GPT-6.1 Sol vs GPT-6 Sol Pro: One Is a Model, the Other Is a Setting

The short answer is that GPT-6.1 Sol and GPT-6 Sol Pro are not two models competing for the same slot, and comparing their benchmark scores is a category error: GPT-6 Sol Pro is not a separate model at all. It is GPT-6 Sol with reasoning.mode set to pro in the Responses API — the same identifier, gpt-6-sol, the same rate card, the same 1,050,000-token window, doing more model work before it returns a final answer and billing the extra tokens at standard rates. GPT-6.1 Sol is a genuinely separate deployment with its own identifier, gpt-6.1-sol, and a cached-input rate that is half of GPT-6 Sol's. So the real question is not which is smarter. It is whether a brand-new model with a 50% cache discount beats an execution mode on an older model whose pro configuration Open​AI has not yet documented for the new tier.

Two different kinds of thing

Start with what each name actually resolves to when you put it in a request. "GPT-6 Sol Pro" resolves to gpt-6-sol plus a mode parameter. "GPT-6.1 Sol" resolves to gpt-6.1-sol — a distinct snapshot on a distinct model page, with no mode parameter mentioned anywhere on that page. That asymmetry is the whole comparison, and it is why the spec list below has almost nothing in it that is a fair fight.

• What it is — GPT-6.1 Sol is a separate model deployment vs GPT-6 Sol Pro is gpt-6-sol with reasoning.mode: "pro"
• Identifier you send — gpt-6.1-sol vs gpt-6-sol
• Input price — $2.00 per million tokens vs $2.00 per million tokens; identical, and pro mode carries no surcharge on the Sol rate card
• Output price — $10.00 per million tokens for both; pro mode's additional reasoning tokens bill at this rate
• Cached input — $0.10 per million on 6.1 Sol vs $0.20 per million on Sol, in either mode — the only line where the choice is free of trade-offs
• Cache writes — $2.50 per million for both
• Context — 1,050,000 tokens and 128,000 max output for both
• Reasoning effort — low, medium (default), high, xhigh, max, with none unsupported on 6.1 Sol vs the same ladder plus none on Sol, and pro mode independent of effort
• Tool calling — Responses API on both; Chat Completions without tools on 6.1 Sol, whereas Sol supports function calling in Chat Completions only with effort set to none
• Latency — no published figure for either; pro mode is slower by construction, since it performs more work before the final answer
• Cost per task — unpublished for pro mode, and not meaningfully comparable anyway, because it depends on how much extra work pro mode performs on your task

Read that list and notice the shape of it: every row is either identical, or a comparison between a documented value and an undocumented one.

What pro mode actually buys, and what it costs

Open​AI's description of pro mode is short and worth quoting rather than paraphrasing, because the vagueness is the point: it is "a Responses API execution mode that applies more model work to a request before returning a single final answer," it can improve reliability on difficult tasks, it increases latency, and it "aggregates the tokens from that work in reported usage," billed at the selected model's standard token rates. The vendor's own guidance on when to use it is unusually conservative for a launch document — pro mode is for cases where "a marginal quality improvement materially affects the outcome," and standard mode is preferred "for routine, latency-sensitive, or high-volume work, and whenever your evaluations do not show a meaningful gain from pro mode."

What Open​AI does not publish is the multiplier. There is no per-task figure, no range, and no pro mode entry on the Sol rate card. The cost arrives entirely as volume, visible in the usage object under reasoning tokens that are billed as output and never returned in the response body. At $10.00 per million output tokens an extra 10,000 tokens per task costs a cent, so the decision is rarely about the headline; it is about whether the extra work changes your result. That is a measurement, not a lookup, and it is the one thing about this pairing a reader can settle without waiting for anyone's benchmark.

The gap in the documentation that decides this matchup

GPT-6.1 Sol's model page — the one carrying its context size, pricing block, effort ladder, tool list and snapshot list — does not mention reasoning.mode, pro mode, or standard mode anywhere. The prose guide that covers pro mode still frames the feature as working with "any GPT-5.6 model" and tells developers to keep their selected model and set reasoning.mode to pro rather than switching to a separate Pro slug. We checked both pages, and we could not confirm from Open​AI's documentation that a pro-mode configuration exists for the 6.1 tier. It may work; the parent GPT-6 guide lists pro mode among the capabilities the family carries forward. But "may work" is not a thing to put a production path on, and it is the honest state of the record as of September 30, 2026.

That gap produces a genuinely lopsided decision. If you want pro mode today, the documented home for it is GPT-6 Sol — and the cost of choosing it is that you pay $0.20 rather than $0.10 for cached input on every reused prefix, plus whatever the pro mode's extra work adds, against a model whose vendor-reported scores are behind 6.1 Sol's on every task family Open​AI published. If you want the 6.1 tier's cache rate and its benchmark position today, you are giving up a documented pro configuration. There is no row where you get both, because nobody has told us whether the second one exists.

Settling it on your own traffic in an afternoon

The measurement is unglamorous and it takes one experiment, not a benchmark suite. Take a task set that represents your difficult work, run it three times, and read the usage object each time: once on gpt-6-sol at medium effort in standard mode, once on gpt-6-sol at medium effort with pro mode enabled, and once on gpt-6.1-sol at medium effort. Hold effort constant across all three — the whole point is to isolate one variable at a time. Compare task success, latency, and total billed tokens. The pro-mode run's token total against the standard-mode total is your multiplier on your traffic; it will not match anyone else's, because the design is that the amount of extra work scales with the difficulty of the request.

Two practical notes for running it. First, the 6.1 run and the Sol run are the same request body with one string changed, so the experiment is cheap to set up and easy to keep as a regression test. Second, if the pro-mode call on the 6.1 identifier returns an error rather than a result, you have your answer about the documentation gap for free — and you have learned it before putting it anywhere near production.

Both configurations, one key

This is the kind of comparison that costs more in operational overhead than in tokens, which is where routing stops being a footnote. OrcaRouter serves GPT-6 Sol at Open​AI's own list price with zero markup, so the $2.00 / $0.20 / $10.00 rate card above — including the 272K repricing rule — is passed through exactly as the vendor lists it. That means the three arms of the experiment described above can run through one endpoint with one API key and no second contract: the two Sol configurations differ by a parameter, and the 6.1 tier slots in beside them the moment it is routable. Until it is, the model you can call is the one whose pro mode is documented. For hard, low-volume work where a marginal quality gain changes the outcome, pro mode on Sol is what the vendor's own guidance recommends; for everything that is high-volume or latency-sensitive, the standard configuration is both faster and, on reused prefixes, now twice as expensive per cached token as the newer tier. A routing rule that splits traffic along that line — pro mode for the requests that earn it, a cheaper configuration for the bulk — is a parameter change in a routing DSL rather than a re-architecture, and it is the version of this decision that survives the next model refresh.

Who should pick which, concretely. If you have already measured a reliability gain from pro mode on your own tasks, stay where you measured it and wait for Open​AI to document the 6.1 equivalent before moving — the cache saving is real but it is cents on a prefix, and re-measuring a quality gain costs more than the discount is worth on low-volume work. If you have never measured pro mode at all, the 6.1 tier is the better starting point: it is the newer model, its vendor-reported results lead on every task family Open​AI published, its cached input is half the price, and the pro question can be revisited when the documentation catches up. Neither choice is wrong today. What is wrong is assuming they are alternatives, when one of them is a checkbox on the other.