openai
OpenAI: GPT-6 Luna
openai/gpt-6-luna
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
openai/gpt-6-luna is listed in the OpenRouter catalog as OpenAI's GPT-6 Luna, a fast, cost-efficient member of the GPT-6 series positioned below GPT-6 Sol. The catalog describes it as intended for high-volume, latency-sensitive work such as chat, classification, and lightweight agentic tasks. It lists text, image, and file inputs with text output, optional reasoning across multiple effort levels, tool and structured-output support, and a 1,050,000-token context window.[1]
Strengths
- OpenRouter describes the model as fast and cost-efficient for high-volume, latency-sensitive workloads.[1]
- Text, image, and file inputs are cataloged, alongside text output and a 1,050,000-token context window.[1]
- The catalog lists configurable reasoning effort plus support for tools, tool choice, structured outputs, response format, seed, and verbosity.[1]
Limitations
- The source pack contains only an OpenRouter catalog entry; an independent model card was not available, and production fit should be validated on the intended workload.[1]
- Only text output is listed; image, file, audio, and other output modalities are not cataloged.[1]
- The catalog does not provide a knowledge cutoff, and its benchmark fields omit coding and agentic results.[1]
Where to use it
- Potentially suitable for high-volume chat and classification where catalog-described speed and cost efficiency are priorities.[1]
- May fit applications that need text responses from text, image, or file inputs and can benefit from a very large context window.[1]
- Could support lightweight agentic workflows using tool calls and structured outputs, subject to workload validation.[1]
Sources
- [1] OpenRouter catalog · OpenAI: GPT-6 LunaSources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
GPT-6 Luna is the fast, cost-efficient model in OpenAI's GPT-6 series, positioned below GPT-6 Sol. It is suited for high-volume and latency-sensitive workloads such as chat, classification, and lightweight agentic...
Specifications
- Model ID
- openai/gpt-6-luna
- Canonical ID
- openai/gpt-6-luna-20260922
- Creator
- openai
- Added to OpenRouter
- 22.09.2026
- Context length
- 1,050,000
- Modality
- text+image+file->text
- Input modalities
- file, image, text
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $0.100 / out $0.500
- Knowledge cutoff
- —
- Expiration date
- —
Tasks and capabilities
A grade appears only where the source supplies a matching measurement for that task, the model is active and the cohort holds at least 10 comparable models. The capability column lists facts the source declares — supported formats, reasoning and tool parameters — which are not measured quality.
| Task | Grade | Source-declared capability |
|---|---|---|
| General quality | BB — next 30% of the cohort | reasoning: optional · default medium · efforts: max, xhigh, high, medium, low, none · parameters: include_reasoning, reasoning, reasoning_effort |
| Coding | No measurement | — |
| Agents | No measurement | parameters: structured_outputs, tool_choice, tools |
| Writing | No measurement | output: text |
| Reasoning | No measurement | reasoning: optional · default medium · efforts: max, xhigh, high, medium, low, none · parameters: include_reasoning, reasoning, reasoning_effort |
| Document analysis | No measurement | input: file |
| Vision | No measurement | input: image |
| Image generation | No measurement | — |
| Audio | No measurement | — |
| Video | No measurement | — |
| Embeddings | No measurement | — |
Evaluations
| Source | Benchmark | Value | Grade | Data retrieved |
|---|---|---|---|---|
| Artificial Analysis | Intelligence index | 27.9 | BB — next 30% of the cohort | 13.11.2026 |
How grades work
Each letter grade compares a model only against other models scored by the same source and metric in the current snapshot. S means the top 10%, A the next 20%, B the next 30%, C the rest. Models without a published measurement are shown as not rated — a supported format alone never implies measured quality.
- S — S — top 10% of the cohort
- A — A — next 20% of the cohort
- B — B — next 30% of the cohort
- C — C — below the B band
Providers
Optional live provider statistics from OpenRouter. Missing values are unknown, not zero.
| Provider | Context length | Price in /1M | Price out /1M | Latency p50 (30m, s) | Throughput p50 (30m, tok/s) | Uptime (30m) |
|---|---|---|---|---|---|---|
| OpenAI | 1,050,000 | $0.050 | $0.250 | — | — | 100% |
| Amazon Bedrock | 1,050,000 | $0.100 | $0.500 | — | — | 100% |
| OpenAI | 1,050,000 | $0.100 | $0.500 | — | — | 100% |
| Azure | 1,050,000 | $0.100 | $0.500 | — | — | 100% |
| Azure | 1,050,000 | $0.110 | $0.550 | — | — | 100% |
| Azure | 1,050,000 | $0.110 | $0.550 | — | — | 100% |
| Amazon Bedrock | 1,050,000 | $0.110 | $0.550 | — | — | 100% |
| OpenAI | 1,050,000 | $0.200 | $1 | — | — | 100% |
Provider data retrieved: 13.11.2026
Related articles
- Amplitude's Wave agent uses OpenAI models to ship product improvements in one to two daysAmplitude says its proactive product agent Wave, powered mostly by OpenAI's GPT-6.1 Sol, GPT-6 Luna and GPT-Image-2.5 Flare models, helps teams ship product improvements in one to two days instead of roughly a month.
- Google DeepMind and Artificial Analysis Introduce MKA, a Benchmark for Visually Grounded Agent WorkMKA is a 200-task benchmark that measures whether AI agents can extract decisive evidence from blueprints, scans, and charts and turn it into professional deliverables.