AI Agent Reasoning & Chain-of-Thought Cost Calculator
Estimate the hidden cost of reasoning models and chain-of-thought tokens. Compare OpenAI o1/o3-mini, DeepSeek-R1, Gemini 2.5 Flash Thinking, and Claude 3.7 Sonnet Extended Thinking.
Scenario presets
Workload
Reasoning-token counts are hidden; use the explicit field once you have traces. Last updated: 2026-07-08. See notes.
Monthly reasoning cost
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Total monthly cost
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Premium vs standard
Projected 12-month cost
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Provider comparison for this workload
| Provider / Model | Type | Monthly cost | Cost per 1k requests | Best for |
|---|---|---|---|---|
| OpenAI o1 o1 | Reasoning | — | — | Hard STEM, coding, long reasoning |
| OpenAI o3-mini o3-mini | Reasoning | — | — | Cheap reasoning for coding and math |
| DeepSeek-R1 deepseek-reasoner | Reasoning | — | — | Open-weight reasoning at low cost |
| Gemini 2.5 Flash Thinking gemini-2.5-flash-thinking | Reasoning | — | — | Fast, cheap multimodal reasoning |
| Claude 3.7 Sonnet Extended Thinking claude-3-7-sonnet-20250219 | Reasoning | — | — | Balanced reasoning with long context |
| GPT-4o (standard) gpt-4o | Standard | — | — | Baseline non-reasoning comparison |
| Claude 3.5 Sonnet (standard) claude-3-5-sonnet-20240620 | Standard | — | — | Baseline non-reasoning comparison |
| Gemini 1.5 Pro (standard) gemini-1.5-pro | Standard | — | — | Baseline non-reasoning comparison |
Cheapest reasoning option
Enter a workload to see the cheapest reasoning provider.
Verdict
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Frequently asked questions
What are hidden reasoning or chain-of-thought tokens?
Reasoning models like OpenAI o1, o3-mini, DeepSeek-R1, and Claude 3.7 Sonnet Extended Thinking generate internal scratchpad tokens before returning the final answer. Providers usually bill these hidden tokens as output, so the cost of a 200-token final answer can be 10–50x higher than it looks.
How does the reasoning multiplier work?
The reasoning multiplier converts your expected visible output into estimated hidden CoT output. If you set 1,000 output tokens and a 5x multiplier, the calculator bills 5,000 reasoning tokens at the model's output rate plus 1,000 final-output tokens. You can also enter an explicit reasoning token count to override the multiplier.
When is a reasoning model worth the premium?
Reasoning models pay off when accuracy, code correctness, or multi-step logic directly impacts revenue or risk. For simple classification, summarization, or retrieval tasks, a standard LLM is usually cheaper and faster. The calculator shows the premium versus a standard baseline so you can decide.
Does this include cached input discounts?
Yes. Enter your cache-hit percentage and the calculator applies each provider's cached-input rate to that portion. Reasoning models from OpenAI and DeepSeek currently offer steep cached-input discounts.
How accurate are these estimates?
They are directional. Reasoning token counts vary by prompt, by problem difficulty, and by model temperature/settings. Use the explicit reasoning-token input once you have production traces. Last updated: 2026-07-08.
Reasoning-model prices and token ratios are directional estimates based on published provider pricing and community benchmarks. Hidden chain-of-thought (CoT) token counts vary widely by problem difficulty. Replace assumptions with your own traces for precise forecasts.