GPT-5.6 Luna Pro
Cost-efficient GPT-5.6 model for fast, high-volume workloads
- Context
- 1.1M
- Released
- 3mo ago
- Input / 1M tokens
- $0.20
- Output / 1M tokens
- $1.20
- Cached input / 1M tokens
- $0.02
- Cache write / 1M tokens
- $0.25
Sources:models.devOpenRouter
Benchmarks
DeepSWE is the primary coding benchmark where an exact model evaluation exists. Other sourced evaluations appear below; missing scores are not zero.
Not evaluated in DeepSWE 1.1 as of 2026-09-22. Results are only shown for exact model versions in the official dataset. Official leaderboard.
No benchmark results have been matched to this model yet.
Specifications
- Developer
- OpenAI
- Model family
- GPT
- Type
- Language model
- Predecessor
- —
- Successor
- —
- Released
- July 9, 2026
- Model ID
openai/gpt-5.6-luna-pro- Openness
- Closed
- License
- —
- Input modalities
- file, image, text
- Output modalities
- text
- Modality
- text+image+file->text
- Tokenizer
- GPT
- Max output tokens
- 128K
- Knowledge cutoff
- 2026-02-16
- Hugging Face
- —
- Source freshness
- Metadata synced Oct 8, 2026, 10:17 PM UTC
Additional source evidence
- Source model
- openai/gpt-5.6-luna
- Source family
- gpt-luna
- Last updated
- 2026-07-09
- Knowledge cutoff
- 2026-02-16
- Model context
- 1.1M
- Maximum input
- 922K
- Model output limit
- 128K
- Open weights
- No
- File attachments
- Yes
- Reasoning
- Yes
- Tool calling
- Yes
- Structured output
- Yes
- Temperature control
- No
Published benchmarks
| Benchmark | Score | Evaluation details | Evidence |
|---|---|---|---|
| SWE-Bench Pro | 62.7 · resolve rate | 2026-07-09 | Source |
| Terminal-Bench | 84.7 · success rate | 2.1 · 2026-07-09 | Source |
| DeepSWE | 67.2 · resolve rate | 1.1 · 2026-07-09 | Source |
| GPQA Diamond | 92.3 · accuracy | 2026-07-09 | Source |
| FrontierMath | 78.6 · accuracy | Tier 1-3 · v2 · 2026-07-09 | Source |
| BrowseComp | 83.3 · accuracy | 2026-07-09 | Source |
| OSWorld | 45.6 · success rate | 2.0 · 2026-07-09 | Source |
| MMMU Pro | 78.4 · accuracy | no tools · 2026-07-09 | Source |
| Agents' Last Exam | 50.3 | 2026-07-09 | Source |
| Toolathlon | 53.4 · success rate | 2026-07-09 | Source |
| Artificial Analysis Intelligence Index | 51.2 · index score | max · 4.1 · 2026-07-09 | Source |
| Artificial Analysis Coding Agent Index | 74.6 · index score | Codex · max · 1.1 · 2026-07-09 | Source |
Complete source record (JSON)
{
"id": "openai/gpt-5.6-luna",
"name": "GPT-5.6 Luna",
"description": "Cost-efficient GPT-5.6 model for fast, high-volume workloads",
"family": "gpt-luna",
"last_updated": "2026-07-09",
"knowledge": "2026-02-16",
"attachment": true,
"reasoning": true,
"tool_call": true,
"structured_output": true,
"temperature": false,
"modalities": {
"input": [
"text",
"image",
"pdf"
],
"output": [
"text"
]
},
"limit": {
"context": 1050000,
"input": 922000,
"output": 128000
},
"benchmarks": [
{
"name": "SWE-Bench Pro",
"score": 62.7,
"source": "https://openai.com/index/gpt-5-6/",
"metric": "resolve rate",
"date": "2026-07-09"
},
{
"name": "Terminal-Bench",
"score": 84.7,
"source": "https://openai.com/index/gpt-5-6/",
"metric": "success rate",
"version": "2.1",
"date": "2026-07-09"
},
{
"name": "DeepSWE",
"score": 67.2,
"source": "https://openai.com/index/gpt-5-6/",
"metric": "resolve rate",
"version": "1.1",
"date": "2026-07-09"
},
{
"name": "GPQA Diamond",
"score": 92.3,
"source": "https://openai.com/index/gpt-5-6/",
"metric": "accuracy",
"date": "2026-07-09"
},
{
"name": "FrontierMath",
"score": 78.6,
"source": "https://openai.com/index/gpt-5-6/",
"metric": "accuracy",
"dataset": "Tier 1-3",
"version": "v2",
"date": "2026-07-09"
},
{
"name": "BrowseComp",
"score": 83.3,
"source": "https://openai.com/index/gpt-5-6/",
"metric": "accuracy",
"date": "2026-07-09"
},
{
"name": "OSWorld",
"score": 45.6,
"source": "https://openai.com/index/gpt-5-6/",
"metric": "success rate",
"version": "2.0",
"date": "2026-07-09"
},
{
"name": "MMMU Pro",
"score": 78.4,
"source": "https://openai.com/index/gpt-5-6/",
"metric": "accuracy",
"variant": "no tools",
"date": "2026-07-09"
},
{
"name": "Agents' Last Exam",
"score": 50.3,
"source": "https://openai.com/index/gpt-5-6/",
"date": "2026-07-09"
},
{
"name": "Toolathlon",
"score": 53.4,
"source": "https://openai.com/index/gpt-5-6/",
"metric": "success rate",
"date": "2026-07-09"
},
{
"name": "Artificial Analysis Intelligence Index",
"score": 51.2,
"source": "https://artificialanalysis.ai/articles/gpt-5-6-has-landed",
"metric": "index score",
"variant": "max",
"version": "4.1",
"date": "2026-07-09"
},
{
"name": "Artificial Analysis Coding Agent Index",
"score": 74.6,
"source": "https://artificialanalysis.ai/articles/gpt-5-6-has-landed",
"metric": "index score",
"harness": "Codex",
"variant": "max",
"version": "1.1",
"date": "2026-07-09"
}
],
"release_date": "2026-07-09",
"open_weights": false
}Infrastructure
Provider availability
| Provider | Endpoint | Context | Input /M tokens | Output /M tokens | Cached input /M tokens | Cache write /M tokens | Uptime 1d | Data handling |
|---|---|---|---|---|---|---|---|---|
| openai/flex | 1.1M | $0.10 | $0.60 | $0.01 | $0.13 | — | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| openai/flexBatch pricing | 1.1M | $0.10 | $0.60 | $0.01 | $0.13 | 100.00% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| azure | 1.1M | $0.20 | $1.20 | $0.02 | $0.25 | 99.35% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| azureBatch pricing | 1.1M | $0.20 | $1.20 | $0.02 | $0.25 | 99.33% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| openai | 1.1M | $0.20 | $1.20 | $0.02 | $0.25 | 99.93% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| openaiBatch pricing | 1.1M | $0.20 | $1.20 | $0.02 | $0.25 | 99.93% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| azure/eu | 1.1M | $0.22 | $1.32 | $0.02 | $0.28 | 100.00% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| azure/euBatch pricing | 1.1M | $0.22 | $1.32 | $0.02 | $0.28 | 100.00% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| openai/fast | 1.1M | $0.40 | $2.40 | $0.04 | $0.50 | — | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| openai/fastBatch pricing | 1.1M | $0.40 | $2.40 | $0.04 | $0.50 | — | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 |
Provider data:OpenRouter
Text provenance
EU · product-scopedOpenAI textGrain watermarking
In the EU, ChatGPT-generated text uses textGrain. OpenAI says API customers can opt in globally for select models, so watermarking depends on the product surface and configuration.
Official explanation