MoE flagship balancing frontier quality with framework-native serving costs
qwen3-235b-a22b| Scenario | Input Tokens | Output Tokens | Uncached Cost | With Prompt Caching |
|---|---|---|---|---|
| Short Chat Query | 1,000 | 500 | $0.00136 | $0.00109 |
| Document Summarization | 10,000 | 2,000 | $0.00819 | $0.00544 |
| Codebase & Context Analysis | 100,000 | 20,000 | $0.0819 | $0.0544 |
| Batch Corpus Processing | 1,000,000 | 100,000 | $0.6370 | $0.3620 |
Tier 1 (free tier) reference limits — your account's actual quota may be higher.
Ideal for production workloads demanding balanced capabilities, deep context depth (128k tokens), and reliability from Alibaba / Qwen. Excellent when predictable tokenomics and prompt caching support are paramount.
If your use-case requires sub-second streaming latency or ultra-high frequency classification at micro-cent pricing, consider lighter budget options such as Gemini Flash-Lite or Claude Haiku. For deep formal logic, consider dedicated reasoning models like o3.
For Qwen3 235B-A22B, 1 million input tokens costs $0.455, while 1 million output tokens costs $1.82. If using prompt caching, repetitive input prefixes are discounted to $0.18 per million.
Qwen3 235B-A22B features a maximum context window of 131,072 tokens (~98,304 words), with a maximum output limit of 32,768 tokens per completion.
Qwen3 235B-A22B utilizes the Qwen SentencePiece BPE (152k vocabulary). Token counting on TokenMath runs client-side to ensure maximum privacy.
Yes. Qwen3 235B-A22B supports prompt caching with a cached input rate of $0.18/1M (saving up to 60% on repeated input context).