High-efficiency open-weights coding model hosted on Together AI serverless infrastructure Need a token count first? Use the Qwen token counter.
qwen/qwen-2.5-coder-32b-instruct| Scenario | Input Tokens | Output Tokens | Uncached Cost | With Prompt Caching |
|---|---|---|---|---|
| Short Chat Query | 1,000 | 500 | $0.00120 | N/A |
| Document Summarization | 10,000 | 2,000 | $0.00960 | N/A |
| Codebase & Context Analysis | 100,000 | 20,000 | $0.0960 | N/A |
| Batch Corpus Processing | 1,000,000 | 100,000 | $0.8800 | N/A |
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 Qwen 2.5 Coder 32B — Together AI, 1 million input tokens costs $0.80, while 1 million output tokens costs $0.80. If using prompt caching, repetitive input prefixes are discounted to $0.80 per million.
Qwen 2.5 Coder 32B — Together AI features a maximum context window of 131,072 tokens (~98,304 words), with a maximum output limit of 8,192 tokens per completion.
Qwen 2.5 Coder 32B — Together AI utilizes the Qwen Byte-level BPE (~152k vocabulary). Token counting on TokenMath runs client-side to ensure maximum privacy.
No, Qwen 2.5 Coder 32B — Together AI does not currently advertise prompt caching discounts on its standard API tier.