There is no universal China-versus-US winner on API price: the result depends on the specific model, your input and output token volumes, and the pricing rules for your account’s region and endpoint. In the figures verified on October 7, 2026, several Chinese model APIs have lower listed rates than OpenAI’s GPT-6.1 Sol, but that is a token-price comparison—not evidence that the models deliver equivalent results or cost the same to run for your task.
Compare specific APIs, not countries
“Chinese AI models” and “US AI models” are too broad to price as groups. Providers set rates by model and token category, and a model family may have different rates or availability by deployment region. OpenAI’s API pricing page, for example, labels its listed figures “Prices per 1M tokens.” Alibaba Cloud Model Studio lists model-specific prices in CNY and has regional sections on its pricing page.
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The table below puts named models on a common USD-per-million-token basis. OpenAI’s GPT-6.1 Sol figures are OpenAI-published standard short-context rates observed on October 7, 2026. The Chinese-model figures are secondary converted estimates from LLM Abacus, which says it converted official CNY list prices at ¥6.7119 per USD and verified flagship entries on October 7, 2026. They are not a quote for your account: confirm the applicable model, currency, region, and endpoint with the provider before budgeting.
| Model | Input per 1M tokens | Output per 1M tokens |
|---|---|---|
| OpenAI GPT-6.1 Sol, standard short context | $1.00 | $5.00 |
| DeepSeek V4 Flash | $0.30 | $1.19 |
| DeepSeek V4 Pro | $1.34 | $4.02 |
| Qwen3.5 Flash | $0.030 | $0.30 |
| Qwen3.7 Max | $1.79 | $5.36 |
| Kimi K2.6 | $0.97 | $4.02 |
| GLM-5.1 | $0.89 | $3.58 |
Sources: OpenAI API pricing and LLM Abacus’s converted comparison. OpenAI’s listed GPT-6.1 Sol rates are for standard short-context usage; its pricing page shows a higher long-context tier. The converted Chinese figures are useful for screening options, but regional vendor pricing and account terms take precedence.
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Estimate the bill from your own token mix
For a request without cached input, estimate input and output separately:
(input tokens ÷ 1,000,000 × input rate) + (output tokens ÷ 1,000,000 × output rate)
For example, at the listed rates, a workload using 1 million input tokens and 200,000 output tokens would cost $2.00 on GPT-6.1 Sol at its standard short-context rates. Using the converted comparison rates, the same token volumes would cost about $0.54 on DeepSeek V4 Flash, $2.14 on DeepSeek V4 Pro, $0.09 on Qwen3.5 Flash, $2.86 on Qwen3.7 Max, $1.77 on Kimi K2.6, or $1.61 on GLM-5.1. These are arithmetic estimates from the listed rates, not observed bills; they exclude caching, batch discounts, tools, long-context surcharges, and account-specific charges.
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Check pricing rules that can change the total
Cached input
Cache-hit input may have a different rate from ordinary input. On the October 7, 2026 price listings, OpenAI showed $0.05 per million cached input tokens for GPT-6.1 Sol. LLM Abacus’s converted figures listed cached input at $0.006 for DeepSeek V4 Flash, $0.045 for DeepSeek V4 Pro, $0.16 for Kimi K2.6, and $0.19 for GLM-5.1, all per million tokens. Its comparison did not list cached-input figures for the two Qwen models, so do not infer one. DeepSeek’s official rate card separates input, cached-input, and output pricing by model ID; verify the active model and the provider’s cache rules, including any cache-write charge, before applying a discount to your estimate.
Batch, context length, and time-based rates
- Batch: Alibaba Cloud says supported batch calls use 50% of the real-time inference unit price. Check whether your model and request qualify; the discount should not be assumed for ordinary real-time calls.
- Long context: OpenAI lists a higher long-context tier for GPT-6.1 Sol. The short-context rate in the table is not a safe estimate for requests billed at that tier. Check the live rate card against your prompt and output sizes.
- Peak and off-peak pricing: If a provider applies time-based rates to your model or endpoint, include the rate for the time your calls will run rather than relying on a single headline price.
- Other billable usage: Account for tools or other separately priced features when your application uses them; the simple token formula covers only input and output tokens.
Choose the lower-cost option that meets your requirements
- Pin down the deployment. Record the exact model ID, endpoint, region, and currency available to your account. Regional scope and access can affect the price you can actually use.
- Measure a representative workload. Use real input and output token counts, including the share of requests expected to hit a cache and any long prompts or unusually long responses.
- Apply the right rate categories. Calculate ordinary input, cached input, output, and any eligible batch or long-context usage separately, using the provider’s current rate card.
- Check task fit at the required quality bar. Evaluate candidate models on your own representative tasks and include retries, review, or correction effort in your operating-cost decision. A lower rate alone cannot establish that a model is an adequate substitute.
- Recheck the account-level price. Confirm current regional rates, availability, payment and contractual terms, and applicable taxes with the provider. Those details can change the all-in amount and are not settled by converted list-price comparisons.
Recheck live pricing immediately before committing: model names, rates, promotions, and regional terms can change. OpenAI publishes its rates at its API pricing page; Alibaba Cloud publishes model pricing at its Model Studio pricing page; and DeepSeek’s model-specific categories are shown on its API rate card.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
