Context Window

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Claude 4 Opus vs Codellama 34b

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.

Anthropic

Model

Claude 4 Opus

Tool calling

Context window

200K

200,000 tokens · ~150K words

Model page
Meta

Model

Codellama 34b

Context window

16K

16,384 tokens · ~12K 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.

Claude 4 Opus200K
Codellama 34b16K

Claude 4 Opus has about 12.2× the context window of the other in this pair.

Token calculator →Memory savings →

Claude 4 Opus has 1120% more context capacity (200K vs 16K tokens). Codellama 34b is 97% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Claude 4 Opus. Its 200K context fits entire documents without chunking (vs 16K).

  • RAG / high-volume retrieval

    Use Codellama 34b. Input tokens are 97% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Claude 4 Opus. Its 200K 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.

SpecClaude 4 OpusCodellama 34b
Context window200,000 tokens (200K)16,384 tokens (16K)
Max output tokens200,000 tokens (200K)16,384 tokens (16K)
Speed tierDeepBalanced
VisionNoNo
Function callingYesNo
Extended thinkingNoNo
Prompt cachingNoNo
Batch APIYesNo
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.

ProviderClaude 4 Opus inClaude 4 Opus outCodellama 34b inCodellama 34b out
Deepinfra$16.50/M$82.50/M——
Perplexity——$0.350/M$1.40/M
Together Ai————

Frequently asked questions

Claude 4 Opus has a larger context window: 200K tokens vs 16K. 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