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There is no single best model API for every production workload. GPT, Claude, and Gemini are model families, not fixed performance or price categories. Compare the exact versions available to your account on the tasks your application needs. A family name alone does not establish accuracy, context length, tool support, or data handling terms.

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Build a shortlist from the current catalog

Open the model catalog and prices. For each candidate, record its exact ID, supported endpoint, required input types, relevant limits, applicable group, currency, and billing unit. Confirm account access before planning a benchmark. This article maintains no price leaderboard and promises no particular model’s availability. Do not assume every model in a family supports every row. Filter for required capabilities before comparing speed or price.

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Define a repeatable evaluation

Choose a small, representative set of sanitized examples and agree on pass criteria before reading model outputs. Include difficult cases and the response expected when information is missing. Keep prompts and tool definitions consistent where possible; record any model-specific adjustments instead of silently giving one candidate more help. Run the same inputs more than once because generated answers can vary. Record accepted results, timeouts, retries, response length, and latency. For interactive features, distinguish time to first output from time to a complete, usable answer. Evaluate tool workflows across all turns, not just the first response. Public benchmarks can inform a shortlist but cannot replace this application check.

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Connect a candidate and inspect usage

  1. Choose a chat-capable candidate and copy its catalog ID into ROCK_API_MODEL.
  2. Create a platform key at API keys, store it as ROCK_API_KEY, and confirm any balance requirements. Buyers do not need to configure upstream channels.
  3. Use the complete SDK base URL https://rockapi.ai/v1, or the full curl endpoint below. Begin with a minimal request, then replay the evaluation inputs.
Both environment variables must be set locally. Check status and body, then locate the matching dashboard usage record. Diagnose access or request failures with errors and rate limits before interpreting a failed request as poor model quality.

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Compare the cost of useful results

Use pricing and billing to distinguish token categories, per-call charges, dynamic tiers, and group multipliers. Do not double-count cached tokens or apply an already included multiplier again. Include retries, multi-turn tool calls, and human review in your operational estimate.
If no result passes, the candidate has not met your quality bar; a low unit price does not rescue that outcome. Report sample size and conditions with the result, rather than treating it as a universal ranking. Select a candidate that meets quality, latency, budget, and security requirements. Keep another option only after testing its behavior too. Re-evaluate when model versions, prices, prompts, or service terms change, and use the migration checklist before moving application traffic.