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Alibaba Qwen3 32b vs Meta Llama3 2 11b 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.
Model
Meta Llama3 2 11b Instruct
Context window
128K
128,000 tokens · ~96K words
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.
Alibaba Qwen3 32b has about 1× the context window of the other in this pair.
Alibaba Qwen3 32b has 2% more context capacity (131K vs 128K tokens).
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Alibaba Qwen3 32b. Its 131K context fits entire documents without chunking (vs 128K).
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Alibaba Qwen3 32b | Meta Llama3 2 11b Instruct |
|---|---|---|
| Context window | 131,072 tokens (131K) | 128,000 tokens (128K) |
| Max output tokens | N/A | 4,096 tokens (4K) |
| Speed tier | Balanced | Fast |
| Vision | No | Yes |
| Function calling | No | Yes |
| Extended thinking | No | No |
| Prompt caching | No | No |
| Batch API | No | No |
| Release date | N/A | 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 | Alibaba Qwen3 32b in | Alibaba Qwen3 32b out | Meta Llama3 2 11b Instruct in | Meta Llama3 2 11b Instruct out |
|---|---|---|---|---|
| Aws Bedrock | — | — | $0.350/M | $0.350/M |
| Gradient | — | — | — | — |
Frequently asked questions
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Use a smaller model.
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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.
Example: a multi-turn chat session
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