Context Window

Compare

Codellama 34b Instruct vs Llama 4 Maverick 17b 128e Instruct Maas

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
Meta

Model

Llama 4 Maverick 17b 128e Instruct Maas

Tool calling

Context window

1M

1,000,000 tokens · ~750K 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
Llama 4 Maverick 17b 128e Instruct Maas1M

Llama 4 Maverick 17b 128e Instruct Maas has about 244.1× the context window of the other in this pair.

Token calculator →Memory savings →

Llama 4 Maverick 17b 128e Instruct Maas has 24314% more context capacity (1000K vs 4K tokens). Llama 4 Maverick 17b 128e Instruct Maas is 65% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Llama 4 Maverick 17b 128e Instruct Maas. Its 1000K context fits entire documents without chunking (vs 4K).

  • RAG / high-volume retrieval

    Use Llama 4 Maverick 17b 128e Instruct Maas. Input tokens are 65% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Llama 4 Maverick 17b 128e Instruct Maas. Its 1000K 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 InstructLlama 4 Maverick 17b 128e Instruct Maas
Context window4,096 tokens (4K)1,000,000 tokens (1000K)
Max output tokens4,096 tokens (4K)1,000,000 tokens (1000K)
Speed tierBalancedFast
VisionNoNo
Function callingNoYes
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 outLlama 4 Maverick 17b 128e Instruct Maas inLlama 4 Maverick 17b 128e Instruct Maas out
Anyscale$1.00/M$1.00/M——
Google Vertex——$0.350/M$1.15/M

Frequently asked questions

Llama 4 Maverick 17b 128e Instruct Maas has a larger context window: 1000K 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