GPT-6 Sol

GPT-6 Sol is OpenAI's cost-efficient high-end model, announced September 22, 2026 alongside GPT-6 Luna and sitting below the flagship GPT-6 Astra and above the fast GPT-6 Luna. The API id is gpt-6-sol. OpenAI trained it with the same methods as Astra and passed the savings on: $2 per 1M input tokens and $10 per 1M output tokens, a 50% cut against GPT-5.6 Sol's promotional pricing. Context is 1,050,000 tokens with up to 128,000 output tokens, and reasoning effort runs from none to max.

See GPT-5.6 Sol for the model this replaces, or Claude Opus 5.5 for Anthropic's same-day answer.

Key Features

• Frontier-adjacent at half the price: OpenAI frames GPT-6 Sol as distributing Astra's advances rather than as a new ceiling. Astra still wins on raw capability; Sol wins on cost per task.
• Effort scale from none to max: none, low, medium (default), high, xhigh, and max. Chat Completions supports function calling only when effort is set to none; use the Responses API for built-in tools.
• 1.05M context with a 272K surcharge: Prompts above 272K input tokens are billed at 2x input and cache rates and 1.5x output for the whole request, so cache the stable prefix.
• Cheaper caching: Cached input reads cost $0.20 per 1M tokens, a 90% discount. OpenAI also added a prompt caching dashboard and explicit cache breakpoints.
• Day-one Copilot support: GitHub lists GPT-6 Sol as GA in its supported-models table, alongside GPT-6 Luna and GPT-6 Astra.

Model Specifications

Pricing

From OpenAI's GPT-6 Sol model card, Standard text tokens:

Batch and Flex are billed at 50% of Standard. Fast mode is 2x. Regional processing adds a 10% premium where available, and EU data residency requires Standard processing.

Use Cases

• Long-horizon coding agents: OpenAI reports DeepSWE v1.1 at 68.8% for GPT-6 Sol at max effort, within 1.1 points of Claude Fable 5's top 69.9%, at roughly 80% lower cost per task.
• Computer-use workflows: On OSWorld 2.0 offline, GPT-6 Sol at xhigh reaches 60.5% versus 60.3% for Claude Opus 5 at medium effort, again at about 80% lower cost.
• Business automation: On AutomationBench, GPT-6 Sol at xhigh outperforms Claude Opus 5 at max effort while costing about 9% of Opus 5 per task. That competitor row ran with fallback models disabled.
• High-volume production traffic: A $0.20 cached-input rate makes chat, classification, and agent loops affordable at scale.

Limitation: cheaper per token does not mean cheaper per outcome on every workload. Community testing in r/codex noted GPT-6 Sol posts lower scores than GPT-5.6 Sol on some individual benchmarks while leading on others, so measure your own tasks before cutting over.

Getting Started

1. Set model to gpt-6-sol on the Responses API.
2. Start at medium effort, which is the default, then raise to xhigh or max only for hard tasks.
3. Use Chat Completions only if you need plain text, and set reasoning_effort to none if you also need function calling there.
4. Add explicit cache breakpoints for the stable prefix of long prompts to stay under the 272K surcharge.

Frequently Asked Questions

How is this different from GPT-5.6 Sol?

OpenAI lists the same task profile at half the price with better scores on coding, computer use, and professional work. GPT-5.6 Sol remains available at its own rates.

Is it available in ChatGPT and Codex?

OpenAI says GPT-6 Sol and Luna rolled out to ChatGPT Work and Codex for Plus, Pro, Business, Enterprise, and Edu users starting September 22, 2026. The models are not available in Chat, and OpenAI warned the rollout would take most of the day.

Does it replace GPT-6 Astra?

No. OpenAI says Astra remains its best model across the board and recommends it when you want the best possible result without compromise. Sol is the value tier.

Alternatives

• GPT-6 Astra: OpenAI's flagship when you need the top result.
• GPT-6 Luna: the cheaper, faster sibling for focused high-volume work.
• Claude Opus 5.5: Anthropic's same-day flagship, ranked first on Artificial Analysis intelligence.

Tips

1. Compare cost per task, not cost per token. OpenAI's own comparisons lean on cost per task, and those numbers depend on effort and verbosity.
2. Cache aggressively. At $0.20 per 1M cached tokens versus $2 uncached, reused context is where the savings compound.
3. Re-check Copilot model lists. GitHub already lists GPT-6 Sol as GA, but per-plan availability still varies.

Conclusion

GPT-6 Sol brings GPT-6-era coding, computer use, and professional work to a $2/$10 price point with a 1.05M context window and effort control up to max. Start from the official announcement and the API model card, then measure your own cost per task against Astra and Claude Opus 5.5.