GPT 6 Sol

What is GPT 6 Sol?#

GPT 6 Sol is OpenAI's lower-cost GPT-6 model, released on September 22, 2026 alongside GPT 6 Luna. It runs on Unifically as openai/gpt-6-sol. OpenAI trained it with similar methods to GPT 6 Astra and describes it as a highly capable, lower-cost alternative to Astra for difficult work tasks. It takes text and image input, returns text, and works across a 1,050,000-token context window with a 128,000-token max output and knowledge up to April 20, 2026. Reasoning effort runs from none up to max. OpenAI's API price is half of what GPT 5.6 Sol costs under its current promotional pricing.

Key features of GPT 6 Sol#

Opus 5-level agent work for less

On Agents' Last Exam, which runs long professional workflows across 55 fields, Sol scores 56.4% at max effort. That is above Claude Opus 5's best score of 55.9% at 60% lower cost per task, and above GPT 5.6 Sol at 53.6%. At low effort it still reaches 48.7% for $0.86 a task.

Business workflows at 9% of the cost

AutomationBench chains multi-step business processes across apps. Sol at xhigh effort scores 33.2% for $0.27 a task, ahead of Claude Fable 5.1 at 31.4% and Claude Opus 5 at 26.9%, which costs $3.05 a task. GPT 5.6 Sol scores 28.8% there.

Half the factual errors of GPT 5.6 Sol

On OpenAI's set of difficult factual prompts, Sol answers with a factual error 4.5% of the time at xhigh effort against 8.4% for GPT 5.6 Sol. On Artificial Analysis the hallucination rate drops from 92% to 60%, partly because Sol declines more questions it cannot answer.

Faster and cheaper to run independently

Artificial Analysis scores Sol 47.5 on its Intelligence Index at max effort, level with GPT 5.6 Sol, while generating about 107 output tokens per second against 72.5 and costing $1.06 per index task against $1.99. It ranks 18th of 210 models.

Best for#

Use cases#

Make Sol the default model for a coding agent: plan a change, edit across a repo, run the tests, and review the diff, with effort raised only for the hard tickets. Run back-office automation that moves data between apps, fills forms, and drafts the summary at the end. Point it at a browser or desktop for research passes and QA. Load contracts, logs, or a whole codebase into the 1M-token window and ask for analysis with fewer invented facts than GPT 5.6 Sol. For teams already on Astra, move the routine agent steps to Sol and keep Astra for the tasks that need the top score.

Limitations#

Sol is cheaper, not stronger at the top end. At max effort GPT 5.6 Sol still scores higher on DeepSWE v1.1, 72.7% against 68.8%, and on OSWorld 2.0, 66.2% against 64.4%. On FrontierCode 1.1, Claude Opus 5 and Claude Fable 5.1 score above every Sol setting.

Artificial Analysis measured a drop of about 100 Elo on its GDPval knowledge-work eval, which it attributes to shorter deliverables that more often leave out required elements. OpenAI reports slightly lower HealthBench scores than GPT 5.6 Sol, with answers about 45% shorter. If you need long, complete documents, ask for the sections you need explicitly.

OpenAI rates Sol High for cybersecurity and biology risk, so it ships with the same safeguards as GPT 5.6, and some dual-use requests are refused. Sol and Luna are not on the Terminal-Bench 4.0 or LMArena boards yet.

Long prompts cost more. Once a request's prompt passes 272,000 tokens, the whole request bills at the long-context rate: double on input, cache read, and cache write, and 1.5x on output.

GPT 6 Sol vs GPT 6 Astra#

Astra remains OpenAI's best model and wins every head-to-head OpenAI published: 59.3% against 56.4% on Agents' Last Exam, 41.4% against 33.2% on AutomationBench, and 73.5% against 64.4% on OSWorld 2.0. Sol closes most of that gap for far less. On Agents' Last Exam it spends $2.93 a task against $6.23 for Astra, and on AutomationBench $0.27 against $1.73. Sol is also rated High for cybersecurity rather than Critical, so it runs behind lighter restrictions than Astra. Use Astra when the score is what matters; use Sol for everything else.

When to use GPT 6 Sol#

Use Sol as the everyday model for coding, agents, and analysis, where you want results close to the top of the chart at a fraction of the price. Step up to GPT 6 Astra for the hardest computer-use and workflow tasks, and drop to GPT 6 Luna for high-volume calls such as classification, extraction, and short replies.

API examples#

Call GPT 6 Sol from any language by POSTing to /v1/chat/completions, the OpenAI-compatible endpoint shared by every language model on the platform. Full parameter docs live at docs.unifically.com/models/llm/openai/gpt-6-sol.

curl -X POST https://api.unifically.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "openai/gpt-6-sol",
    "messages": [
      { "role": "user", "content": "Review this pull request and list any bugs you find, most serious first." }
    ]
  }'

The response comes back synchronously with the completion. Set "stream": true to receive tokens as they generate, and "reasoning_effort": "low" when you want a faster, cheaper answer.