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Germey
Germey

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One Coding Budget, Three Protocols: A Practical Multi-Model Setup with AceDataCloud Coding Plan

#ai #programming #devtools #api

If you use coding agents in the terminal or your IDE and want to switch models without maintaining a separate subscription for every ecosystem, this setup is for you. AceDataCloud Coding Plan is a 30-day, usage-metered multi-model API plan, not an official Claude, ChatGPT, or other consumer membership and not an unlimited plan.

At the time of writing, the public pricing API lists four 30-day tiers. Coding Plus starts at a ¥99 display anchor or $14.645677 USD, with 400 base Credits plus a 100-Credit limited-time bonus. The current promotion ends November 30, 2026 at 23:59:59 UTC+8. Higher tiers increase the quota, while the published model access range is the same across tiers.

Plan USD price Credits during promotion Base + bonus
Coding Plus $14.645677 500 400 + 100
Coding Standard $44.232902 1,600 1,200 + 400
Coding Pro $103.407354 4,000 3,000 + 1,000
Coding Max $206.962643 10,000 8,000 + 2,000

The CNY number is a display anchor; the price field is the USD amount. Do not mix them in cost calculations.

Start with the protocol, not the model name

Coding clients do not all speak the same API protocol. The Coding Plan setup center documents three main paths:

  • Claude Messages for Anthropic-style clients such as Claude Code.
  • OpenAI Responses for Codex agent workflows.
  • OpenAI-compatible Chat Completions for custom-provider clients such as OpenCode, Kimi Code, OpenClaw, Aider, Cline, Continue, and others.

This distinction matters because “the plan includes multiple model families” does not mean every client can call every model. A working combination must satisfy three conditions:

  1. the Coding credential permits the API;
  2. the chosen model is available for that credential and endpoint;
  3. the client supports the required protocol.

The published scope includes Claude, GPT/Codex, GLM, Kimi, and DeepSeek families, among others, but the catalog changes. Treat the live model directory as discovery data rather than a promise that every returned model works with every credential.

Minimal OpenAI-compatible configuration

The easiest protocol-neutral validation is to test the API before configuring an IDE. Create a Coding Application, obtain its dedicated token, and keep it in an environment variable rather than committing it to a repository:

export ACE_CODING_TOKEN="YOUR_CODING_APPLICATION_TOKEN"
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First retrieve the current model directory:

curl https://api.acedata.cloud/v1/models \
  -H "Authorization: Bearer $ACE_CODING_TOKEN"
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Choose an exact id, then cross-check it against your Coding Application's API scope and the protocol supported by your client. Now send a small Chat Completions request:

export ACE_MODEL_ID="YOUR_COMPATIBLE_MODEL_ID"

curl https://api.acedata.cloud/v1/chat/completions \
  -H "Authorization: Bearer $ACE_CODING_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "'"$ACE_MODEL_ID"'",
    "messages": [
      {
        "role": "user",
        "content": "Return a TypeScript function that validates a non-empty string, plus three tests."
      }
    ]
  }'
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For an OpenAI-compatible client, use https://api.acedata.cloud/v1 as the base URL, the Coding token as the API key, and the verified model ID. Claude Code and Codex should follow their dedicated guides instead of forcing Chat Completions into a client designed for another protocol.

A model-routing policy that controls spend

A useful multi-model setup needs a task policy, not random switching. Start with three lanes:

Work lane Typical tasks Selection goal
Routine boilerplate, test generation, formatting, docs lower cost and adequate code quality
Repository cross-file debugging, review, refactoring stronger code understanding and context handling
Escalation architecture decisions, stubborn failures, final review strongest suitable reasoning model

For example, use a cost-conscious model for repeated unit-test generation, move to a stronger coding model for a cross-module refactor, and reserve the highest-cost option for problems where additional reasoning changes the outcome. The exact model IDs should come from the live catalog and your client/protocol checks—not from a static blog list.

This approach also makes experiments measurable. Keep the task constant, record Credits consumed, and compare whether the more expensive lane reduces retries or review time. Do not compare only “number of prompts”: uncached input, output, cache writes, and cache reads are metered separately, while context size, tools, and parallel agents change consumption.

Calculate your real budget boundary

For the current promotional amount, effective USD per Credit is:

Coding Plus: 14.645677 / 500 = $0.029291354 per Credit
Coding Standard: 44.232902 / 1600 = $0.027645564 per Credit
Coding Pro: 103.407354 / 4000 = $0.025851839 per Credit
Coding Max: 206.962643 / 10000 = $0.020696264 per Credit
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These figures are package math, not a model-token price comparison. To estimate a specific model cost, multiply its published Credit rate for the relevant input/output/cache item by the package's effective USD-per-Credit value. Compare against an official API only when model version, billing item, service tier, cache condition, date, and currency all match.

Start with Plus if your goal is integration validation or occasional projects. Move up only after observing your own repository workload. Plans do not auto-renew or fall back to the general account balance. Renewing before expiration carries remaining Credits forward and extends validity by 30 days; otherwise unused Credits expire.

Verification checklist

Before migrating a real project:

  1. Confirm /v1/models responds with the Coding token.
  2. Verify the exact model ID, API scope, endpoint, and client protocol.
  3. Run one small deterministic coding request.
  4. Check the returned content and usage record.
  5. Run a repository task such as adding tests to one module.
  6. Record Credits before and after the task.
  7. Fully restart the client if it keeps calling its original endpoint.
  8. Re-check model IDs when a previously valid name stops working.

The practical value is not “all models everywhere.” It is one bounded API budget, several compatible protocols, and a deliberate way to assign different coding tasks to different model families.

Use the Coding Plan page for current packages and the English setup center for client-specific instructions.

References

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