Muse Wonder vs GPT-6 Sol: One of These Models Can Decide Not to Think

The single most expensive line in this comparison is a setting, not a spec. Muse Wonder, released on October 12, 2026, reasons on every call — the effort ladder runs from minimal to max and there is no position on it that means "don't" — and those thinking tokens bill at $20.00 per million output tokens. GPT-6 Sol, which the vendor shipped generally available on September 22, 2026, accepts none as a reasoning effort setting, so the same request can be answered without a reasoning pass at $10.00 per million output tokens, or a quarter of Muse Wonder's rate on the input side at $2.00 against $4.00. Four points of independent index separate them, and about 4.3× separates their measured cost per task. Which of those numbers governs your bill depends on a parameter you set.

There is a second distinction that makes this pairing sharper than a usual versus page. GPT-6 Sol is a settled, widely deployed mid-tier model with three weeks of independent measurement and a published deprecation notice pointing at its own successor. Muse Wonder is two days old, has one evaluator's row, and comes in two checkpoints where the higher-scoring one has no per-token price at all. One side of this comparison you can benchmark today with confidence; the other you can call today and cannot yet forecast.

Where each model sits

• GPT-6 Sol — the middle tier of the GPT-6 stack, between the flagship GPT-6 Astra and the cheap GPT-6 Luna, generally available since September 22, 2026 at $2.00 per million input tokens and $10.00 per million output.

• Muse Wonder — Meta Superintelligence Labs' frontier release of October 12, 2026, served as muse-wonder-1.0 on the Meta Model API and inside Muse Code at $4.00 in and $20.00 out. A second checkpoint, Muse Wonder Full, scores higher on Meta's own table and is subscription only.

• Context — GPT-6 Sol 1,050,000 tokens; Muse Wonder 1,048,576. Level, to the point of being the same number printed two ways.

• Output ceiling — GPT-6 Sol 128,000 tokens in one response; Muse Wonder 943,718. This is the one dimension where Muse Wonder is not competing, it is in a different category, and it matters the moment you ask for a long artifact in a single turn.

• Input price — GPT-6 Sol $2.00 per million below 272,000 input tokens; Muse Wonder $4.00 flat. Half price, but with a boundary attached.

• Output price — GPT-6 Sol $10.00 per million; Muse Wonder $20.00. Same 2× ratio, and the line that dominates any agentic bill.

• Reasoning — GPT-6 Sol's effort ladder runs none, low, medium (default), high, xhigh, max. Muse Wonder's runs minimal, medium (default) through max, with no off position. Thinking tokens bill as output on both.

• Modalities in — GPT-6 Sol takes text, image and files; Muse Wonder takes text, image, speech and video. Both write text only.

• Independent index — GPT-6 Sol 48 at max effort, ranked around eighteenth of 212 models, at about $1.05 per index task with a 90% cache discount; Muse Wonder 52 at max effort, ninth of 219, at about $4.52 per index task with a 95% discount.

The 272,000-token line, and why it is the real GPT-6 Sol price

The GPT-6 Sol rate card has a step at 272,000 input tokens, and it is not an overage on the excess. Above that threshold the whole request reprices: input doubles to $4.00, cached input to $0.40, and output rises 1.5× to $15.00. A 300,000-token request bills all 300,000 tokens at the long-context rate.

Read against Muse Wonder's flat $4.00 and $20.00, that changes the comparison at exactly the point where it would otherwise be most lopsided. Below 272K, GPT-6 Sol is half price on input and 40% cheaper on output. Above it, the two models are within a dollar of each other on both lines — and Muse Wonder is the cheaper one on output, at $20.00 against $15.00's 1.5× multiplier being applied to a request whose entire context has just doubled in price.

Both vendors sell caching as the mitigation, and both are honest about roughly the same discount: 90% on GPT-6 Sol, 95% on Muse Wonder, with a cache read of $0.20 per million on each inside their cheap tiers. That symmetry is what makes the cost-per-task gap so informative. The two models are priced within 2× of each other on the rate card and their measured cost per index task differs by 4.3×, which means the difference is being created by how many tokens each model spends, not by what it charges for them. GPT-6 Sol takes in fewer tokens and emits fewer of them to reach a similar answer — four index points lower, at roughly a fifth of the spend.

What Muse Wonder's extra $3.47 per task buys

Four index points and a higher price is not a value proposition on its own, so it is worth being specific about what the extra money goes to.

The output ceiling is the clearest answer. 943,718 tokens in a single response against GPT-6 Sol's 128,000 is a seven-fold difference, and it is the reason a long-form generation task that needs stitching on Sol does not need stitching on Muse Wonder. If your workload generates large artifacts — a full module, a migration plan, a long research document — the cost comparison above is not the relevant one, because the cheaper model cannot complete the task in one call at all.

Speech and video input is the second. Muse Wonder accepts them natively; GPT-6 Sol takes text, images and files, so audio has to become text before it reaches the model. For a pipeline whose input is recordings, that removes a component rather than shortening a prompt.

The third is the agentic suites, and it comes with the heaviest caveat. On Meta's own vendor-reported table, Muse Wonder scores 85.6 on Terminal-Bench 4.0-AA and 78.8 on SWE-Marathon, and Meta's table places GPT-6 Astra — the tier above Sol, not Sol itself — at 81.4 and 74.1. Those are Meta's harness numbers, unreproduced by anyone, and they compare a model Meta just launched against a model it did not choose. They are a reason to run a coding-agent evaluation of your own, not a reason to move a production path.

Both models carry a mandatory-reasoning caveat in different forms, and the GPT-6 Sol side is worth stating precisely because it is easy to get wrong. Sol is the one model in the GPT-6 stack that accepts none; the flagship GPT-6 Astra's ladder starts at low. So the cost lever in this comparison is not "GPT-6 versus Muse Wonder" — it is that one model in the stack can skip thinking entirely and the other one cannot skip it at all.

One footnote about the Sol you are pricing

Artificial Analysis' page for GPT-6 Sol carries a deprecation banner noting that the vendor has since released GPT-6.1 Sol, and lists the older model at an index of 48 against the newer one's 51.8 at a lower cost per task. That is not a reason to disregard the comparison in this article — GPT-6 Sol is still generally available, still priced as published, and still the model that sits directly opposite Muse Wonder on tier and price. It is a reason to check which identifier you are actually pointing at before you build an evaluation around it, because the two Sol releases are not the same model and their index scores differ by nearly four points.

It is also a useful reminder about vendor tables in general. Meta's launch material reports the callable Muse Wonder at 56 on the Artificial Analysis Index while Artificial Analysis publishes 52, and reports Muse Wonder Full at 59.0 against the evaluator's 56. The vendor, on its own pages, publishes the deprecation notice that makes the same point from the other direction. The number to plan against is the one on the independent board with its revision attached.

Where OrcaRouter comes in

GPT-6 Sol is on OrcaRouter as openai/gpt-6-sol at vendor list rates — $2.00 per million input tokens and $10.00 per million output below 272,000 tokens, $4.00 and $15.00 above, cached input $0.20 and $0.40, with the 1,050,000-token window and the 128,000-token output ceiling passed through as published. The repricing rule travels with it unchanged, which is deliberate: a pass-through that quietly smoothed a vendor's tier boundary would be worse than useless for budgeting.

Muse Wonder is not on OrcaRouter, and we will not suggest otherwise. There is no meta/muse-wonder entry in our catalogue; the model runs on Meta's own API and inside Muse Code. What we do carry from the same lab is Muse Spark 1.2 and Muse Spark 1.1, so Meta's reasoning line is reachable from the same key if you want a Meta model with a published price while the Wonder checkpoint settles.

That is also the shape of the evaluation this page is really recommending. Put a sample of your real traffic through GPT-6 Sol at both sides of the 272,000-token boundary, once with reasoning set to none and once at high, and record the token counts rather than the pass rate alone. OrcaRouter runs 200-plus models behind one OpenAI-compatible endpoint with provider list prices passed through with zero markup, so vendor price changes reach us the same day they are announced, and an idled or failed provider is handled by automatic failover rather than by your error handler. If a provider we route picks up Muse Wonder, the other half of this test becomes a one-line model-name change — and the token counts you already collected are the baseline it gets measured against.

Which one, for whom

• Take GPT-6 Sol if your prompts live below 272,000 tokens and your traffic is high-volume: it is half the input price, has an off switch for reasoning, and measures at roughly a fifth of Muse Wonder's cost per index task on the same board.

• Take Muse Wonder if you generate long outputs in single turns, or if your input arrives as audio or video. GPT-6 Sol cannot produce a 943,718-token answer and cannot hear a recording; those are capability limits rather than pricing choices.

• Mind the boundary whichever way you lean. Crossing 272,000 tokens doubles the whole GPT-6 Sol request, and at that point Muse Wonder is flat-priced and, on output, the cheaper of the two.

• Confirm which Sol you are comparing. GPT-6 Sol and GPT-6.1 Sol are different checkpoints with different index scores and different prices, and both are generally available.

Four index points apart and 4.3× apart on the measured bill, with the deciding variable being whether your model is allowed to stop thinking. That combination — a cheaper model that can decline to reason against a dearer one that cannot — is a more useful way to think about this matchup than either scoreboard is.