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Llama3 3 70b vs QwQ 32B
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.
QwQ 32B has about 1× the context window of the other in this pair.
QwQ 32B has 2% more context capacity (131K vs 128K tokens). QwQ 32B is 69% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use QwQ 32B. Its 131K context fits entire documents without chunking (vs 128K).
RAG / high-volume retrieval
Use QwQ 32B. Input tokens are 69% cheaper — critical when sending large retrieved contexts.
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Llama3 3 70b | QwQ 32B |
|---|---|---|
| Context window | 128,000 tokens (128K) | 131,072 tokens (131K) |
| Max output tokens | N/A | 131,072 tokens (131K) |
| Speed tier | Deep | Balanced |
| Vision | No | No |
| Function calling | No | Yes |
| Extended thinking | No | Yes |
| Prompt caching | No | No |
| Batch API | No | No |
| Release date | N/A | Mar 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 | Llama3 3 70b in | Llama3 3 70b out | QwQ 32B in | QwQ 32B out |
|---|---|---|---|---|
| Deepinfra | — | — | $0.150/M | $0.400/M |
| Fireworks | — | — | $0.900/M | $0.900/M |
| Gradient | $0.650/M | $0.650/M | — | — |
| Hyperbolic | — | — | $0.200/M | $0.200/M |
| Nebius | — | — | $0.150/M | $0.450/M |
| Nscale | — | — | $0.180/M | $0.200/M |
| Sambanova | — | — | $0.500/M | $1.00/M |
| Snowflake | — | — | — | — |
Frequently asked questions
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Example: a multi-turn chat session
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