comparison
Qwen3 Max vs DeepSeek V4 Pro
Token pricing, context window and real monthly cost, side by side. DeepSeek V4 Pro is the cheaper of the two for a typical workload — about 2.9× less.
| metric | Qwen3 Max | DeepSeek V4 Pro |
|---|---|---|
| Input / 1M | $0.78 | $0.43 |
| Output / 1M | $3.90 | $0.87 |
| Context | 262K | 1.0M |
| Cost @ typical workload | $624/mo | $217/mo |
| Modality | Text only | Text only |
| Price source | routed | list |
| Provider | Qwen | DeepSeek |
Snapshot . Cost uses a typical workload; tune it in the calculator. How we measure →
Which should you pick?
On a typical workload, DeepSeek V4 Pro costs $217/mo against Qwen3 Max's $624/mo — roughly 2.9× 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, DeepSeek V4 Pro has the larger 1.0M-token window (~1,573 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 DeepSeek V4 Pro cheaper?
For a typical workload (1,500 input + 500 output tokens × 200,000 requests/month), DeepSeek V4 Pro costs $217/mo versus $624/mo for Qwen3 Max — about 2.9× 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 DeepSeek V4 Pro?
On price, Qwen3 Max is $0.78/$3.90 per 1M (in/out) and DeepSeek V4 Pro is $0.43/$0.87. DeepSeek V4 Pro has the larger context window at 1.0M tokens.
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Related
- Qwen3 Max and DeepSeek V4 Pro — full specs and price history.
- API cost calculator — compare on your own workload.
- All comparisons — the full head-to-head index.