# GPT-5.6 Luna ![Organization](https://img.shields.io/badge/Org-OpenAI-412991) ![Context](https://img.shields.io/badge/Context-1.1M%20tokens-1f6feb) ![Pricing](https://img.shields.io/badge/Price-%241%20%2F%20%246%20per%201M-2da44e) ![License](https://img.shields.io/badge/License-Proprietary-d29922) ![Modality](https://img.shields.io/badge/Modality-Text%20%2B%20Image-8957e5) ![Released](https://img.shields.io/badge/Released-Jul%202026-555) > **Quick answer:** GPT-5.6 Luna is OpenAI's most cost-efficient tier in the GPT-5.6 family, released July 9, 2026. It scores 88.0% on ARC-AGI and 50.3% on Agent's Last Exam — capable frontier-level performance at just $1 input / $6 output per 1M tokens. It shares the same 1.1M-token context window as Sol and Terra. ## At a Glance **Where GPT-5.6 Luna leads** - Lowest cost in the GPT-5.6 family: $1/$6 per 1M tokens — 5× cheaper than Sol - 88.0% ARC-AGI — strong abstract reasoning for a cost-optimised tier - 50.3% Agent's Last Exam — competitive with Terra on multi-step agentic tasks - AA Intelligence Index 51.2, Coding Agent Index 74.6 - Full 1.1M-token context window — same as Sol and Terra - Ideal throughput-per-dollar for high-volume production pipelines **Where it lags** - 8.5-point ARC-AGI gap vs Terra (96.5%) and 9.5 points vs Sol (97.5%) - No `max` or `ultra` reasoning effort modes - Lower ceiling on the highest-complexity reasoning and cybersecurity tasks - Closed API model with no open weights **Best for:** High-volume agentic pipelines, latency-sensitive applications, cost-constrained teams, and tasks where Terra or Sol capability is not required. ## What GPT-5.6 Luna Is GPT-5.6 Luna is the efficiency-first tier of OpenAI's GPT-5.6 family. It is designed for workloads where throughput, response speed, and cost per operation matter more than maximum capability. The Luna tier is a permanent capability band in the GPT-5.6 naming scheme — meaning it will improve independently as OpenAI updates the generation, without being replaced by a cheaper, weaker model. Despite its lower price point, Luna is a capable frontier model. An 88.0% ARC-AGI score places it well above most models released before the GPT-5.6 generation, and its 50.3% Agent's Last Exam score — within 0.1 points of Terra — suggests that on multi-step agentic tasks, the practical gap between Luna and Terra may be smaller than raw benchmark numbers suggest. For many real-world agent tasks (document processing, code generation, API orchestration), Luna delivers effectively equivalent results to Terra at 60% lower cost. Luna shares the GPT-5.6 family's full 1.1M-token context window, which distinguishes it from prior cost-tier models that typically imposed context constraints. This makes Luna a compelling option for long-context batch processing — summarising large document sets, running retrieval-augmented generation over full repositories, or analysing multi-session conversation logs — at a fraction of the per-run cost of Sol. ## Specifications | Field | Value | |---|---| | Organization | OpenAI | | Parameters | Undisclosed | | Context window | ~1,100,000 tokens | | Architecture | Undisclosed (GPT-5.6 family) | | License | Proprietary (API only) | | Release date | July 9, 2026 | | Knowledge cutoff | September 2025 (estimated) | | Modality | Text + Image (multimodal) | ## Pricing | | Input (per 1M tokens) | Output (per 1M tokens) | |---|---|---| | OpenAI API | $1.00 | $6.00 | Cache writes are billed at 1.25× the uncached input rate; cache reads receive a 90% discount. Pricing per [OpenAI pricing page](https://openai.com/api/pricing/) as of July 2026. ## Context Window GPT-5.6 Luna has a **~1.1M-token context window** — roughly **800–900 pages** of text in a single request. This is identical to Sol and Terra, making Luna suitable for long-document and full-codebase workloads at the lowest cost per run in the GPT-5.6 family. ## Public Benchmark Scores | Benchmark | Score | Source | Date | |---|---|---|---| | [ARC-AGI](/benchmarks/arc-agi) | 88.0% | Benchgen evaluation | 2026-07 | | [Agent's Last Exam](/benchmarks/agents-last-exam) | 50.3% | Benchgen evaluation | 2026-07 | *Per-variant scores currently available for ARC-AGI and Agent's Last Exam. Shared family scores (GPQA Diamond, TerminalBench, SWE-Bench Pro, etc.) are reported for GPT-5.6 Sol — see the [GPT-5.6 Sol page](/models/openai/gpt-5-6-sol) for the full benchmark table.* ## GPT-5.6 Luna vs Alternatives | Model | Context | ARC-AGI | Agent's Last Exam | Price (in/out per 1M) | |---|---|---|---|---| | [GPT-5.6 Sol](/models/openai/gpt-5-6-sol) | 1.1M | 97.5% | 52.7% | $5 / $30 | | [GPT-5.6 Terra](/models/openai/gpt-5-6-terra) | 1.1M | 96.5% | 50.4% | $2.50 / $15 | | **GPT-5.6 Luna** | 1.1M | 88.0% | 50.3% | $1 / $6 | | [GPT-5.5](/models/openai/gpt-5-5) | — | — | — | — | Luna's strongest case vs Terra: Agent's Last Exam scores are nearly identical (50.3 vs 50.4), suggesting Luna handles multi-step agentic tasks at near-Terra level — at 60% lower cost. The larger 8.5-point ARC-AGI gap indicates Luna may underperform on novel or highly abstract single-step reasoning problems. ## How GPT-5.6 Luna Performs on Real Agent Tasks Luna's 50.3% Agent's Last Exam score is notable in context: it sits within 0.1 points of Terra (50.4%) and only 2.4 points below Sol (52.7%). For agent builders, this means Luna is a viable production tier for the large category of tasks that don't require Sol's maximum reasoning ceiling — routine code review, document summarisation, structured data extraction, and API-driven automation workflows. Where Luna is most appropriate to evaluate first: high-volume pipelines where per-token cost directly affects unit economics; applications that run the same prompt type repeatedly (batch processing, RAG, classification); and prototyping phases where model spend should be minimised before committing to a higher tier. ## Use GPT-5.6 Luna via API ```python from openai import OpenAI client = OpenAI(api_key="YOUR_API_KEY") response = client.chat.completions.create( model="gpt-5.6-luna", messages=[{"role": "user", "content": "Summarise the key clauses in this contract..."}], ) print(response.choices[0].message.content) ``` ## Related Models & Benchmarks - Compare: [GPT-5.6 Sol](/models/openai/gpt-5-6-sol) · [GPT-5.6 Terra](/models/openai/gpt-5-6-terra) · [GPT-5.5](/models/openai/gpt-5-5) · [GPT-5.1 Instant](/models/openai/gpt-5-1-instant) - Benchmarks: [ARC-AGI](/benchmarks/arc-agi) · [Agent's Last Exam](/benchmarks/agents-last-exam) · [GPQA Diamond](/benchmarks/gpqa-diamond) ## Frequently Asked Questions

What is GPT-5.6 Luna?

GPT-5.6 Luna is the most cost-efficient tier of OpenAI's GPT-5.6 model family, released July 9, 2026. It is priced at $1/$6 per 1M tokens and delivers strong agentic performance — within 0.1 points of Terra on Agent's Last Exam — at 60% lower cost than Terra.

What is GPT-5.6 Luna's context window?

GPT-5.6 Luna supports approximately 1.1 million tokens of context — the same as Sol and Terra — roughly 800–900 pages of text in a single request.

How much does GPT-5.6 Luna cost?

GPT-5.6 Luna is priced at $1.00 per 1M input tokens and $6.00 per 1M output tokens via the OpenAI API, with a 90% discount on cached input reads.

Is GPT-5.6 Luna open source?

No. GPT-5.6 Luna is a proprietary, API-only model. There are no open weights available.

What is the difference between GPT-5.6 Luna and Terra?

Terra ($2.50/$15 per 1M) scores 96.5% on ARC-AGI vs Luna's 88.0% — an 8.5-point gap indicating Terra has a higher ceiling for complex reasoning tasks. However, on Agent's Last Exam (multi-step agentic performance), the gap is just 0.1 points (50.4% vs 50.3%), suggesting Luna is nearly equivalent for most production agent workflows at 60% lower cost.

What is GPT-5.6 Luna's knowledge cutoff?

GPT-5.6 Luna's training knowledge cutoff is estimated at September 2025.

--- *Specs and scores sourced from OpenAI's official GPT-5.6 announcement (July 9, 2026) and Benchgen evaluations; third-party benchmark scores attributed inline. Pricing cited to the OpenAI pricing page. Last updated 2026-07-21.*

GPT-5.6 Luna — Training jobs

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