One Coding Budget, Three API Protocols: A Practical Multi-Model Setup

If you use Claude Code, Codex, or another configurable coding agent and want one bounded budget across several model families, this guide shows how to choose the right protocol, verify a Coding Plan credential, and assign models by task instead of by brand.

AceDataCloud Coding Plan is currently offered as a 30-day, Credit-metered multi-model API plan, starting with Coding Plus at a ¥99 display anchor (US checkout price: $14.645677). During the current promotion, scheduled to end November 30, 2026, Coding Plus includes 500 Credits: 400 base plus 100 bonus. It is an API plan, not an official Claude, ChatGPT, or Codex consumer membership.

Start with the client, then choose the protocol

The important integration decision is not “Which model is best?” It is “Which protocol does my existing client speak correctly?”

Coding Plan exposes three useful protocol families:

Client or integration style	Protocol to prefer	Relevant endpoint shape
Claude Code and Anthropic-native tooling	Claude Messages	/v1/messages
Codex and Responses-aware agents	OpenAI Responses	/openai/responses
Generic editors and custom scripts	OpenAI-compatible Chat Completions	provider-specific paths such as /openai/chat/completions, /glm/chat/completions, or /deepseek/chat/completions

Do not assume every client supports every model through every protocol. A model can exist in the live catalog but still be incompatible with your client's request format or your credential's allowed API scope. The Coding Plan integration center maps clients including Claude Code, Codex, Kimi Code, OpenCode, and OpenClaw to their appropriate configuration guides.

The minimum verification workflow

After purchasing a plan, create or select the Coding Application credential shown by the platform. Keep it out of source control:

export ACE_CODING_TOKEN="YOUR_CODING_APPLICATION_TOKEN"

First request the live model directory:

curl --fail-with-body https://api.acedata.cloud/v1/models \
  -H "Authorization: Bearer $ACE_CODING_TOKEN"

Use an exact id returned by that directory as the API model value. The directory is dynamic, so a repository should not treat a model name copied from an old article as permanent.

Next, validate the protocol you actually intend to use. For a Claude Messages client, a minimal request has this shape:

curl --fail-with-body https://api.acedata.cloud/v1/messages \
  -H "Authorization: Bearer $ACE_CODING_TOKEN" \
  -H "Content-Type: application/json" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "MODEL_ID_FROM_LIVE_DIRECTORY",
    "max_tokens": 64,
    "messages": [{"role": "user", "content": "Return only: coding-plan-ok"}]
  }'

For an OpenAI-compatible integration, preserve the provider path documented for the selected model family:

curl --fail-with-body https://api.acedata.cloud/openai/chat/completions \
  -H "Authorization: Bearer $ACE_CODING_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "MODEL_ID_FROM_LIVE_DIRECTORY",
    "messages": [{"role": "user", "content": "Return only: coding-plan-ok"}],
    "max_tokens": 64
  }'

A successful response confirms authentication, endpoint, scope, model ID, and basic request formatting. It does not prove that a particular IDE is configured correctly; test the client only after this smaller request works.

Split work by cost and failure impact

A multi-model plan is most useful when model selection is deliberate. A practical routing policy is:

1. Fast, repetitive work: use an available lower-cost model for boilerplate, test-case expansion, formatting, and first-pass documentation.
2. Repository reasoning: move to a stronger coding model for cross-file changes, dependency analysis, and review.
3. High-risk changes: reserve your most capable compatible model for migrations, security-sensitive code, and final review.
4. Fallbacks: switch manually when a model or protocol is unavailable; do not silently send a security task to a weaker fallback.

The current service description covers Claude, GPT/Codex, GLM, Kimi, and DeepSeek ecosystems. Exact versions should come from the live directory and the selected client guide.

This policy matters because Credits are not equivalent to a fixed number of prompts. Uncached input, output, cache writes, and cache reads are metered separately. Long context, agent tools, repeated retries, and parallel workers can consume substantially more than a short single-file request.

Pick a tier with arithmetic, not optimism

All four tiers currently have the same Coding model access; they differ mainly in included Credits. Under the current promotion:

Tier	30-day display anchor	Included Credits
Coding Plus	¥99	500
Coding Standard	¥299	1,600
Coding Pro	¥699	4,000
Coding Max	¥1,399	10,000

Start with the smallest tier that covers a representative week multiplied by four, plus a retry margin. Track which jobs create long contexts or agent loops, then resize at renewal.

There are three boundaries to include in that calculation:

• unused Credits expire at the end of the term;
• renewing before expiry carries remaining Credits forward and extends the term by 30 days;
• there is no automatic renewal or fallback to the general account balance.

That last point is useful for budget containment: an unattended agent cannot quietly spill into a separate general balance. It also means automation should detect insufficient-balance errors and stop cleanly.

Troubleshooting checklist

When a client fails after the curl test succeeds, compare these items in order:

• the token belongs to a Coding Application;
• the base URL and path match the client's protocol;
• the model ID exactly matches the current directory;
• the credential scope includes the required API;
• the client was fully restarted after configuration changes;
• no workspace-level setting overrides the user-level setting.

The configuration center contains client-specific setup, while the Coding Plan page shows current purchase options. For background on plan fit and subscription boundaries, see Ace Data Cloud's ¥99 plan guide and multi-subscription analysis.

The maintainable approach is simple: pin the protocol per client, discover model IDs dynamically, route tasks by risk, and let measured Credit usage—not model hype—choose the next tier.