When to use GPT-6.1 Sol and when to use GPT-6 Astra
OpenAI positions GPT-6.1 Sol as the new balance between capability and cost for important everyday work. Astra remains the choice for the hardest tasks, such as frontier scientific research.What changes compared with GPT-6 Sol
Complex documents, with tables and fine print
In the GDP.pdf evaluation, models answer real questions about work PDFs in finance, healthcare, legal, and seven other professional fields, with tables, charts, diagrams, and fine-print details. GPT-6.1 Sol scored above Claude Opus 5.5 at less than half the cost per task and came close to Astra at about one-fifth of the cost.Business processes from start to finish
In AutomationBench, agents run complete workflows with 47 tools across sales, marketing, operations, support, finance, and HR. GPT-6.1 Sol scored 2.2 points above Claude Opus 5.5 at about one-third of the cost, and 4.8 points above GPT-6 Sol at the same setting.Steadier at operating applications
In long computer-use tasks (OSWorld 2.0), GPT-6.1 Sol beat GPT-6 Sol by 7 points at less than half the cost. It came within 2.1 points of Astra, at about one-seventh of the cost per task.Fewer factual errors
On difficult questions where users had already flagged errors from earlier models, the share of answers with at least one factual error fell from 11.4% to 7.7%, a reduction of about 32% compared with GPT-6 Sol. Across all tested settings, the error rate stayed within 2 points of Astra’s.More transparent about what it couldn’t do
OpenAI reports that the model better respects the restrictions set by the user and more often flags when a tool isn’t working, instead of carrying on as if everything were fine.What the benchmarks show
OpenAI published most results as comparisons with other models, in quality and cost per task.
Artificial Analysis’s independent evaluation confirms how close it is to Astra.
GPT-6.1 Sol ranked 10th out of 222 models in the same price range, where the median is 26. The index combines ten evaluations, including real professional work, document analysis, and business processes.
The results in the table were published by OpenAI, measured in the company’s research environment or API. Numbers for other models came from public reports. Results depend on the tools and settings used in each measurement. Validate in your own workflow before changing a production setup.
Where it doesn’t lead
- Astra stays ahead on the hardest scientific tasks: on Terminal-Bench Science, it had the highest score among the models tested (68.1%), and OpenAI recommends Astra for this kind of research.
- On Artificial Analysis, running at maximum effort, the model took longer to start answering and generated text more slowly than the average for its range. For simple tasks, lower effort levels tend to be faster.
Where it creates the most value
A contract or report full of fine print
Contracts, expert reports, annual reports, and tenders hide deadlines, penalties, and conditions in tables and footnotes. GPT-6.1 Sol reads the whole document and answers based on what is written. Expected result: answers with the source page or clause and the points of attention highlighted.A process that spans several systems
A lead arrives in the CRM and needs a proposal, an email, and an entry in the tracking spreadsheet. A refund request moves through support, finance, and approval. The model runs the steps in sequence and asks for confirmation at critical points. Expected result: the process completed end to end, with every step recorded.An analysis that has to be right
Reports for leadership, answers to customers, and internal opinions can’t contain wrong information. With fewer factual errors, GPT-6.1 Sol reduces rework during review. Expected result: analysis with sources cited and gaps pointed out, instead of filled in by guesswork.How to apply it in Tess
1
Choose the model for the job
Use GPT-6.1 Sol for documents, business processes, and everyday analysis. Save GPT-6 Astra for the hardest work, when Sol’s quality still doesn’t close the deliverable.
2
Provide the material and the expected format
Send the documents, spreadsheets, and examples of the result. Say who the material is for and how it will be used.
3
Set tools and approvals
Enable only the tools the process actually uses. Require confirmation before sending, publishing, or changing data.
4
Ask for a check before delivery
Instruct the agent to verify numbers, clauses, and sources, and to say what it couldn’t confirm.
How to write the request
GPT-6.1 Sol performs best when the request works like an agreement, not a step-by-step script. Say what needs to come out, let the model choose the path, and define how the result will be checked.
Not every request needs every part. A simple question needs only result and context. An agent that changes systems needs clear limits and approvals.
Three playbooks to get started
1. Contract review
Input: contract PDF, the company’s points of attention, and an opinion template.Tools: file reading and document generation.
2. End-to-end sales follow-up
Input: list of opportunities in the CRM, proposal template, and sales policy.Tools: CRM, email, and spreadsheet integrations.
3. Invoice and payment reconciliation
Input: invoice PDFs, bank statement, and accounts payable spreadsheet.Tools: file reading, data analysis, and spreadsheet generation.
Limitations and best practices
- For the hardest tasks, especially frontier scientific research, GPT-6 Astra remains the safer choice.
- OpenAI maintains reinforced safeguards for cybersecurity and biology topics.
- A 1.05M-token context doesn’t replace selecting information. Send only the relevant content.
- The usage price is one-fifth of Astra’s, but the final cost of each task depends on how much the model works and how many revisions it needs. Compare both in your workflow by the cost of an approved result.
- Review numbers, clauses, and citations before publishing or sending.
- Keep human approval for financial, legal, clinical, or high-impact decisions.