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Llama 3.1 70B Hanami x1 vs Qwen2p5 Coder 1p5b
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.
Qwen2p5 Coder 1p5b has about 2× the context window of the other in this pair.
Qwen2p5 Coder 1p5b has 104% more context capacity (32K vs 16K tokens).
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Qwen2p5 Coder 1p5b. Its 32K context fits entire documents without chunking (vs 16K).
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Llama 3.1 70B Hanami x1 | Qwen2p5 Coder 1p5b |
|---|---|---|
| Context window | 16,000 tokens (16K) | 32,768 tokens (32K) |
| Max output tokens | N/A | 32,768 tokens (32K) |
| Speed tier | Deep | Balanced |
| Vision | No | No |
| Function calling | No | No |
| Extended thinking | No | No |
| Prompt caching | No | No |
| Batch API | No | No |
| Release date | Jan 2025 | N/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.
| Provider | Llama 3.1 70B Hanami x1 in | Llama 3.1 70B Hanami x1 out | Qwen2p5 Coder 1p5b in | Qwen2p5 Coder 1p5b out |
|---|---|---|---|---|
| Fireworks | — | — | $0.100/M | $0.100/M |
Frequently asked questions
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Example: a multi-turn chat session
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