LLM API · OpenAI
OpenAI GPT-5.6 Luna pricing
Input
$0.200 /Mtok
Output
$1.20 /Mtok
Cached input
$0.020 /Mtok
Batch discount
−50%
Context window
1,050k tokens
Max output
128k tokens
Tier
small
Ranked 10 of 29 by price among 29 LLM APIs in the index — the cheapest is Mistral AI Ministral 3 (3B) at $0.100. Figures are USD list prices excluding tax.
Price history
Every weekly reading since this item entered the index on 15 August 2026. Dots mark readings where the figure changed.
| Date | Figure | Reading | Change |
|---|---|---|---|
| 2026-08-15 | Cached input $/Mtok | $0.020 | — |
| 2026-08-15 | Input $/Mtok | $0.200 | — |
| 2026-08-15 | Output $/Mtok | $1.20 | — |
| 2026-09-05 | Cached input $/Mtok | $0.020 | — |
| 2026-09-05 | Input $/Mtok | $0.200 | — |
| 2026-09-05 | Output $/Mtok | $1.20 | — |
Machine-readable: history.json · Every movement also appears in the change log.
What it costs in practice
Computed from the current figures above. Change the assumptions in the LLM API cost calculator.
| Workload | Cost |
|---|---|
| One million input tokens | $0.200 |
| One million output tokens | $1.20 |
| Blended $/Mtok at 4:1 input:output | $0.400 |
| 1,000 chatbot replies (1,500 in / 300 out) | $0.660 |
| Summarising 1,000 ten-page documents (5,000 in / 400 out) | $1.48 |
| A month of 10,000 requests a day (2,000 in / 500 out) | $300.00 |
| 1,000 replies with a 1,200-token cached prefix (300 fresh in / 300 out) | $0.444 |
| Those 1,000 document summaries via the batch API (−50%) | $0.740 |
Compared with other models in the same tier
| Model | blended $/Mtok (4:1) | vs this |
|---|---|---|
| Llama 4 Scout Instruct via Fireworks AI | $0.240 | -40% |
| Mistral AI Mistral Small 4 | $0.240 | -40% |
| Groq GPT-OSS 120B | $0.240 | -40% |
| Qwen3 235B A22B Instruct via Together AI | $0.280 | -30% |
| Mistral AI Codestral | $0.420 | +5% |
| Cohere Command R (03-2024) | $0.700 | +75% |
| Google Gemini 2.5 Flash | $0.740 | +85% |
| Google Gemini 3.5 Flash-Lite | $0.740 | +85% |
How to read this page
The figures are the list prices published on the vendor's own pricing page, re-read every week by an automated check that never invents a number: if the page cannot be read, the previous reading is kept and the item is flagged after three missed weeks. Negotiated rates, volume tiers, free credits and regional pricing are not modelled. The methodology describes the process and its limits; the price index shows every llm api figure side by side.