GPT-5.6 Luna (batch)
OpenAI · released Jul 9, 2026
GPT-5.6 Luna is a fast, cost-efficient model in OpenAI's GPT-5.6 series. It is suited for high-volume, latency-sensitive tasks such as chat, classification, and lightweight agentic workflows, providing capable reasoning for...
Specification
- Context window
- 1.05M
- Max output
- 128K
- Knowledge cutoff
- Feb 16, 2026
- Parameters
- Undisclosed
- Licence
- Proprietary
- Serving providers
- 1
- Moderated
- Yes
Intelligence
52.3
96th percentile
Coding
71.4
Coding Index
Agentic
46.9
Agentic Index
Output speed
169 t/s
Median across providers
Latency
69.79s
Time to first token
Cost per task
$0.05
Estimated
Benchmarks
Where the score comes from
The Intelligence Index is a composite. These are the underlying evaluations this model was actually measured on.
Evaluation scores
Percentage correct · higher is better
- GPQA Diamond91.1%
- AA-LCR (long context)78.3%
- SciCode52.5%
- Humanity's Last Exam39.5%
- τ²-bench (Telecom)31.1%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| GPQA Diamond | 91.1% |
| AA-LCR (long context) | 78.3% |
| SciCode | 52.5% |
| Humanity's Last Exam | 39.5% |
| τ²-bench (Telecom) | 31.1% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- GPT-5.4OpenAI53.1
- GLM 5.2Z.ai52.6
- GPT-5.6 Luna (batch)OpenAI52.3
- GPT-5.6 LunaOpenAI52.3
- Gemini 3.5 FlashGoogle52.0
- V4 Flash 0731DeepSeek51.8
- V4 Flash 0423DeepSeek51.8
- Gemini 3.6 FlashGoogle51.6
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| GPT-5.4 | 53.1 |
| GLM 5.2 | 52.6 |
| GPT-5.6 Luna (batch) | 52.3 |
| GPT-5.6 Luna | 52.3 |
| Gemini 3.5 Flash | 52.0 |
| V4 Flash 0731 | 51.8 |
| V4 Flash 0423 | 51.8 |
| Gemini 3.6 Flash | 51.6 |
Percentile among all indexed models
Pricing
What it costs to run
List prices per million tokens, plus what one representative task works out to.
List price
- Input / 1M tokens
- $0.1
- Output / 1M tokens
- $0.6
- Cached input / 1M
- $0.01
- Blended 3:1
- $0.225
One task, estimated
$0.05
- Input tokens
- 50,000
- Output tokens
- 80,000
- Profile
- Reasoning
Estimated from list pricing: 50K input tokens plus 80K output tokens for reasoning models (25K for non-reasoning).
Serving providers
Speed and latency figures are medians across these providers, so a widely-served model reports a blend rather than any single endpoint.
Arena
Head-to-head generation quality
Elo from pairwise judgements, broken out by the kind of thing the model was asked to build.
- Overall Elo
- 1,285
- Win rate
- 50.4%
- Strongest at
- UI components
- Tournaments
- 611
Elo by category
Dot position on a 1,175–1,300 scale · Elo has no meaningful zero
- 1,285
- 1,275
- 1,242
- 1,236
- 1,178
Agent categories (full-stack apps, mobile apps) are only scored for models tested in agent harnesses.
View as table
| Category | Elo | Win rate |
|---|---|---|
| UI components | 1285 | 50.4% |
| Websites | 1275 | 50.9% |
| Data visualisation | 1242 | 48.8% |
| Game development | 1236 | 45.5% |
| 3D scenes | 1178 | 38.5% |