Compare
Gpt 4o Mini Audio Preview vs Qwen3 Coder 480B A35B (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 Audio Preview
Context window
128K
128,000 tokens · ~96K words
Model
Qwen3 Coder 480B A35B (free)
Context window
262K
262,000 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 Coder 480B A35B (free) has about 2× the context window of the other in this pair.
Qwen3 Coder 480B A35B (free) has 104% more context capacity (262K vs 128K tokens).
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Qwen3 Coder 480B A35B (free). Its 262K context fits entire documents without chunking (vs 128K).
Long output (reports, code files)
Use Qwen3 Coder 480B A35B (free). Its 262K 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 | Gpt 4o Mini Audio Preview | Qwen3 Coder 480B A35B (free) |
|---|---|---|
| Context window | 128,000 tokens (128K) | 262,000 tokens (262K) |
| Max output tokens | 16,384 tokens (16K) | 262,000 tokens (262K) |
| Speed tier | Fast | Balanced |
| Vision | No | No |
| Function calling | Yes | Yes |
| Extended thinking | No | No |
| Prompt caching | No | No |
| Batch API | Yes | No |
| Release date | N/A | Jul 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 Audio Preview in | Gpt 4o Mini Audio Preview out | Qwen3 Coder 480B A35B (free) in | Qwen3 Coder 480B A35B (free) out |
|---|---|---|---|---|
| Openai | $0.150/M | $0.600/M | — | — |
Frequently asked questions
Powered by Mem0
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
80% less to send — works with any model