Practical guide
LLM API Pricing Comparison by Cost per Successful Task
Compare OpenAI, Claude, Gemini, and DeepSeek models using token cost, retry rate, output length, and task success—not list price alone.
By StackCalcs · Updated · Sources checked
Compare the same evaluated task set
An LLM API pricing comparison is useful only when each model performs the same job. Run a representative evaluation set through each shortlisted model. Record uncached input, cached input, output, retries, and the number of acceptable results for each model.
Imagine 10,000 document extractions. Model A averages 2,200 input and 180 output tokens and succeeds 96% of the time. Model B averages 1,900 input and 350 output tokens and succeeds 88%, requiring 1,364 retries to obtain roughly 10,000 successful results. Use the LLM API Cost Calculator with each model’s average tokens per request and request count after retries, then divide spend by accepted outputs. A lower first-pass token price can lose once longer answers and rework are included.
Normalize the inputs before deciding
Keep system prompts, retrieval context, output limits, and quality thresholds consistent. Separate cached input because every listed model exposes a cached-input rate, but caching behavior, write premiums, and eligibility differ. Compare latency and rate limits alongside cost if missed deadlines have business consequences.
The calculator ranks a verified snapshot of current models on list price, including batch, long-context, and cache-write rates. It does not test quality or include tools, regional processing, commitments, taxes, or negotiated discounts, and vendors tokenize the same text differently. Re-run the comparison when a model version, prompt, or rate changes.
FAQ
Is the cheapest token rate the cheapest model?
Not necessarily. Output mix, cache use, retries, and success rate determine cost per completed task.
Can I compare providers with different context limits?
Yes, if every candidate can complete the representative task. If a model requires truncation or a different retrieval strategy, document that architecture change rather than treating the token totals as equivalent.
Official sources
- https://developers.openai.com/api/docs/pricing
- https://platform.claude.com/docs/en/about-claude/pricing
- https://ai.google.dev/gemini-api/docs/pricing