Context Window

Compare

Llama4 Maverick vs Qwen2 5 Coder 7b

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

Llama4 Maverick

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Alibaba

Model

Qwen2 5 Coder 7b

Context window

33K

32,768 tokens · ~25K 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.

Llama4 Maverick128K
Qwen2 5 Coder 7b33K

Llama4 Maverick has about 3.9× the context window of the other in this pair.

Token calculator →Memory savings →

Llama4 Maverick has 290% more context capacity (128K vs 32K tokens). Qwen2 5 Coder 7b is 95% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Llama4 Maverick. Its 128K context fits entire documents without chunking (vs 32K).

  • RAG / high-volume retrieval

    Use Qwen2 5 Coder 7b. Input tokens are 95% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecLlama4 MaverickQwen2 5 Coder 7b
Context window128,000 tokens (128K)32,768 tokens (32K)
Max output tokens16,384 tokens (16K)N/A
Speed tierBalancedFast
VisionNoNo
Function callingYesNo
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.

ProviderLlama4 Maverick inLlama4 Maverick outQwen2 5 Coder 7b inQwen2 5 Coder 7b out
Nebius——$0.010/M$0.030/M
Nscale——$0.010/M$0.030/M
Snowflake$0.240/M$0.970/M——

Frequently asked questions

Llama4 Maverick has a larger context window: 128K tokens vs 32K. 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.

Calculate your savingsLearn about Mem0

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