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GPT-4o-mini Search Preview vs Qwen3 Next 80B A3B Instruct (free)
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
GPT-4o-mini Search Preview
Context window
128K
128,000 tokens · ~96K words
Model
Qwen3 Next 80B A3B Instruct (free)
Context window
262K
262,144 tokens · ~197K 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.
Qwen3 Next 80B A3B Instruct (free) has about 2× the context window of the other in this pair.
Qwen3 Next 80B A3B Instruct (free) has 104% more context capacity (262K vs 128K tokens).
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Qwen3 Next 80B A3B Instruct (free). Its 262K 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 | GPT-4o-mini Search Preview | Qwen3 Next 80B A3B Instruct (free) |
|---|---|---|
| Context window | 128,000 tokens (128K) | 262,144 tokens (262K) |
| Max output tokens | 16,384 tokens (16K) | N/A |
| Speed tier | Fast | Fast |
| Vision | Yes | No |
| Function calling | Yes | Yes |
| Extended thinking | No | No |
| Prompt caching | Yes | No |
| Batch API | Yes | No |
| Release date | Mar 2025 | Sep 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 | GPT-4o-mini Search Preview in | GPT-4o-mini Search Preview out | Qwen3 Next 80B A3B Instruct (free) in | Qwen3 Next 80B A3B Instruct (free) out |
|---|---|---|---|---|
| Openai | $0.150/M | $0.600/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