GPT-6 Sol vs Luna: Security, Cost, Routing

Last updated: October 2026

GPT-6 Sol vs Luna: which one should you deploy?

GPT-6 Sol is OpenAI's mid-tier model for complex coding and agent work at $2 per million input tokens, while GPT-6 Luna is the high-volume tier at $0.10. Route simple, repeatable tasks to Luna, send hard agent work to Sol or GPT-6.1 Sol, and enforce that routing in one gateway.

This guide compares OpenAI's GPT-6 Sol, GPT-6 Luna and GPT-6.1 Sol on cost, security and routing, and shows how they sit under GPT-6 Astra. We ran no tests. For Astra's cyber capability, read our GPT-6 Astra analysis for CISOs. For Grok's API prices next to OpenAI's, see Grok vs ChatGPT.

TL;DR: Key Takeaways

• Luna is 20 times cheaper per token, but only 12.6 times cheaper per benchmark run. Input costs $0.10 against $2, yet Artificial Analysis measured $48 against $605 to run its Intelligence Index at high effort, because Luna writes more tokens (Artificial Analysis, 2026).
• The price cut did not raise independent scores. Artificial Analysis calls both models level with GPT-5.6, and Sol lost about 100 Elo on GDPval-AA (Artificial Analysis, 2026). At max effort it scores Sol 48 and Luna 38 (Artificial Analysis, 2026).
• GPT-6.1 Sol costs the same as Sol but is treated as Critical in cybersecurity. On ExploitBench Internal Port it scores 21.5% against 5.5% for GPT-6 Sol, vendor-reported (OpenAI, 2026).
• Prompt injection resistance is close across tiers. OpenAI reports instruction hierarchy defender success of 99.97% for Sol and Luna and 99.99% for GPT-6.1 Sol (OpenAI, 2026).
• Cheaper code carried more flaws per line. SonarSource found 285 vulnerabilities per million lines from GPT-6 Sol against 178 for Astra (SonarSource, 2026).

Sol, Luna and Astra at a glance

Sources: OpenAI API docs, OpenAI, OpenAI, OpenAI, OpenAI Help Center.

What are GPT-6 Sol and GPT-6 Luna?

GPT-6 Sol and GPT-6 Luna are OpenAI models released on September 22, 2026 that bring methods from GPT-6 Astra to cheaper tiers. Sol targets everyday professional work with more reasoning, while Luna targets speed and volume. Both list at about half the price of their GPT-5.6 predecessors (OpenAI, 2026).

The prior GPT-5.6 prices were $4 and $20 for Sol and $0.20 and $1.20 for Luna. OpenAI says Sol makes about half as many factual mistakes as GPT-5.6 Sol on its internal evaluation, a vendor-reported claim (OpenAI, 2026).

Where GPT-6.1 Sol and Astra fit

OpenAI released GPT-6.1 Sol on September 29, 2026, describing it as near-Astra performance at lower cost (OpenAI, 2026). It keeps Sol's $2 and $10 price but halves cached input to $0.10. It does not accept none reasoning effort, while Sol and Luna do (OpenAI, 2026). Astra remains the flagship at $10 and $50.

Where Sol and Luna run: API, Work and Codex

In the API, the model IDs are gpt-6-sol, gpt-6-luna and gpt-6.1-sol. In ChatGPT, Sol and Luna run in Work and Codex for Plus, Pro, Business, Enterprise and Edu users, but not yet in Chat. Free and Go users can try Luna in the desktop app (OpenAI, 2026).

Both support function calling, web search, computer use and MCP in the Responses API (OpenAI, 2026). Rate limits favor volume on Luna: at Tier 5 it allows 30,000 requests and 180M tokens per minute, against 15,000 and 40M for Sol (OpenAI, 2026).

GPT-6 Sol vs Luna cost: price, caching and cost per task

Luna is 20 times cheaper than Sol per token, but cost per task is closer to 12 times, because Luna writes more tokens and scores lower. Model the full cost of a task, including retries and escalations, before you route a workload.

Pricing mechanics that change the bill

• Prompt caching: cached reads cost 10% of the input rate, and 5% on GPT-6.1 Sol. Cache writes cost 1.25 times input, and entries last 30 minutes (OpenAI, 2026).
• Batch: a 50% discount on standard rates (OpenAI, 2026).
• Long prompts: above 272K input tokens, the whole request costs 2 times input and 1.5 times output (OpenAI, 2026).
• Fast mode: 2 times standard rates. Regional processing adds 10%. Web search costs $10 per 1,000 calls plus tokens (OpenAI, 2026).

Cost per task: our arithmetic

The table uses OpenAI list prices from the sources above, and the token counts are illustrative, not measured. We used 50K output tokens for Luna because Artificial Analysis recorded 51K against 31K for Sol per task. Astra is left out of the agent row because OpenAI says it often needs fewer tokens (OpenAI, 2026), so equal counts would mislead.

The routing row shows the main lever. Sending everything to Sol costs $4,000, while Luna first and Sol on 20% of items costs $1,000, a 75% saving if Luna's pass rate holds. Measure your own escalation rate, because each failure adds Sol cost.

Artificial Analysis found the same pattern at high effort. Luna needed $48 to run its Intelligence Index and scored 33, while Sol needed $605 and scored 42 (Artificial Analysis, 2026). That is about $1.45 per index point for Luna and $14.40 for Sol, our arithmetic.

Benchmarks and speed: vendor-reported against independent

OpenAI's own results show Sol and Luna close on coding and computer use, while independent scores show a clear intelligence gap. Treat vendor figures as best-case, since OpenAI picks the effort setting for each, and plan with the independent ones.

Sources: OpenAI, OpenAI, Artificial Analysis, Artificial Analysis, SonarSource.

OpenAI says GPT-6.1 Sol matches Astra on DeepSWE at roughly one-fifth of the cost and comes within 2.1 points on OSWorld 2.0 at about one-seventh (OpenAI, 2026). We found no independent confirmation yet.

Artificial Analysis also reports regressions: Sol dropped about 100 Elo and Luna about 75 on GDPval-AA, a knowledge-work evaluation (Artificial Analysis, 2026).

Speed and latency

At high effort, Luna generated 134 tokens per second against 94 for Sol, with a 14.19-second time to first token against 15.42 (Artificial Analysis, 2026). At max effort the time to first answer token reached 96 seconds for Luna and 108 for Sol, so reserve max for batch work (Artificial Analysis, 2026).

How we compared

We ran no tests. We read OpenAI model, pricing and safety pages, plus Artificial Analysis and SonarSource, between September 22 and October 5, 2026. Effort settings differ by row, so compare tiers within a row.

Security and governance for Sol and Luna in production

Cheaper tiers are not lower-risk tiers. OpenAI reports similar prompt injection resistance across Sol and Luna, but a secondary reading of its data shows weaker honesty about tool failures on Luna, and GPT-6.1 Sol raises the cyber class at the same price. Each model needs its own controls.

What OpenAI reports

Sources: OpenAI addendum, OpenAI PDF, OpenAI, Kingy AI for the Luna figure.

OpenAI states it treats GPT-6.1 Sol as Critical in cybersecurity, as it did for Astra, and uses the same safeguards stack, with advanced cyber work gated through its Daybreak program (OpenAI, 2026). The addendum we retrieved does not state a level for GPT-6 Sol or Luna. Check the appendix in Astra's system card before you classify them.

Three findings matter for deployment:

• Upgrading Sol to GPT-6.1 Sol changes your risk class. The price is identical, but one offensive-security score rose nearly fourfold, so review access policies first.
• Luna's tool-failure honesty needs a check. The 28.7% figure is a secondary transcription of OpenAI's chart, so verify it, then test your own tools.
• Cheaper code needs scanning. SonarSource, on Java tasks at medium effort in September 2026, found GPT-6 Sol at 285 vulnerabilities per million lines against Astra's 178, and 2 blocker-severity issues per million lines (SonarSource, 2026).

OpenAI cautions that its alignment evaluations "deliberately test challenging situations and do not measure failure rates in typical use" (OpenAI, 2026). According to OWASP (2025), indirect prompt injection arrives through websites and files, and mitigations include least privilege and human approval for high-risk actions. Model scores do not replace either control, and our GPT-5.6 system card analysis found function-calling resistance weaker than connector resistance on the previous generation.

Data retention and residency

OpenAI says API data is not used for training unless you opt in, and abuse-monitoring logs are kept up to 30 days. Zero Data Retention is available on eligible endpoints, including Responses and Chat Completions, and then store is always treated as false (OpenAI, 2026).

OpenAI lists residency in ten regions, including the US, Europe and the UK. The model-specific note we found covers EU residency for GPT-6.1 Sol, GPT-6 Sol and GPT-6 Luna on Standard, Flex and Batch. Fast mode is unavailable with EU residency, and regional processing adds 10% (OpenAI, 2026).

How NeuralTrust addresses this

Routing between tiers is a policy decision, so it needs enforcement outside the model. Agent Gateway (TrustGate) sits between your agents and OpenAI's API, so teams can switch models without rewriting agents, apply inline prompt inspection and PII redaction, and keep a record of every call (NeuralTrust).

• Runtime inspection: Agent Runtime Security (TrustGuard), part of the Runtime Security Mesh, inspects tool calls and outputs at run time, whichever model produced them. Details are on the agent security page.
• Red team each model separately: AI Red Teaming (TrustTest) attacks LLMs and agents with adversarial techniques and runs in CI, so a move from Sol to GPT-6.1 Sol triggers a new test run (NeuralTrust).
• Posture management: Agent Posture Management (TrustLens) tracks agent posture, so you can find agents pinned to a model tier your policy no longer allows (NeuralTrust).

Which should you choose? Routing by workload

Start with the cheapest tier that passes your own evaluation, and escalate by rule, not by habit. Luna fits repeatable, well-specified tasks, Sol and GPT-6.1 Sol fit multi-step agent work, and Astra fits cases where your tests show a gap.

Pick GPT-6.1 Sol over Sol for new agent work unless you need none reasoning effort, since the price matches and cached reads cost half as much. Pin versions in code, because GPT-6.1 Sol arrived a week after Sol and Luna. At launch, Sol and Luna were not in ChatGPT Chat, so employees meet them only in Work and Codex.

Conclusion

For GPT-6 deployments, use Luna for volume, Sol or GPT-6.1 Sol for agent work, and Astra only where your tests demand it. Luna is 20 times cheaper per token, yet independent data puts the real saving near 12 times and shows a 10-point intelligence gap. Treat each model as its own risk, route through a gateway, and red team every change.

Secure Sol, Luna and Multi-Model Agents in Production with NeuralTrust

Route every OpenAI tier through one policy layer, and test each model against your own threats first.

Related Comparisons

• GPT-6 Astra: Security Implications for CISOs
• Grok vs ChatGPT 2026: Benchmarks, Pricing, Security
• Claude vs ChatGPT (2026): Benchmarks, Pricing & Verdict
• Perplexity vs ChatGPT: 2026 Benchmark & Pricing

FAQs about GPT-6 Sol and Luna

1. What is the difference between GPT-6 Sol and GPT-6 Luna?

Sol is the mid-tier model for complex coding, computer use and professional work at $2 and $10 per million input and output tokens. Luna is the fastest, cheapest tier at $0.10 and $0.50, aimed at focused, high-volume tasks. Both share a 1.05M-token context window and 128K output limit (OpenAI, 2026).

2. How much do GPT-6 Sol and GPT-6 Luna cost?

GPT-6 Sol costs $2 per million input tokens, $0.20 cached and $10 output. GPT-6 Luna costs $0.10, $0.01 cached and $0.50. Batch processing takes 50% off, and prompts above 272K input tokens cost 2 times input and 1.5 times output (OpenAI, 2026).

3. Is GPT-6 Luna good enough for production?

For classification, extraction and routine support, often yes. Luna scored 66.6% on DeepSWE against Sol's 68.8% in OpenAI's own results, but 38 against 48 on Artificial Analysis's Intelligence Index at max effort. Test on your data, set an escalation rule, and monitor tool-failure honesty (OpenAI, Artificial Analysis, 2026).

4. Is GPT-6.1 Sol better than GPT-6 Sol?

OpenAI says so for agentic coding: 6.4 points higher on DeepSWE v1.1, with the same $2 and $10 price and cached input at $0.10. It is also treated as Critical for cybersecurity, so review access policies before switching (OpenAI, 2026).

5. Are GPT-6 Sol and Luna safe for regulated data?

They can meet residency and retention needs, but the model alone is not the control. OpenAI offers EU residency for both, Zero Data Retention on eligible endpoints, and no training on API data by default. Add redaction, access control and logging outside the model (OpenAI, 2026).

6. Which GPT-6 model should I use for coding agents?

Start with GPT-6.1 Sol, which OpenAI says matches Astra on DeepSWE at about one-fifth of the cost, and escalate hard tasks to Astra. SonarSource found GPT-6 Sol had more vulnerabilities per line than Astra, so scan generated code (SonarSource, 2026).

About the Author

Roger Howroyd is Head of Global SEO and AI at NeuralTrust, where he leads the company's search strategy across SEO, AEO, GEO, and LLM optimization. He specializes in AI-powered search, content strategy, and SEM. Connect on LinkedIn.

NeuralTrust is the leading platform for securing and scaling AI agents. Named a Pioneer in the Gartner Emerging Market Quadrant for AI Application Security 2026, recognized across four Gartner Hype Cycle reports in 2026, and featured in the Gartner Market Guide for Guardian Agents 2026, the Gartner Market Guide for AI Gateways 2025 and the KuppingerCole Leadership Compass for Generative AI Defense 2025. Headquartered in Barcelona with offices in London and New York. ISO 27001 certified.

Sources

1. OpenAI, Introducing GPT-6 Sol and Luna, September 22, 2026.
2. OpenAI, Introducing GPT-6.1 Sol, September 29, 2026.
3. OpenAI API docs, GPT-6 Sol model page.
4. OpenAI API docs, GPT-6 Luna model page.
5. OpenAI API docs, GPT-6.1 Sol model page.
6. OpenAI API docs, GPT-6 Astra model page.
7. OpenAI API docs, Pricing.
8. OpenAI API docs, Latest model guide.
9. OpenAI API docs, Data controls.
10. OpenAI API docs, Prompt caching.