GPT-5 nano vs Qwen3.7-Flash pricing
Side-by-side LLM API pricing.Qwen3.7-Flash is cheaper on input ($0.03 vs $0.05 /1M), and Qwen3.7-Flash is cheaper on output ($0.13 vs $0.40 /1M). On a blended average, Qwen3.7-Flash is lower — but which wins for you depends on your input/output mix, so check the example workloads below or run the calculator.
| Attribute | GPT-5 nano | Qwen3.7-Flash |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Tier | Budget | Budget |
| Status | Legacy | GA |
| Context window | 400K | 1M |
| Input /1M | $0.05 | $0.03 |
| Output /1M | $0.40 | $0.13 |
| Cached input /1M | $0.005 | $0.006 |
| Batch input /1M | $0.025 | — |
| Batch output /1M | $0.20 | — |
| Blended (avg in+out) | $0.225 | $0.08 |
| 1M in + 1M out | $0.4500 | $0.1600 |
| 1M in (90% cached) + 100K out | $0.0495 | $0.0214 |
List prices, USD, directional. Rates are provider list prices per 1M tokens and are meant for comparison, not billing. Batch rates shown as 50% off are derived where a provider offers batch but does not publish a separate figure. Preview, promo, intro, peak/off-peak, long-context, and third-party-host prices are labeled where they apply. Token counts vary by tokenizer, so per-token price is not always a like-for-like cost. Always confirm with the provider before relying on a number. Prices as of 2026-11-02.
Which should you pick?
For a balanced job (1M input + 1M output), Qwen3.7-Flash costs $0.1600 versus $0.4500. If your prompts are large and reused, prompt caching changes the maths — the cache-heavy row above shows a 90%-cached input scenario. If your work can run asynchronously, both providers' batch rates cut the bill further where offered.
Full price matrices: GPT-5 nano pricing → · Qwen3.7-Flash pricing → · Back to the LLM pricing hub →