Compare
Meta Llama3 2 11b Instruct vs Qwen Plus 2025 04 28
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
Model
Qwen Plus 2025 04 28
Context window
129K
129,024 tokens · ~97K 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.
Qwen Plus 2025 04 28 has about 1× the context window of the other in this pair.
Qwen Plus 2025 04 28 has 0% more context capacity (129K vs 128K tokens). Meta Llama3 2 11b Instruct is 12% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Qwen Plus 2025 04 28. Its 129K context fits entire documents without chunking (vs 128K).
RAG / high-volume retrieval
Use Meta Llama3 2 11b Instruct. Input tokens are 12% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use Qwen Plus 2025 04 28. Its 16K max output lets you generate complete artifacts in one request.
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Meta Llama3 2 11b Instruct | Qwen Plus 2025 04 28 |
|---|---|---|
| Context window | 128,000 tokens (128K) | 129,024 tokens (129K) |
| Max output tokens | 4,096 tokens (4K) | 16,384 tokens (16K) |
| Speed tier | Fast | Balanced |
| Vision | Yes | No |
| Function calling | Yes | Yes |
| Extended thinking | No | Yes |
| 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 | Meta Llama3 2 11b Instruct in | Meta Llama3 2 11b Instruct out | Qwen Plus 2025 04 28 in | Qwen Plus 2025 04 28 out |
|---|---|---|---|---|
| Alibaba Cloud | — | — | $0.400/M | $1.20/M |
| Aws Bedrock | $0.350/M | $0.350/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