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 May 2026 pricing. The bars are dollars per month at the stated request volume.
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, May 2026. All numbers exclude prompt caching (90% saving on cached input for Anthropic), batch (50% saving on most providers), and committed-use discounts.