We Found Claude at 65% Off in the China Market — And Proved It Actually Works

TL;DR — We discovered Chinese API aggregators reselling Anthropic Claude at 65% below OpenRouter through legitimate enterprise channels. Before relisting any model we ran three waves of validation: connectivity probing, billing-overhead forensics (against the open-source One-API billing formula), and a 6-scenario semantic benchmark. All three Claude models — Opus 4.7, Sonnet 4.6, Haiku 4.5 — passed. They are now live on NovAI at the most competitive prices on the global market.

Why Claude was missing from NovAI until now

Since NovAI launched, we have been a Chinese-model-first gateway — DeepSeek, Qwen, GLM, Doubao, MiniMax, Moonshot. Claude was conspicuously absent, despite being one of the most-requested models from our users. Why?

Because the math did not work. Anthropic's direct pricing is steep:

• Claude Opus: $15 input / $75 output per 1M tokens
• Claude Sonnet: $3 / $15 per 1M
• Claude Haiku: $1 / $5 per 1M

OpenRouter resells at those same prices plus a 5.5% topup surcharge. If we bought from either source and added any margin, we would land higher than both. Our users would get nothing new.

The discovery: a fully-working Chinese aggregator with legitimate upstream

In April 2026 we began a systematic audit of the top Chinese API aggregators. Our target was simple: find a source whose Claude prices were materially below Anthropic direct without sacrificing the official model endpoints.

One aggregator stood out. For the purposes of this article we will call them "the upstream". Their published Claude prices were 35% of Anthropic direct retail. That is not a typo — sixty-five percent below. But cheap alone means nothing. Telegram is full of shady reseller screenshots. Before shipping a single line of integration code, we had to prove three things:

1. Reachability: does the upstream endpoint actually respond, from our production infrastructure?
2. Authenticity: are we talking to real Anthropic models, or a degraded fine-tune?
3. Billing transparency: is there hidden token inflation that would erase the discount?

What follows is exactly how we tested each one.

Wave 1: Connectivity & model enumeration

First smoke test — list available models, then POST a minimal "hi" to every Claude variant we cared about.

curl -sS -H "Authorization: Bearer $KEY" \
  "https://upstream.example/v1/models" \
  | jq '.data[] | select(.id | test("claude")) | .id'

# Then enumerate each candidate:
for m in claude-opus-4-7 claude-opus-4-6 claude-sonnet-4-6 \
         claude-haiku-4-5-20251001 claude-sonnet-4-5-20250929; do
  curl -sS -o /dev/null -w "$m %{http_code}\n" \
    -X POST "https://upstream.example/v1/chat/completions" \
    -H "Authorization: Bearer $KEY" -H "Content-Type: application/json" \
    -d "{\"model\":\"$m\",\"messages\":[{\"role\":\"user\",\"content\":\"hi\"}],\"max_tokens\":10}"
done

Result: 6 models came back HTTP 200 with real-looking completions. Several deprecated aliases returned 403 — a good sign, because it means the upstream is not routing every request through a generic fallback, they actually maintain per-model access.

Wave 2: Billing overhead forensics

This is where most reseller audits stop. It is also where most hidden costs live.

When you send a 2-character message ("hi") to Anthropic direct, the returned prompt_tokens should be roughly 8–10. If the upstream returns 413 or 2041, someone is injecting a system prompt you did not see. And you are paying for it.

We ran the same "hi" call 6 consecutive times against each model and measured the reported input tokens. Full results:

The user pays for the overhead tokens at our retail price, which means a 2000-token injection would erase 30%+ of any meaningful conversation — unacceptable. We dropped those variants.

The 413-token overhead on Sonnet 4.6 and Haiku 4.5 is still noticeable, so we added explicit disclosure to the model cards. At Haiku's $0.50 / 1M retail that is still just $0.0002 per call, a negligible amount for anything beyond trivial test messages.

Wave 3: One-API billing formula audit

After the overhead probe, we realized the upstream runs on the open-source One-API billing engine — evidenced by the X-Oneapi-Request-Id response header and a publicly readable /api/pricing endpoint.

One-API computes cost as:

cost_usd = (input_tokens * model_ratio
          + output_tokens * model_ratio * completion_ratio)
          * group_ratio * (1 / 500000)

Pulling /api/pricing gave us the raw model_ratio, completion_ratio, and the group-level discount table. Cross-multiplying against the scraped marketplace prices produced an exact match to 4 decimal places:

Wave 4: Six-scenario semantic benchmark

Cheap and correctly-billed means nothing if quality is degraded. Final check: can the upstream-routed models actually handle real tasks?

We ran each model through six representative scenarios:

• Code: implement Fibonacci with memoization (expect: clean, idiomatic Python)
• Translation: Chinese → English preserving idiom and tone
• Math: multi-step train-meeting problem with stepwise reasoning
• Summarization: compress 4 technical sentences into 2
• Role-play: professional rebuttal as senior engineer
• Long-context: extract unique IPs from a 10-line log

All three models passed all six scenarios. Opus 4.7 produced the most elegant code (used functools.lru_cache instead of a manual dict). Sonnet 4.6 and Haiku 4.5 were indistinguishable from their Anthropic-direct behavior we benchmarked last quarter.

Final pricing on NovAI

Prices are OpenRouter-comparable (their standard + 5.5% surcharge). NovAI charges zero platform fee and zero topup surcharge, so every USD you deposit buys USD-equivalent compute.

Quickstart — drop-in for OpenAI SDK

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_NOVAI_KEY",
    base_url="https://aiapi-pro.com/v1",
)

# Swap model name — that's it.
response = client.chat.completions.create(
    model="claude-opus-4-7",   # or claude-sonnet-4-6, claude-haiku-4-5
    messages=[{"role": "user", "content": "Explain RAG in 3 paragraphs."}],
    stream=True,
)
for chunk in response:
    print(chunk.choices[0].delta.content or "", end="", flush=True)

Further reading

• Claude Opus 4.7 Deep Review — 1M Context Changes Everything
• Claude vs GPT-5 vs DeepSeek-V4: The Complete 2026 Benchmark
• Migrate from Anthropic SDK to OpenAI SDK in 3 Lines
• DeepSeek vs Claude: When to pick which