GPT-Rosalind vs GPT-6.1 Sol: The Same Lab Sells You Two Doors Into Biology

GPT-Rosalind and GPT-6.1 Sol come from the same company, run through the same API surface, and are separated by a factor of two and a half on output tokens. Only one of them requires you to fill in a form. That is the whole comparison in a sentence, and the rest of this article is about why the sentence is less useful than it looks. GPT-Rosalind has existed since 16 April 2026 and GPT-6.1 Sol since 29 September 2026; what changed in October is that O​penAI started handing Rosalind to universities for free through ChatGPT Business seats while still withholding API access, which forces every lab to answer a question it did not have to answer in September: when do you pay 2.5× for a specialist, and when do you route the work to the cheap general model and accept the loss? When a specialist's access terms change after launch, the change is logged with its dates and sources in the AI Incident Archive, next to the other model and vendor incidents we track.

Two O​penAI models with different onboarding

Start with what each one asks of you before the first request, because that determines whether the price comparison is even available to you.

• Entry requirement — GPT-Rosalind: trusted-access review, institutional verification, no published service level. GPT-6.1 Sol: none beyond a billing account.

• Where it runs — GPT-Rosalind: ChatGPT, Codex and the API, for approved organisations. GPT-6.1 Sol: Plus, Pro, Business, Enterprise and Edu tiers in ChatGPT Work and Codex, plus the API — explicitly not the consumer Chat product.

• Academic route — GPT-Rosalind: up to 30 free ChatGPT Business seats for up to twelve months under the 15 October academic plan, with API access excluded. GPT-6.1 Sol: normal commercial terms only.

• What the gate is protecting — GPT-Rosalind: the model's biology capability. GPT-6.1 Sol: nothing; it is a general model and is sold to anyone with a card.

That last row is the one that matters for planning. A gate that exists because a model is dangerous on a specific subject tells you something about where its capability is concentrated. GPT-6.1 Sol does not carry that kind of gate, and the absence is informative: O​penAI's own positioning for it is agentic coding, computer use and document-heavy professional work, one tier below GPT-6 Astra. Biology is not in the pitch.

The rate card, in the only units that decide anything

Sticker prices per million tokens are the least useful way to compare a specialist against a general model, because the specialist is tuned to use fewer tokens and the general model is tuned to be cheap per token. Both effects are real, and they push in opposite directions. Here are the raw numbers first.

• Input — GPT-Rosalind $5.00 per 1M vs GPT-6.1 Sol $2.00. The specialist costs 2.5× as much.

• Output — GPT-Rosalind $25.00 per 1M vs GPT-6.1 Sol $10.00. Also 2.5×.

• Cached input — GPT-Rosalind $0.50 vs GPT-6.1 Sol $0.10. A 5× gap, and the one that compounds across a long agent session.

• Long prompts — GPT-6.1 Sol reprices a whole request above 272K input tokens at 2× input and cache rates and 1.5× output. GPT-Rosalind publishes no equivalent long-context surcharge.

• Context — GPT-Rosalind 1,000,000 tokens. GPT-6.1 Sol 1,050,000 tokens with 128,000 max output.

• Knowledge cutoff — GPT-6.1 Sol documents 30 April 2026. O​penAI does not publish a comparable cutoff for GPT-Rosalind.

Now the part the sticker price hides. Rosalind's own evaluation material reports token reductions as prominently as score gains: 7.2% fewer tokens on MedChemBench, 31% fewer on GeneBench, 5.3% fewer on LabWorkBench, and a headline figure of 4.42% higher performance per token on LifeSciBench. A 31% token saving on one benchmark does not close a 2.5× price gap. Nothing in O​penAI's published numbers does. The specialist premium is real and it is paid for accuracy, not efficiency.

What a science task costs on the general model

GPT-6.1 Sol's launch table includes one row that is directly relevant to a lab, and it is the only place O​penAI puts a cost per task rather than a cost per token. On Terminal-Bench Science 0.1, a set of scientific workflows, O​penAI reports GPT-6.1 Sol more than doubling GPT-6 Sol's score at maximum reasoning effort, at an average of $5.47 per task against $23.21 for Claude Opus 5.5 and $23.80 for Astra.

Two caveats before that number gets used in a budget. It is vendor-reported, like every other figure in that table, and the benchmark measures scientific workflows as a software problem — reading data, running tooling, producing a result — not as a domain-reasoning problem. It is closer to what a computational group actually does all day than to what a medicinal chemist does. Which makes it a fair estimate for the plumbing around the science and a poor estimate for the science itself.

That distinction is the practical answer to the whole comparison. GPT-6.1 Sol at $5.47 per task is the right instrument for the code that parses instrument output, the notebook that orchestrates a sweep, the literature triage before a meeting. GPT-Rosalind is the instrument for the question that needs a model tuned on structure-activity relationships to have an opinion.

Neither model has an independent score, and that is not symmetric

This is the part of the matchup almost every comparison skips. GPT-6.1 Sol launched one week after GPT-6 Sol, and at the time of writing Artificial Analysis has no entry for it. The company's own framing is that it "nearly matches" GPT-6 Astra at a fifth of the price, which is a positioning claim about a model that has not been independently scored. GPT-Rosalind has no independent score either, but for a different reason: specialist models are not tracked on the general leaderboard at all, so the absence is structural rather than temporary.

• Independent composite — Both absent. GPT-6.1 Sol is too new and unscored; GPT-Rosalind is out of scope for the board.

• Vendor benchmark suite — GPT-6.1 Sol: DeepSWE v1.1, GDP.pdf, AutomationBench 1.0.6, OSWorld 2.0 and Terminal-Bench Science 0.1, all reported as deltas against O​penAI's own models and against Claude Opus 5.5. GPT-Rosalind: MedChemBench, GeneBench, LabWorkBench, BixBench and LifeSciBench, all reported against GPT-5.5.

• Independent corroboration — None for either. Every number in the previous row was produced by the company selling the model.

What that means in practice is that you cannot rank these two models, and no third party is going to do it for you soon. The decision has to be made on task fit and access, not on a score comparison. Anyone who hands you a table putting Rosalind and Sol on the same axis has invented the axis.

The awkward middle: most labs need both queues

A research organisation does not run one kind of request. It runs a long tail of ordinary work — parsing, plotting, refactoring the analysis script, summarising the paper, drafting the methods section — and a short head of requests where the domain tuning is the entire point. The tail is where the tokens are. A single sequencing pipeline can spend more tokens on log parsing than on the biology question that motivated it.

Priced against that reality, the sensible configuration is not a choice between the two models; it is a split with a rule. The tail goes to the cheap general model, which is available today, needs no review, and costs $2.00 / $10.00 with $0.10 cached input. The head goes to the specialist, which costs 2.5× and is worth it for exactly the requests where a general model's answer would be plausible and wrong.

The failure mode to design against is silent drift between the queues. If the specialist key is rate-limited or the specialist route errors, a naive fallback chain will happily answer a biology question with the general model, and the output will look like a specialist answer. That is worse than an error, because an error is visible. The rule you want is: named route for the specialist, hard fail when it is unavailable, general model only for requests that were routed there deliberately.

Where OrcaRouter sits, and where it does not

GPT-6.1 Sol is on OrcaRouter at the provider's list price — $2.00 input, $10.00 output and $0.10 cached input per million tokens, with 0% markup, which means O​penAI's pricing changes reach our catalogue on the day they are published rather than at the next contract renewal. The 272K long-prompt repricing carries through as a tier on the same model entry, so a request that crosses the threshold is billed at the higher tier rather than surprising you on an invoice.

GPT-Rosalind is not on OrcaRouter, is not on any third-party router, and is reachable only through O​penAI's trusted-access programme. We are not going to blur that. What our side of the split buys you is the boring half: one key and one bill for GPT-6.1 Sol alongside 200+ other models, automatic failover so a provider hiccup moves a request instead of killing a run, and a routing DSL that lets you write the split-queue rule down as configuration rather than as a convention in a wiki. Model fusion is the other lever worth knowing about here — a panel that includes both a cheap general model and an expensive specialist, answering together, is a way to get a second opinion on a borderline result without paying specialist rates for every request.

How this resolves

Two things would collapse the ambiguity, and both are plausible within a quarter. An independent evaluation of GPT-6.1 Sol would tell you whether "nearly matches Astra" is true on tasks you care about; until then the claim is a positioning statement with a price attached. And any move by O​penAI to include API access in the academic plan would change the arithmetic inside universities overnight — free specialist capacity in a lab that currently has free chat seats and no pipeline access.

Absent both, the rule is about request shape rather than model quality. If a wrong answer costs you an experiment, a filing or a retraction, the specialist's 2.5× is cheap and the fact that it needs a review is a filter you should be glad exists. If a wrong answer costs you a re-run, GPT-6.1 Sol is available today at a fifth of Astra's price, with a 1.05M-token context, and no one has to approve you to use it.