As of August 20, 2026, GPT-5.6 Luna API pricing is $0.20 per million input tokens and $1.20 per million output tokens. A typical 100K-in / 20K-out request costs $0.04. Source: https://developers.openai.com/api/docs/pricing
Source: OpenAI API pricing (https://developers.openai.com/api/docs/pricing)
Input $/M
$0.20
Output $/M
$1.20
Blended / 1M
$1.40
100K / 20K request
$0.04
| Context Window | 1.1M |
|---|---|
| Max Output | 128K tokens |
| Release Date | 2026-07-09 |
| Knowledge Cutoff | 2026-02-16 |
| License | Proprietary |
| Throughput | 48.45216448188169 tok/s |
| GPQA | 92.3% |
|---|---|
| GPQA Diamond | 63.6% |
| SWE-bench Verified | 93% |
| DeepSWE | 67.2% |
| DeepSWE 1.1 | 67.2% |
| SciCode | 52.5% |
| Arena Code Elo | 1522.94 |
| Vibe Code Bench | 77.1% |
| BrowseComp | 83.3% |
| SimpleQA | 41.7% |
| FinanceAgent v2 | 55.0% |
| ARC-AGI 2 | 59.5% |
| ARC-AGI-1 Verified | 34.2% |
| Terminal-Bench 2.1 | 84.7% |
| ToolAthlon | 53.4% |
| MMMU-Pro | 78.4% |
| MMMU-Pro (With Tools) | 79.5% |
| FrontierMath | 78.6% |
| Graphwalks BFS (0K-128K) | 81.3% |
| Graphwalks BFS (256K-1M) | 51.2% |
| AIME 2025 | 40% |
| MMMU | 85.0% |
| Agents' Last Exam | 50.3% |
| ARC-AGI-3 | 0.2% |
| Arena Vision Elo | 1249 |
| Artificial Analysis Index | 51 |
| AutomationBench | 14.9% |
| Benchcad | 63.1% |
| Benchcad With Python Tool | 73.9% |
| Big Finance Bench | 36% |
| Capture The Flag Challenges | 85.2% |
| Code Migration | 44.5% |
| CorpFin v2 | 64.2% |
| CritPt | 20.6% |
| CyberBench | 83.9% |
| EMB | 67.1% |
| Exploitbench | 33.2% |
| Exploitgym | 12.4% |
| FrontierCode | 39.8% |
| FrontierCode 1.1 | 39.8% |
| Frontiermath Tier 4 V2 | 58.5% |
| GDP-PDF | 22.7% |
| Genebench Pro | 10.8% |
| Graphwalks Bfs 128k | 81.3% |
| Graphwalks Bfs 1m | 51.2% |
| HealthBench | 55.8% |
| HealthBench Consensus | 95.1% |
| HealthBench Hard | 32% |
| HealthBench Professional | 55.7% |
| IOI | 72.9% |
| Kernelgen 1p | 22.4% |
| Lifescibench | 51.2% |
| Management Consulting Tasks | 35.4% |
| Medchembench | 30.4% |
| MedCode | 42.4% |
| MedScribe | 84.4% |
| MMLU-Pro | 86.0% |
| MRCR v2 | 41.3% |
| Mrcr V2 8 Needle 512k 1m | 41.3% |
| Nanogpt | 1.7% |
| Openai Connectors | 99.9% |
| Openai Search Function Calling | 89.7% |
| OSWorld 2.0 | 45.6% |
| Posttrainbench Lite | 29.6% |
| ProofBench | 54% |
| Rsi Index | 41.9% |
| SAGE | 44.2% |
| Sec Bench Pro | 48.9% |
| SimpleBench | 46.8% |
| SkillsBench | 60.5% |
| SWE-bench Pro | 62.7% |
| Tax Eval v2 | 76.2% |
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As of August 20, 2026, GPT-5.6 Luna OpenAI API pricing is $0.20 per million input tokens and $1.20 per million output tokens. Input tokens are the prompt you send; output tokens are the completion. Long answers therefore cost more than short ones even when the prompt stays the same. Source: https://developers.openai.com/api/docs/pricing
On this page the blended GPT-5.6 Luna API cost is $1.40 for 1 million input tokens plus 1 million output tokens. That 1:1 mix is the comparison scale used across the LLM pricing table. A 3:1 mix (three million input tokens per one million output tokens), which some calculators use, would be about $1.80.
A typical GPT-5.6 Luna API request with 100K input tokens and 20K output tokens costs about $0.04 at current rates as of August 20, 2026. Multiply that by daily or monthly volume to estimate spend. Caching, batch APIs, and volume discounts from OpenAI can bring the real invoice lower than the public list rate at https://developers.openai.com/api/docs/pricing.
GPT-5.6 Luna supports a 1M token context window and can generate up to 128K output tokens per request. The context window is the total prompt plus conversation history the model can see in one call. Larger windows cost more when you fill them, because every input token is billed per million tokens.
No. GPT-5.6 Luna is a proprietary model by OpenAI. The only public way to call it is OpenAI's API, billed per million input and output tokens. There is no self-host option for the weights.
GPT-5.6 Luna currently reports GPQA 92.3%, GPQA Diamond 63.6%, SWE-bench Verified 93%. See the performance table for the full eval set. Use those scores with the token prices above when you are choosing between a cheaper LLM and a frontier model. GPQA, SWE-bench, and HLE are the evals people usually compare first.
GPT-5.6 Luna scores 93% on SWE-bench Verified. Pair that score with OpenAI API pricing on this page: a slightly worse SWE-bench model can still win if it is much cheaper per million tokens. Open the SWE-bench or coding leaderboard from the sidebar for the full ranking, then jump into an LLM comparison against a coding specialist.
GPT-5.6 Luna has a published knowledge cutoff of 2026-02-16. Facts after that date are not guaranteed to be in the weights. For current events you still need retrieval or tools, which adds input tokens and therefore API cost per million tokens.
Use the Compare picker on this page to start an LLM comparison. Search by model or provider, pick a second model, and AnotherWrapper opens a dedicated GPT-5.6 Luna vs page with per-million-token prices, blended 1M-in / 1M-out cost, context window, throughput, and benchmarks. You can also open any model from the sidebar, or go back to the LLM pricing table to sort the full index.
The cheapest LLM alternative depends on whether you optimize for blended token cost, coding evals, or context window. Start with the popular matchups on this page, then sort the LLM pricing table by blended cost to see which hosted APIs undercut GPT-5.6 Luna. Open-weight models can be cheaper still if you self-host, but that is not the same as OpenAI API pricing on this page.
GPT-5.6 Luna is called through OpenAI's API with an API key. Bill against input and output tokens separately; cache or shorten prompts if spend is high. If you are shipping a full product, AnotherWrapper includes pre-built API routes and templates for OpenAI and 5+ other providers with auth, payments, and deployment already configured.
This GPT-5.6 Luna page tracks the latest published OpenAI API pricing from OpenAI, quoted per million tokens. List rates can differ from invoices because of prompt caching, batch discounts, committed-use tiers, and private contracts. Official rate sheet: https://developers.openai.com/api/docs/pricing. Benchmark scores come from official publications and independent evals, not from a private lab run by AnotherWrapper.
GPT-5.6 Luna has a measured throughput of about 48.45216448188169 tokens per second (tok/s). Throughput is how fast completions stream once the model starts generating; time-to-first-token can still lag under load. Faster APIs matter when you are building chat or agents, even if the per-million-token price is the same.
GPT-5.6 Luna is a proprietary model from OpenAI. Current API pricing is $0.20 per million input tokens and $1.20 per million output tokens, with a 1.1M token context window. A blended 1M-in / 1M-out workload costs $1.40.
Rank it on the LLM leaderboard, SWE-bench, and Humanity's Last Exam.
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