Compare
Meta Llama3 1 8b Instruct vs Qwen-Plus
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 1 8b 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.
Qwen-Plus has about 1× the context window of the other in this pair.
Qwen-Plus has 0% more context capacity (129K vs 128K tokens). Meta Llama3 1 8b Instruct is 45% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Qwen-Plus. Its 129K context fits entire documents without chunking (vs 128K).
RAG / high-volume retrieval
Use Meta Llama3 1 8b Instruct. Input tokens are 45% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use Qwen-Plus. 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 1 8b Instruct | Qwen-Plus |
|---|---|---|
| Context window | 128,000 tokens (128K) | 129,024 tokens (129K) |
| Max output tokens | 2,048 tokens (2K) | 16,384 tokens (16K) |
| Speed tier | Fast | Balanced |
| Vision | No | No |
| Function calling | Yes | Yes |
| Extended thinking | No | Yes |
| Prompt caching | No | Yes |
| Batch API | No | No |
| Release date | N/A | Feb 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 | Meta Llama3 1 8b Instruct in | Meta Llama3 1 8b Instruct out | Qwen-Plus in | Qwen-Plus out |
|---|---|---|---|---|
| Alibaba Cloud | — | — | $0.400/M | $1.20/M |
| Aws Bedrock | $0.220/M | $0.220/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