Mid-tier Mainstream models Direct connect ★ 3.4 / 5

DawCode Review: Pricing & Comparison

A new 2026 platform, daily check-in rewards, ¥4 new-user trial credit

Last verified: 2026-08-11 · Visit official site →

Daily check-ins: an extra reward for staying active

Most AI relay platforms keep their incentive structure simple: the more you top up, the lower your unit price. DawCode (dawcode.ai) adds another dimension: daily check-in rewards — check in every day you log in, and accumulate extra tokens or points that convert into usable credit.

This mechanism has real value for developers who use the Claude API on a regular basis: you’re already using it every day, so checking in along the way for a small extra reward effectively lowers your average long-term cost.

The new-user path

DawCode’s new-user design is built around a “try before you buy” flow:

  1. Register an account
  2. Get ¥4 in trial credit (no top-up required first)
  3. Use the trial credit to test Claude model calls
  4. Once you’ve confirmed it meets your needs, consider topping up for real

¥4 in credit is enough for testing purposes — under normal usage, ¥4 of Claude Opus API calls can support dozens to hundreds of conversation turns.

Integration

from anthropic import Anthropic

# Compatible with the Anthropic SDK
client = Anthropic(
    api_key="your DawCode API key",
    base_url="https://api.dawcode.ai"  # check the official site for the actual endpoint
)

message = client.messages.create(
    model="claude-opus-4-8",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Help me review this SQL query for performance issues"}
    ]
)
print(message.content[0].text)

Claude Opus pricing reference

DawCode prices Claude Opus 4.6/4.7/4.8 input at around ¥10/million tokens, GPT-5.5 at around ¥2/million tokens, and Sonnet 5 at around ¥6/million tokens (third-party price tracking; check the official site for exact figures). Among domestic Claude relay platforms in 2026, that puts it in the medium range, suited to developers with fairly frequent Opus usage.

For comparison: Zhihui API has similar Opus pricing and is also a new 2026 platform; WinToken offers a larger new-user trial credit; AiGoCode takes the reverse-engineered low-price route, with lower prices but a different compliance profile.

The sensible strategy for a new platform remains the same: verify with a small trial first, then decide whether to scale up usage.

Information verified 2026-08-11. Details of the check-in reward mechanism and pricing are subject to DawCode’s current official-site information — as a new platform, details tend to update frequently.

  • Zhihui API: a new 2026 platform, redemption codes starting at ¥5, Opus as low as ¥8.12/million tokens
  • WinToken: a new platform with a big signup bonus — roughly ¥113 in new-user trial credit, subscription + pay-as-you-go dual mode, broad model coverage
  • ByteCat: built for coding engineers, includes a full Codex Linux/Windows guide, mainland direct connect, specialized in AI coding models
  • DeepSeek: DeepSeek’s official API, with V4-series pricing that upended the market and strong reasoning ability

Quick facts

Pricing modelPay-as-you-go; ¥4 new-user trial credit; a daily check-in reward mechanism; Opus 4.8 around ¥10/million input tokens (third-party price tracking); Alipay/WeChat Pay
Model coverageMainly the Claude Opus series; some GPT/DeepSeek coverage; check the official site for specifics
Latency / SLAMainland direct connect; a new 2026 platform, so stability needs more time to verify
Mainland direct connectDirect connect
Best forDevelopers
Referral programNo public affiliate/referral program found.

Pros

  • Daily check-in rewards: regular DawCode users can accumulate extra token credit through daily check-ins, lowering the long-term cost of use
  • Low barrier to entry: ¥4 new-user trial credit, letting you verify platform quality at close to zero cost
  • Mainland direct connect: no proxy required

Cons

  • A new 2026 platform with a short operating history — more user feedback and time are needed to verify stability
  • The rate of point accumulation and redemption ratio for check-in rewards need to be looked into after signing up
  • Limited public information; feature details need to be explored on your own

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