Context Window

Compare

Codellama 34b Instruct vs Qwen3.5-122B-A10B

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

Qwen3.5-122B-A10B

Image inputTool calling

Context window

262K

262,144 tokens · ~197K 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
Qwen3.5-122B-A10B262K

Qwen3.5-122B-A10B has about 64× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen3.5-122B-A10B has 6300% more context capacity (262K vs 4K tokens). Qwen3.5-122B-A10B is 60% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen3.5-122B-A10B. Its 262K context fits entire documents without chunking (vs 4K).

  • RAG / high-volume retrieval

    Use Qwen3.5-122B-A10B. Input tokens are 60% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Qwen3.5-122B-A10B. Its 65K 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 InstructQwen3.5-122B-A10B
Context window4,096 tokens (4K)262,144 tokens (262K)
Max output tokens4,096 tokens (4K)65,536 tokens (65K)
Speed tierBalancedBalanced
VisionNoYes
Function callingNoYes
Extended thinkingNoYes
Prompt cachingNoNo
Batch APINoNo
Release dateN/AFeb 2026

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 outQwen3.5-122B-A10B inQwen3.5-122B-A10B out
Anyscale$1.00/M$1.00/M——
Openrouter——$0.400/M$2.00/M

Frequently asked questions

Qwen3.5-122B-A10B has a larger context window: 262K 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.

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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