GPT-Rosalind vs Muse Wonder: What a 1M Context Buys You in a Biology Lab

Muse Wonder and GPT-Rosalind are the two models in this comparison that are closest on paper and furthest apart in purpose. Meta Superintelligence Labs released Muse Wonder on 12 October 2026 at $4.00 per million input tokens and $20.00 per million output, with a 1M-token context and an Artificial Analysis Index of 56 — which is the same price tier and the same context size as GPT-Rosalind, a model O​penAI has been selling since 16 April 2026 at $5.00 and $25.00. The five dollars and five dollars of difference are not the interesting part. The interesting part is that one of these models will answer any question you ask it, and the other will only answer questions from organisations that passed a review, and the second one is the only one of the two that has been tuned on the things a biology lab actually does. When a gated release opens up or a score is revised, the change is logged with its dates and sources in the AI Incident Archive, next to the other model and vendor incidents we track.

Same context window, different reason for having one

Both models carry a million tokens, and it is worth understanding that this is a coincidence of two unrelated design decisions rather than a shared capability.

Meta's reason is breadth. Muse Wonder takes text, image, speech and video in, returns text, and is built for what Meta calls personal and professional agentic tasks. Its sibling Muse Wonder Full is walled inside a Muse Code subscription — $50 a month for the Power Usage plan, $200 for Maximum Usage — which makes it a product decision rather than an API tier. A million tokens is what you need when a single agent session has to hold a screen recording, a document, a conversation history and a tool transcript at once.

O​penAI's reason is depth. GPT-Rosalind carries a million tokens so a lab can put a whole reference atlas, a protocol library and a dataset description in one pass. Its tool surface is a life-sciences plugin with more than fifty tools; its published benchmarks are chemistry and genomics; its gate exists because the model is judged good enough at biology to need one.

• Context window — Muse Wonder 1,000,000 tokens vs GPT-Rosalind 1,000,000 tokens. A genuine tie.

• Input modalities — Muse Wonder: text, image, speech and video. GPT-Rosalind: text and scientific file inputs through ChatGPT, Codex and the API; no general image-input claim published.

• Output — Text only, both.

• Price — Muse Wonder $4.00 input / $0.20 cached / $20.00 output per 1M vs GPT-Rosalind $5.00 / $0.50 / $25.00. The specialist costs 25% more on every line and 2.5× more on cache reads.

• Access — Muse Wonder callable today as muse-wonder-1.0 on the Meta Model API and inside Muse Code. GPT-Rosalind requires trusted-access review.

• Independent score — Muse Wonder 56 on the Artificial Analysis Index, v4.3.2. GPT-Rosalind: not tracked on that board.

That last pair is the crux. On every axis a procurement team can measure without doing science, Muse Wonder wins: it is cheaper, it is available without an application, it takes more input types, and it has an independent score. The only axis where GPT-Rosalind wins is the one no leaderboard measures, and it is the one this article is about.

The scores that are not comparable, stated plainly

Muse Wonder's 56 and Rosalind's 27.5% are not two readings of the same thing, and any table that puts them in adjacent columns is misleading. The 56 is an independent composite over ten general evaluations: AA-Briefcase, GDPval-AA, AutomationBench, Terminal-Bench, SciCode, Humanity's Last Exam, GDP.pdf and others. The 27.5% is O​penAI's own medicinal-chemistry benchmark, scored against GPT-5.5, unreproduced.

• Muse Wonder — 56 on the Artificial Analysis Index; Meta's own table reports Terminal-Bench 4.0-AA at 85.6, SWE-Marathon at 78.8, OSWorld V2.1 at 72.6, HealthBench Pro at 56.6 and DeepSWE 1.1 at 52.0. All of the Meta rows are vendor-reported; the index number is independent.

• GPT-Rosalind — MedChemBench 27.5% vs 25.1%, GeneBench 21.6% vs 20.4%, LabWorkBench 63.2% vs 55.8%, and a 4.42% performance-per-token gain on LifeSciBench. All O​penAI-reported, none independently reproduced.

• The one overlap worth noting — HealthBench Pro is a clinical-knowledge benchmark, and GPT-Rosalind publishes nothing on it. Meta's 56.6 there says nothing about whether Muse Wonder can design a perturbation experiment.

There is no public biology benchmark on which these two models have both been scored. That absence is the most important fact in the comparison, and it is not going to be fixed by either vendor.

What Muse Wonder Full does to the price comparison

Meta shipped two checkpoints on 12 October, and the split changes what "Muse Wonder" means depending on which door you come in through.

• Muse Wonder — muse-wonder-1.0, on the Meta Model API and inside Muse Code, $4.00 / $0.20 / $20.00.

• Muse Wonder Full — a separate, higher-scoring checkpoint that Meta says is available only to Muse Code subscribers. Not a flag on the API model; a different model behind a $50-to-$200 monthly product.

• The spread — Meta's own table shows Muse Wonder Full at 75.3 on JobBench against 30.8 for Muse Wonder, and 59.1 against 10.0 on Vals Finance Agent V2. A 44-point gap between two checkpoints from the same lab on one benchmark is either a genuine capability split or a harness artefact, and only a third party can settle which.

For a lab, the practical reading is that the model you can buy through an API and the model Meta publishes its best numbers for are not the same model. Anyone comparing "Muse Wonder" to GPT-Rosalind on the strength of Full's scores is comparing a subscription product to an API model.

Where a million tokens actually gets used

The context tie is the reason these two models get compared at all, so it is worth being concrete about what a lab does with a million tokens versus what a general agent does with them.

A general agent holding a million tokens is holding state: the conversation, the screenshots, the tool outputs, the current file. The context is a workspace. A biology workflow holding a million tokens is holding a reference: the atlas, the annotation tables, the protocol corpus, the prior literature. The context is a library.

Those are different workloads and they stress different things. A workspace rewards fast tool loops and cheap cache reads; a library rewards the ability to keep a large body of domain text in view and reason across it without losing the thread. Meta optimises for the first — Muse Wonder's Active Memory keeps state across a session, and Active Focus manages the model's own attention on long tasks, both features that only make sense for agentic use. O​penAI optimises for the second, which is why the published gains are on tasks where the model has to hold a protocol and a perturbation in mind at once.

• Cache economics — Muse Wonder $0.20 per million cached vs GPT-Rosalind $0.50. On a long agent session that re-sends a large system prompt every turn, the general model is 2.5× cheaper on the line that dominates.

• Session design — Muse Wonder's persistent state across sessions is the claim that most changes how you architect an application, and it is also the one a model card cannot demonstrate.

• Domain text — Neither vendor publishes a long-context retrieval benchmark for scientific text, so the tie is unverified in the direction that matters here.

The gate is the differentiator, not the benchmark

Here is the asymmetry that decides the procurement conversation. Muse Wonder is sold to anyone with a card. GPT-Rosalind is sold only to organisations that clear a review, and the review exists because O​penAI concluded the model's biology capability should not be available on demand. Since 11 September 2026 that gate has been open globally to eligible organisations rather than a named partner list; since 15 October there has also been an academic path that gives eligible labs up to 30 free ChatGPT Business seats for up to twelve months and explicitly excludes API access.

Two readings of that, and both are defensible. The cynical one: a gate is a tax on legitimate researchers that does nothing to stop anyone else. The useful one: a vendor that gates a model has told you where it thinks the capability is concentrated, and a vendor that sells a general model to everyone has told you the same thing by omission. Meta's published strengths for Muse Wonder are agentic work, coding and professional tasks. Biology is not in the pitch, and there is no gate because there is nothing to gate.

So the decision is not "which model is better." It is "does my request belong in the library or the workspace," and the answer is usually obvious once the question is asked in those terms.

Running the pair, and being honest about which half we serve

Neither Muse Wonder nor GPT-Rosalind is on OrcaRouter. Muse Wonder is reachable through Meta's own API and inside Muse Code; GPT-Rosalind only through O​penAI's trusted-access programme. We are not going to imply we route either one.

What we do carry from that family is the Muse Spark line — Muse Spark 1.2 and Muse Spark 1.1 at $1.25 input and $4.25 output per million tokens with a 1,048,576-token context, an updated checkpoint at identical pricing, and a broader input surface that includes video and audio as well as text, images and PDFs. For a lab testing whether a Meta model fits its pipeline before committing to a Muse Code subscription, that is the cheap way to find out. Alongside it sit 200+ other models behind one key at provider list price with 0% markup, so a vendor price cut is live on our side the same day it is published.

The architecture that makes sense here is the same split these comparisons keep landing on. The workspace half — orchestration, parsing, the code around the science, the document work — runs on a model you can call today, with automatic failover so a provider hiccup moves a request rather than killing a run. The library half stays on a direct specialist key, behind a named route in the routing DSL that fails loudly when the specialist is unavailable instead of quietly answering with a general model. If you want a second opinion on a borderline result without paying specialist rates for every request, model fusion across a panel is the mechanism: several models answer, you compare, and the disagreement is the signal.

What would settle it

Three things. An independent harness could re-run Meta's JobBench and Vals Finance spreads, which would tell you whether the 44-point gap between the two Muse Wonder checkpoints is capability or artefact. A muse-wonder-full API path would make the higher-scoring model purchasable rather than subscription-bound. And any independent biology evaluation covering both models would replace this entire argument with a number, which is what the field has needed since April.

Absent those, the rule is simple enough to write on a card. If the request is agentic, multimodal, or about keeping a long session coherent, Muse Wonder at $4.00 / $20.00 with a 1M context and an independent score of 56 is the better buy and you can start today. If the request is the biology, the only model here that has been tuned for it is behind a review, costs 25% more on every line, and does not appear on any leaderboard you can check — and that combination is what a specialist looks like before the benchmarks catch up.