity (128K vs 4K tokens). Qwen3 8b Fp8 is 96% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen3 8b Fp8. Its 128K context fits entire documents without chunking (vs 4K).

  • RAG / high-volume retrieval

    Use Qwen3 8b Fp8. Input tokens are 96% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Qwen3 8b Fp8. Its 20K 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 InstructQwen3 8b Fp8
Context window4,096 tokens (4K)128,000 tokens (128K)
Max output tokens4,096 tokens (4K)20,000 tokens (20K)
Speed tierBalancedFast
VisionNoNo
Function callingNoNo
Extended thinkingNoYes
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 outQwen3 8b Fp8 inQwen3 8b Fp8 out
Anyscale$1.00/M$1.00/M——
Novita——$0.035/M$0.138/M

Frequently asked questions

Qwen3 8b Fp8 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.

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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