comparison
Qwen3 Max vs MiniMax M2.5
Token pricing, context window and real monthly cost, side by side. MiniMax M2.5 is the cheaper of the two for a typical workload — about 4.6× less.
| metric | Qwen3 Max | MiniMax M2.5 |
|---|---|---|
| Input / 1M | $0.78 | $0.15 |
| Output / 1M | $3.90 | $0.90 |
| Context | 262K | 205K |
| Cost @ typical workload | $624/mo | $135/mo |
| Modality | Text only | Text only |
| Price source | routed | routed |
| Provider | Qwen | MiniMax |
Snapshot . Cost uses a typical workload; tune it in the calculator. How we measure →
Which should you pick?
On a typical workload, MiniMax M2.5 costs $135/mo against Qwen3 Max's $624/mo — roughly 4.6× cheaper. But the ranking depends on your output-to-input ratio: output is the pricier direction for both, so an output-heavy job (code generation, long answers) widens the gap while an input-heavy one (summarization, retrieval) narrows it. If you need to fit more in a single prompt, Qwen3 Max has the larger 262K-token window (~393 pages).
These are list and routed market prices, not measured outcomes. Two models at the same rate can still cost different amounts to finish the same task, because verbose or reasoning-heavy models emit more tokens. That gap is exactly what measured cost-per-task captures.
Frequently asked questions
Is Qwen3 Max or MiniMax M2.5 cheaper?
For a typical workload (1,500 input + 500 output tokens × 200,000 requests/month), MiniMax M2.5 costs $135/mo versus $624/mo for Qwen3 Max — about 4.6× less. Because output is priced higher than input, the winner can flip if your workload writes much more or less than this; check your own numbers in the calculator.
What's the main difference between Qwen3 Max and MiniMax M2.5?
On price, Qwen3 Max is $0.78/$3.90 per 1M (in/out) and MiniMax M2.5 is $0.15/$0.90. Qwen3 Max has the larger context window at 262K tokens.
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- GPT-5.4 nano vs MiniMax M2.5
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Related
- Qwen3 Max and MiniMax M2.5 — full specs and price history.
- API cost calculator — compare on your own workload.
- All comparisons — the full head-to-head index.