Ultra-low-latency budget model engineered for high-frequency micro-tasks Need a token count first? Use the GPT token counter.
gpt-5.6-luna| Scenario | Input Tokens | Output Tokens | Uncached Cost | With Prompt Caching |
|---|---|---|---|---|
| Short Chat Query | 1,000 | 500 | $0.00080 | $0.00062 |
| Document Summarization | 10,000 | 2,000 | $0.00440 | $0.00260 |
| Codebase & Context Analysis | 100,000 | 20,000 | $0.0440 | $0.0260 |
| Batch Corpus Processing | 1,000,000 | 100,000 | $0.3200 | $0.1400 |
Tier 1 (free tier) reference limits — your account's actual quota may be higher.
Ideal for production workloads demanding budget capabilities, deep context depth (1.05M tokens), and reliability from OpenAI. Excellent when predictable tokenomics and prompt caching support are paramount.
If your use-case requires sub-second streaming latency or ultra-high frequency classification at micro-cent pricing, consider lighter budget options such as Gemini Flash-Lite or Claude Haiku. For deep formal logic, consider dedicated reasoning models like o3.
For GPT-5.6 Luna, 1 million input tokens costs $0.20, while 1 million output tokens costs $1.20. If using prompt caching, repetitive input prefixes are discounted to $0.02 per million.
GPT-5.6 Luna features a maximum context window of 1,050,000 tokens (~787,500 words), with a maximum output limit of 16,384 tokens per completion.
GPT-5.6 Luna utilizes the OpenAI o200k_base (200k vocabulary). Token counting on TokenMath runs client-side to ensure maximum privacy.
Yes. GPT-5.6 Luna supports prompt caching with a cached input rate of $0.02/1M (saving up to 90% on repeated input context).