OpenAI released GPT-6 Sol and GPT-6 Luna on September 22, 2026, expanding the GPT-6 family with two models aimed at very different production workloads. Sol is built for complex coding and agentic workflows, while Luna is optimized for focused, high-volume tasks where cost and throughput matter.
Both models offer a 1,050,000-token context window, up to 128,000 output tokens, text and image input, reasoning controls, function calling and structured outputs.
This guide compares GPT-6 Sol vs GPT-6 Luna across pricing, reasoning, context size, coding, AI agents and real-world deployment choices.
GPT-6 Sol vs GPT-6 Luna: Quick Comparison
| Feature | GPT-6 Sol | GPT-6 Luna |
|---|---|---|
| Model ID | gpt-6-sol | gpt-6-luna |
| Primary positioning | Complex coding and agentic workflows | Focused, high-volume workloads |
| Context window | 1,050,000 tokens | 1,050,000 tokens |
| Maximum output | 128,000 tokens | 128,000 tokens |
| Standard input price | $2 / 1M tokens | $0.10 / 1M tokens |
| Cached input | $0.20 / 1M tokens | $0.01 / 1M tokens |
| Standard output price | $10 / 1M tokens | $0.50 / 1M tokens |
| Reasoning effort | none → max | none → max |
| Image input | Yes | Yes |
| Function calling | Yes | Yes |
| Structured outputs | Yes | Yes |
The key difference is not context size. It is the capability-to-cost tradeoff. OpenAI currently positions GPT-6 Astra for the hardest end-to-end work, GPT-6 Sol for a balance of intelligence and cost, and GPT-6 Luna for cost-sensitive workloads at scale.
What Is GPT-6 Sol?
GPT-6 Sol is OpenAI’s GPT-6 model designed for complex coding, reasoning and agentic workflows. It is intended for work where a model needs to plan, use tools, inspect information and complete multi-step tasks rather than simply generate a short response.
- Software engineering agents
- Repository-level debugging
- Multi-step research
- Workflow automation
- Document and data analysis
- Tool-using AI agents
- Complex planning
Sol supports reasoning effort from none through max. That gives developers room to use lighter inference for straightforward tasks and more compute for difficult reasoning problems.
For the wider coding-agent landscape, see Vynula’s Best AI Coding Agents in 2026 guide.
What Is GPT-6 Luna?
GPT-6 Luna is OpenAI’s efficiency-focused GPT-6 model. OpenAI describes it as its most efficient model for focused, high-volume tasks.
- Classification and routing
- Data extraction
- Summarization
- Support-ticket processing
- Structured data generation
- Content tagging
- Repetitive agent subtasks
Luna is still a reasoning model. It supports the same reasoning-effort range as Sol, including high, xhigh and max. Its overall design and pricing, however, are optimized for efficiency.
GPT-6 Sol vs Luna Pricing
For standard API processing with prompts up to 272K input tokens, OpenAI currently lists the following rates per one million tokens:
| Model | Input | Cached Input | Cache Writes | Output |
|---|---|---|---|---|
| GPT-6 Astra | $10.00 | $1.00 | $12.50 | $50.00 |
| GPT-6 Sol | $2.00 | $0.20 | $2.50 | $10.00 |
| GPT-6 Luna | $0.10 | $0.01 | $0.125 | $0.50 |
On those standard rates, Luna’s listed input and output token prices are one-twentieth of Sol’s. For systems processing millions or billions of tokens, that difference can materially affect unit economics.
What Happens With Very Long Prompts?
Both Sol and Luna have a 1.05-million-token context window, but prompts above 272K input tokens use OpenAI’s higher long-context pricing. GPT-6 Sol rises to $4 per million input tokens and $15 per million output tokens, while GPT-6 Luna rises to $0.20 input and $0.75 output under standard processing.
A large context window is therefore a capability, not an invitation to send every available document with every request. Retrieval, caching and context selection remain important. See Context Engineering for AI Agents for a deeper explanation.
GPT-6 Sol vs Luna for AI Agents
Modern AI agents can reason about a goal, choose tools, search information, inspect files, execute code and repeat those actions across multiple steps. Both Sol and Luna can participate in that architecture.
Sol fits difficult planning and reasoning stages. A software engineering agent investigating a large codebase, for example, may need to reason across several files, test hypotheses and recover from tool failures.
Luna fits repetitive supporting stages. An agent can use it to classify documents, extract fields, summarize tool results, validate formatting or route requests.
A practical model-routing pattern: Luna → Sol → Astra. Keep routine work on the efficient model and escalate only when complexity justifies it.
GPT-6 Sol vs Luna for Coding
OpenAI explicitly positions GPT-6 Sol for complex coding and agentic workflows, making it the stronger starting point for repository-level debugging, architecture work, multi-file edits, migrations and autonomous software-development tasks.
Luna can still be useful in coding pipelines for repetitive work such as documentation generation, code classification, simple transformations, log summarization and metadata extraction.
GPT-6 Astra vs Sol vs Luna
- GPT-6 Astra: OpenAI’s most capable model for the hardest end-to-end work.
- GPT-6 Sol: strong reasoning and agentic capability with a lower price than Astra.
- GPT-6 Luna: the efficiency-focused option for cost-sensitive, high-volume tasks.
All three currently offer a 1.05M context window and 128K maximum output, so the main architectural question is how much model capability each stage of a workflow actually needs.
Are GPT-6 Sol and Luna Available in ChatGPT?
OpenAI announced GPT-6 Sol and GPT-6 Luna in ChatGPT Work and Codex on September 22, 2026. OpenAI’s release notes say these models are separate from the models available in ordinary Chat. Both models are also available through the API.
For a broader overview, see Which ChatGPT Model Should You Use in 2026? and ChatGPT Models Explained.
GPT-6 Sol and Luna API Model IDs
gpt-6-sol
gpt-6-lunaA minimal Responses API request for Sol looks like this:
from openai import OpenAI
client = OpenAI()
response = client.responses.create(
model="gpt-6-sol",
input="Analyze this software architecture and identify scalability bottlenecks."
)
print(response.output_text)For a focused high-volume workload, the model can be changed to gpt-6-luna. Production teams should evaluate both on their own tasks instead of relying only on generic benchmarks.
What Changed From GPT-5.6?
GPT-6 Sol and Luna extend the GPT-6 generation rather than simply renaming the GPT-5.6 lineup. OpenAI says the new models build on advances behind GPT-6 Astra while bringing those capabilities into faster and more affordable models.
For previous-generation context, read Vynula’s GPT-5.6 Sol, Terra and Luna guide.
Should You Use GPT-6 Sol or GPT-6 Luna?
Use GPT-6 Sol when your application needs stronger reasoning, difficult coding, multi-step planning or complex agent behavior.
Use GPT-6 Luna when your application handles large volumes of focused, repetitive work and token economics or throughput are major concerns.
For many production systems, using both can be more efficient than selecting a single model globally. A router can keep simple tasks on Luna and escalate complex tasks to Sol only when necessary.
Frequently Asked Questions
What is GPT-6 Sol?
GPT-6 Sol is an OpenAI reasoning model designed for complex coding, agentic workflows and demanding professional tasks.
What is GPT-6 Luna?
GPT-6 Luna is OpenAI’s efficiency-focused GPT-6 model for focused, high-volume workloads.
When were GPT-6 Sol and GPT-6 Luna released?
OpenAI released both models on September 22, 2026.
How much does GPT-6 Sol cost?
For standard processing with prompts up to 272K input tokens, OpenAI lists GPT-6 Sol at $2 per million input tokens, $0.20 per million cached input tokens and $10 per million output tokens.
How much does GPT-6 Luna cost?
For the same standard short-context tier, OpenAI lists GPT-6 Luna at $0.10 per million input tokens, $0.01 per million cached input tokens and $0.50 per million output tokens.
What is the GPT-6 context window?
Both GPT-6 Sol and GPT-6 Luna support a 1,050,000-token context window and up to 128,000 output tokens.
Is GPT-6 Sol better than GPT-6 Luna?
They target different workloads. Sol is positioned for more demanding coding and agentic reasoning, while Luna is optimized for efficient, high-volume work.
Official Sources
- OpenAI — GPT-6 Sol model documentation
- OpenAI — GPT-6 Luna model documentation
- OpenAI — model catalog and guidance
- OpenAI API pricing
- OpenAI API changelog
Last updated: September 24, 2026. Pricing and product availability can change; check OpenAI’s official documentation before making production purchasing decisions.
