The first step in model selection is not comparing leaderboards. Confirm the task, context, output format and whether bad results can be detected automatically. Verify GPT, Claude and Gemini protocol endpoints, tool calls, latency and cost with the same business samples.
Check before you start
Answer five questions first:
- Is the input short text, a long context, an image or a file?
- Should the output be plain text, structured data or a tool-call result?
- How long can users wait?
- Can the results be verified by rules or manually?
- What is the acceptable cost for a single task?
Link model details and integration checks
Use these public Link model pages to check model IDs and setup instructions. The pages were checked for accessibility on 26 October 2026; this is not proof of successful API calls or evidence that your API Key, group or monthly plan has the same access.
| Model details | Check before accessing |
|---|---|
| gpt-6-astra API | Check whether your client needs Responses or Chat Completions, and whether the model ID and tool-calling support match your selected group. |
| claude-haiku-5-5 API | Choose Anthropic Messages or a compatible endpoint; test authentication, streaming completion events and tool calling separately. |
| gemini-3.6-flash API | Choose an OpenAI-compatible endpoint or native generateContent. Check the separate Gemini group; do not assume a GPT or Claude monthly plan applies. |
| gpt-image-2 API | Check the image endpoint, input format, output format and billing unit. A text-token cost example is not an image-generation price quote. |
These links open product information, not API request endpoints. Confirm the protocol and access permissions, test a small sample, then compare costs for the same tasks.
Link billing: choosing pay-as-you-go or a monthly plan · Compare monthly-plan costs for your actual usage days
Configuration steps
GPT models
Suitable for dialogue, summarization, translation, code generation, classification and extraction. When choosing, pay attention to:
- Context window.
- Tool calling capabilities.
- Structured output stability.
- Output speed versus tokens cost.
With an OpenAI-compatible endpoint, first verify the model list, short text, structured output, streaming and tool calls, then choose a capability tier for complex coding and long agent tasks.
Claude models
For Anthropic Messages, verify message structure, continuous streaming, tool calls, context and usage separately. One successful OpenAI Chat Completions request does not prove that the Claude endpoint is fully usable.
Gemini models
Link lists a separate Gemini group. Start with OpenAI-compatible setup, then check native generateContent separately. Catalog labels do not replace image, audio, SSE or tool-call tests. Do not assume GPT or Claude plan coverage applies to Gemini, or that Gemini is a drop-in replacement for Codex or Claude Code protocols.
Gemini API configuration and model catalog
Comparing GPT, Claude and Gemini
Use the same business prompts, parameters and test windows, and record separately:
- Whether structured output passes validation.
- Whether tool names and JSON arguments are complete.
- Time to first byte, total time and error distribution.
- Input and output tokens, retry count and total cost per task.
- Manual correction time and result stability.
FAQ
Should the most expensive model be used for all tasks?
It shouldn't be. Stratify tasks by complexity and failure cost: use low-cost models for tasks that are simple and verifiable, use more capable models for tasks that are complex and costly to fail.
The model names are similar, can they be replaced directly?
Full compatibility cannot be assumed. Even within the same family, context, output caps, tool calls, and parameter support may differ. Use fixed samples for regression testing.
How to assess true costs
Don’t just look at unit price per million tokens. Also record the number of retries, output length, manual correction time, and failed task costs.
Next step
Establish a set of fixed samples covering real failure cases, record completion rate, delay, tokens, manual correction time and total cost of a single task respectively, and then decide the production route.