GPT-6.1 Sol: benchmarks, pricing and comparison with Astra

These are results published by Artificial Analysis, not tests run by Rankerfox. The Intelligence Index is a composite index, not a percentage of correct answers. Cost is the average in US dollars for tasks in this API evaluation.

Row sources: GPT-6 Sol, Sol 6.1 high, Sol 6.1 xhigh, Astra and Opus 5.5.

At xhigh, Sol 6.1 sits one point behind Astra in this snapshot, with an average measured cost around 5.9 times lower. That is calculated from 2.31 / 0.39, not a promise of six times more subscription usage. Opus scores higher in the listed configuration. Identically named effort settings do not establish equal compute budgets across vendors.

For professional work, the meaningful measure is the cost of an accepted result. A draft requiring three revisions and extensive verification costs more than the initial model response. Conversely, choosing the most expensive model for every small change can waste budget. Our Claude model guide provides additional context.

The Artificial Analysis Coding Agent results compare these models inside Codex, including terminal tasks. Environment and effort settings remain important.

In this protocol, Sol 6.1 xhigh outperforms max. That supports testing settings rather than maximizing every slider; it does not prove superiority on every task. Without uncertainty intervals, a small score difference cannot establish a universal winner.

GitHub also reports fewer tokens and steps in early tests, alongside a gradual Copilot rollout. This is a related observation, not the same Codex evaluation shown above.

• Turn an export into decisions: connect queries, pages and conversions, flag inconsistencies, and produce a verification list. Calculations and record matching should be reproducible.
• Make a focused technical fix: explain the issue, propose a change, and check its effects. Ask for tests rather than just a persuasive answer.
• Develop source-based content: build an outline, compare arguments, and revise the draft. Provide reference pages so older model knowledge does not replace current facts.
• Work across several files: connect spreadsheets, notes and documents without losing units, dates or restrictions. Identify missing evidence before accepting a conclusion.

These are use cases to evaluate, not measured SEO scores. A coding benchmark does not prove that an article will rank higher in Google. Luna may be enough for repetitive, clearly scoped work; keep Astra or Claude available as a second opinion when the decision is difficult or an error would be costly.

Give each model the same small test set: identical files, instructions, tools and acceptance criteria. Include an anomaly to find in an export, a bug to fix, a document whose exceptions must survive the summary, and a draft to check against its sources.

1. Define success before testing: an exact calculation, passing tests, a correct citation, or explicitly identifying missing information.
2. Record the model and effort: do not silently compare a fast Sol configuration with a heavily reasoning Astra configuration.
3. Count rework: correction time, invented details, tool-use errors and necessary human interventions.
4. Repeat important cases: one striking success or isolated failure does not represent your whole workload.

Our practical starting point is an intermediate effort level, increasing it when the problem warrants it. Reserve the most demanding setting for work that genuinely benefits from extra reasoning.

OpenAI's standard rates are $2 per million input tokens and $10 per million output tokens for Sol 6.1, versus $10 and $50 for Astra. Sol 6.1 cached input costs $0.10, half GPT-6 Sol's rate.

These are API rates, not Rankerfox or ChatGPT subscription prices. Above 272,000 input tokens, the model documentation specifies higher rates for the full request. Context, reasoning, caching and tools can therefore change the actual bill.

ChatGPT Pro is included in Rankerfox Premium, alongside other Rankerfox AI tools. For Sol 6.1, the product matters: OpenAI offers it in ChatGPT Work and Codex, not the model picker for a regular ChatGPT conversation. Rollout depends on the plan and workspace settings, according to the official help page.

Your session's picker identifies the model actually accessible. The API rates and benchmark scores above do not establish an included Rankerfox message, token or task allowance. See Rankerfox plans for the offer and the ChatGPT Pro page for context on access.

Does Sol 6.1 remove the need for Astra?

Not always. Try it on recurring work; keep the flagship for cases where complexity, ambiguity or the cost of an error makes comparing responses worthwhile.

Should I always choose max effort?

No. The Codex results here show that a higher setting is not automatically more effective. Measure accepted quality and the revisions needed on your own examples.

Does an index of 51 mean 51% correct answers?

No. The Intelligence Index is a composite score. Terminal-Bench percentages, API cost and time per task are different measurements and should be read separately.

Are these results from an internal Rankerfox test?

No. The tables use the published evaluations linked above, checked on October 1, 2026. Practical recommendations are our interpretation, not a new in-house benchmark run.