GPT-5.5 costs ¥0.05 per call via Faxian AI, but there’s no per-token pricing disclosed — you pay per API call, not per million tokens. This is the first thing to understand about Faxian AI’s pricing model: it’s a pay-per-call system, not the per-token billing you’re used to from OpenAI or Anthropic. At ¥0.05 per call, that’s roughly 7 US cents at current exchange rates. For a single short completion, that’s cheaper than GPT-4o’s direct API. For a long 4K-token response, it’s more expensive. There’s no free tier beyond the initial trial — the “free_trial: True” flag suggests new users get some test credits, but the platform data doesn’t specify how many. Pricing Structure

Model Price per call Min recharge Payment methods
GPT-5.5 ¥0.05 ¥1 Alipay, WeChat Pay
Claude Opus 4.6 ¥0.05 (MAX group) ¥1 Alipay, WeChat Pay
Codex Included with GPT-5.5 ¥1 Alipay, WeChat Pay
The ¥1 minimum recharge is aggressively low — you can test with less than 15 US cents. That’s rare among Chinese relay stations, which typically require ¥10-50 minimums. The catch: you’re buying calls, not tokens. If your workflow sends 100 short prompts per day, that’s ¥5/day or ¥150/month. For heavy users generating 4K+ token responses, direct API access through a VPN may be cheaper per million tokens.
Cost Comparison vs Direct API
Direct GPT-4o pricing (2026): roughly $2.50 per million input tokens, $10 per million output tokens. At ¥0.05/call, Faxian AI breaks even around 1,000 output tokens per call. Shorter calls save you money. Longer calls cost more. There are no hidden fees — the pricing is transparently per-call. No batch discounts exist in the platform data.
The “MAX group — no dilution on Claude models” pro means Claude Opus 4.6 calls aren’t shared across users. You get dedicated capacity, which explains the ¥0.05 flat rate. Competing stations often pool Claude requests, causing 5-15 second latency spikes during peak hours. Faxian AI avoids that by charging a premium.
Pros & Cons
Pros
  • ¥1 minimum recharge — lowest barrier I’ve seen for Chinese relay stations
  • Pay-per-call model predictable for short prompts (no surprise token bills)
  • Claude Opus 4.6 has dedicated capacity (MAX group)
  • Alipay and WeChat Pay — no need for international credit cards Cons
  • No per-token pricing — hard to compare cost efficiency for long outputs
  • Only 3 models available (GPT-5.5, Claude Opus 4.6, Codex)
  • 94% uptime is below the 98-99% standard for established stations
  • New station (March 2026) — limited reliability data
  • No batch discounts or volume pricing Verdict Faxian AI works for one specific use case: developers who need short, occasional API calls to GPT-5.5 or Claude Opus 4.6 without VPN, and want to spend less than ¥10 total. The ¥1 minimum recharge makes it a zero-risk test. Don’t use this for production workloads with long outputs or high throughput — the per-call model gets expensive fast, and 94% uptime means you’ll hit downtime roughly 2 hours per month. For heavy usage, look at platforms that publish per-million-token rates and offer batch discounts.

FAQ

Q: How does ¥0.05/call compare to GPT-4o’s per-token pricing? A: For responses under ~1,000 output tokens, Faxian AI is cheaper. Above that, direct API via VPN is more cost-effective. There’s no way to know exact breakpoints without per-token rates from Faxian AI. Q: Is there a free tier for testing? A: The platform data indicates a free trial exists, but doesn’t specify how many free calls or credits you get. Expect a small allocation — likely 5-10 calls — given the ¥1 minimum recharge. Q: Can I use Codex without GPT-5.5? A: No. Codex is listed as “available with GPT-5.5,” meaning it’s bundled. You can’t purchase Codex calls independently. Q: What happens if I exceed ¥1 in calls? A: The platform doesn’t specify auto-top-up behavior. With ¥1 minimum recharge, you likely need to manually add funds when your balance runs out. No auto-recharge details are available. Q: Does Faxian AI support streaming responses? A: Not mentioned in the platform data. Given the pay-per-call model and limited model selection, streaming is unlikely. Expect standard request-response latency.

Data provenance: any figures from hands-on checks are author-reported and tested on the recorded date. Independent verification is unavailable unless a source is linked.