Compare
Llama4 Maverick vs Qwen2.5 Coder 7B Instruct
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.
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 Maverick has about 3.9× the context window of the other in this pair.
Llama4 Maverick has 290% more context capacity (128K vs 32K tokens).
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).
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Llama4 Maverick | Qwen2.5 Coder 7B Instruct |
|---|---|---|
| Context window | 128,000 tokens (128K) | 32,768 tokens (32K) |
| Max output tokens | 16,384 tokens (16K) | N/A |
| Speed tier | Balanced | Fast |
| Vision | No | No |
| Function calling | Yes | No |
| Extended thinking | No | No |
| Prompt caching | No | No |
| Batch API | No | No |
| Release date | N/A | Apr 2025 |
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.
| Provider | Llama4 Maverick in | Llama4 Maverick out | Qwen2.5 Coder 7B Instruct in | Qwen2.5 Coder 7B Instruct out |
|---|---|---|---|---|
| Snowflake | $0.240/M | $0.970/M | — | — |
Frequently asked questions
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.
Example: a multi-turn chat session
80% less to send — works with any model