Model comparison
DeepSeek V3.2 vs Qwen3 Max
Compare DeepSeek V3.2 vs Qwen3 Max API pricing: input/output token costs, cache pricing, context windows, workload estimates, and routing fit.
DeepSeek V3.2
deepseek · deepseek/deepseek-v3.2
- Input
- $0.28
- Output
- $0.4
- Context
- 163.8K
Qwen3 Max
novita · novita/qwen/qwen3-max
- Input
- $2.11
- Output
- $8.45
- Context
- 262.1K
Quick take
DeepSeek V3.2 has the lower input price at $0.28 per 1M input tokens. DeepSeek V3.2 is cheaper for the example blended workload below. Qwen3 Max has the larger context window at 262.1K tokens.
Choose DeepSeek V3.2 if...
- DeepSeek V3.2 is the better default for cost-sensitive traffic and repeated high-volume calls.
- DeepSeek V3.2 gives more room for long generated answers, reports, or code output.
- DeepSeek V3.2 is stronger when the same large prompt or document is reused because it supports prompt caching.
Choose Qwen3 Max if...
- Qwen3 Max is safer for long documents, repository analysis, and RAG prompts because it has the larger context window.
Example workload cost
Estimates use input tokens plus 20% output tokens. They exclude provider discounts, cache hits, and tool/search surcharges.
| Workload | DeepSeek V3.2 | Qwen3 Max | Cheaper |
|---|---|---|---|
| 1M input + 200K output | $0.36 | $3.80 | DeepSeek V3.2 |
| 10M input + 2M output | $3.60 | $38.00 | DeepSeek V3.2 |
| 100M input + 20M output | $36.00 | $380.00 | DeepSeek V3.2 |
Context, output, and capability fit
Qwen3 Max provides the larger context window. Check max output separately when the task needs long reports, code generation, or full-document rewrites.
- DeepSeek V3.2 max output
- 163.8K
- Qwen3 Max max output
- 65.5K
- DeepSeek V3.2 features
- prompt caching, function calling
- Qwen3 Max features
- function calling
Risk notes for DeepSeek V3.2
- Smaller context window: long PDFs, codebases, or RAG prompts may need chunking.
Risk notes for Qwen3 Max
- No prompt caching in this snapshot: repeated long-context calls may be more expensive.
Routing tags
budgethigh-volumecodingreasoningopen-weightlocal-openfrontieragents