GPT-6 Sol vs GPT-6 Luna: Which Cheap OpenAI Model Should You Code With?

GPT-6 Sol is OpenAI's everyday workhorse model, and GPT-6 Luna is its fast, ultra-cheap sibling. Both launched on September 22, 2026, at half the price of the GPT-5.6 models they replace. Sol costs $2/$10 per million input/output tokens and became the default model in Codex; Luna costs just $0.10/$0.50 and is built for high-volume, low-latency work. A week later, OpenAI announced GPT-6.1 Sol at DevDay, pitched as "near-Astra capability at a fifth of the price".

If you vibe code with OpenAI tools, or you're adding AI features to an app you're building, these two models probably matter more to your wallet than the flagship GPT-6 Astra. For the bigger picture, see our October 2026 AI news roundup.

Side-by-side comparison

Sources: Simon Willison↗ (opens in a new tab), Vellum's benchmark breakdown↗ (opens in a new tab) and OpenRouter's Luna listing↗ (opens in a new tab). Benchmarks come from different sources and effort settings, so treat them as rough guides.

The striking number: Luna costs 20x less than Sol but scores only a couple of points lower on that coding benchmark. Benchmarks don't capture everything (Sol is noticeably better at long, multi-step agent work), but for many tasks Luna is "good enough" at a tiny fraction of the cost.

GPT-6 Sol: the workhorse

Vellum summarizes Sol as delivering "90% to 95% of Astra's practical capability at 20% of the cost per task". It's the model you'll use most often in Codex, and it's available in GitHub Copilot's model picker.

Use Sol for:

• Building features and fixing bugs in Codex or Copilot
• Multi-step agent tasks that need reliable tool use
• Code review on pull requests
• AI features in your app that need real reasoning (summaries, extraction, support bots)

What about GPT-6.1 Sol?

At DevDay on September 29, OpenAI announced GPT-6.1 Sol, an improved Sol at the same headline price of $2/$10. OpenAI's internal testing showed alignment on par with Astra. Note that early on, the API documentation continued to list the stable identifier as gpt-6-sol, so check OpenAI's model docs for the exact name to use. Read our DevDay recap for the rest of the announcements.

GPT-6 Luna: the speed demon

Luna is a lightweight, high-throughput model. It accepts text and images, has a context window over a million tokens, and a knowledge cutoff of May 18, 2026.

Use Luna for:

• Background loops: monitoring logs, triaging issues, labeling feedback
• High-volume app features: classification, tagging, autocomplete, search query rewriting
• Quick coding questions where latency matters more than depth
• Sub-tasks inside agents, like reading files or summarizing tool output

OpenAI positions Luna as the GPT-6 model for lighter, cheaper usage, including on lower ChatGPT tiers; check your plan's model picker for current availability.

Save money with model routing

The best way to use these models together is routing: send easy work to a cheap model and hard work to an expensive one. This is becoming common in AI coding tools, and you can do it yourself when building AI features:

import OpenAI from 'openai';

const client = new OpenAI();

// Cheap, fast model for simple, high-volume work
export async function tagFeedback(text: string) {
  const res = await client.responses.create({
    model: 'gpt-6-luna',
    input: `Classify this feedback as bug, feature_request or praise. Reply with one word.\n\n${text}`,
  });
  return res.output_text.trim();
}

// Stronger model only when the task needs reasoning
export async function draftBugReport(text: string) {
  const res = await client.responses.create({
    model: 'gpt-6-sol',
    input: `Turn this user feedback into a bug report with steps to reproduce:\n\n${text}`,
  });
  return res.output_text;
}

A few rules of thumb:

1. Start with the cheapest model that might work, measure quality, then move up only where needed.
2. Use caching. Cached input is 90% cheaper for both models; keep your system prompts stable so they cache.
3. Set spending limits in the OpenAI dashboard and per-user rate limits in your app. An unprotected AI endpoint is one of the fastest ways to get a surprise bill (see our security checklist).
4. Keep API keys on the server. Never call the OpenAI API directly from browser code.

Sol and Luna vs the competition

For a full quality-versus-cost comparison, see best AI model for coding in 2026.

Frequently asked questions

When were GPT-6 Sol and Luna released?

Both were released on September 22, 2026. GPT-6.1 Sol was announced at OpenAI DevDay on September 29.

Is GPT-6 Sol good enough for coding?

Yes. It's the default model in Codex and handles most everyday coding work well. Escalate to Astra or another frontier model for the hardest problems.

Is GPT-6 Luna good for coding?

For small, well-defined tasks and quick answers, yes, and it's remarkably cheap. For long agentic sessions, Sol or a frontier model is more reliable.

How much cheaper are they than GPT-5.6?

Both are about half the price of their GPT-5.6 predecessors.