AI Token Cost Calculator
Three common prompt scenarios (short chat, long document summary, agentic tool-use loop) costed across every major AI model using official September 2026 pricing. The bars are dollars per month at the stated request volume.
Put your own numbers in.
Monthly cost at a published rate, for the volume and prompt size you actually send.
Short customer-support chat
Median support ticket: 800 input tokens (system + ticket body), 250 output tokens (reply). 100K tickets/month.
The spread
Same workload. Gemini 2.0 Flash: $18.00/month. GPT-5.5 Pro: $6,900/month. That is a 383x cost ratio for the same prompt.
Based on official provider pricing as of 2026-05-06. Excludes prompt-caching, batch, and volume discounts. Self-hosted open-weight models (Gemma 4, Nemotron 3 Nano Omni) excluded from this view because their cost is infrastructure-driven, not token-priced.
Long document summary
40K input tokens (a 30-page PDF), 600 output tokens (executive summary). 5K runs/month.
The spread
Same workload. Gemini 2.0 Flash: $21.20/month. GPT-5.5 Pro: $6,540/month. That is a 308x cost ratio for the same prompt.
Based on official provider pricing as of 2026-05-06. Excludes prompt-caching, batch, and volume discounts. Self-hosted open-weight models (Gemma 4, Nemotron 3 Nano Omni) excluded from this view because their cost is infrastructure-driven, not token-priced.
Agentic tool-use loop
6K input tokens per turn (history + tool defs), 400 output tokens, 4 turns per task. 20K tasks/month.
The spread
Same workload. Gemini 2.0 Flash: $60.80/month. GPT-5.5 Pro: $20,160/month. That is a 332x cost ratio for the same prompt.
Based on official provider pricing as of 2026-05-06. Excludes prompt-caching, batch, and volume discounts. Self-hosted open-weight models (Gemma 4, Nemotron 3 Nano Omni) excluded from this view because their cost is infrastructure-driven, not token-priced.
How to read these visualizations
The same exact prompt sent to GPT-5.5 Pro versus DeepSeek V4 Flash can differ in cost by more than 200x. That spread is not quality. It is brand, infrastructure, and pricing strategy. Two of the three scenarios above show that even at the "expensive-end" of frontier models, the per-call cost is small; the magic happens at scale, where switching from GPT-5.5 Pro to DeepSeek V4 Pro on a 100K-call/month workload changes the annual line item from millions to tens of thousands.
The right tactic is not "always pick cheap"
For ~80% of production traffic, a mid-tier model (DeepSeek V4 Pro, Gemini 3.1 Pro, Claude Sonnet 4) is the right call. The remaining ~20% (high-stakes reasoning, ambiguous customer queries, agent planning) earns its keep at frontier prices. This is exactly the cascade and Mixture-of-Routers patterns described in our LLM routing deep-dive.
Related calculators
- Cheap vs Expensive Model Comparison : same prompt, two extremes
- Model-Mixing Cost Savings : what cascade and MoR actually save
- AI Model Leaderboard
- Vendor Lock-in Leaderboard
Pricing data sourced from official provider pages and OpenRouter, September 2026. All numbers exclude prompt caching (90% saving on cached input for Anthropic), batch (50% saving on most providers), and committed-use discounts.