Meta Muse Wonder vs GPT-6.1 Sol is the freshest head-to-head in frontier AI: two flagship models landed in the same two-week window, and Artificial Analysis rates their flagship API configurations a dead-even tie: Intelligence Index 52 vs 52. Same composite, completely different invoices, and a config trap that is already muddying half the coverage online. This post is the buying guide: what each model actually is, what each price point actually gets you, and which benchmark wins should drive your decision.
Meta Muse Wonder vs GPT-6.1 Sol: what each model is — and who each is for
Meta Muse Wonder is, in Meta’s own words, a model for “frontier coding, long-running agents, and advanced computer use” that “plans long tasks, delegates to sub-agents, and recovers from errors.” Meta pitches the Wonder family as “models for personal and professional agent use,” with Active Memory and Active Focus features for keeping long-running tasks on track. There are two configurations, and they are not interchangeable — more on that in a moment. Wonder takes text, image, video, and audio as input and outputs text (per Artificial Analysis). It launched October 12, 2026 (llm-stats’ tracked release date for Muse Wonder Full, which shipped the same day).
(Note: this is not the same product as Meta’s earlier local agent model — we covered that separately in our Muse Glimmer post.)
OpenAI GPT-6.1 Sol is OpenAI’s current flagship in the 6.1 line: a proprietary, multimodal-input model (text and images) with a 1,050,000-token input window and 128,000-token output cap, an April 2026 knowledge cutoff, and a documented system card at deploymentsafety.openai.com (all per llm-stats’ model profile, fetched this week). Artificial Analysis runs it at multiple reasoning efforts — its low-effort config scores 42 on the AA Intelligence Index, its max config 52 — which tells you Sol is a reasoning-effort-flexible model rather than a single fixed mode. Its launch and the “$500 ChatGPT plan” story were already covered in our GPT-6.1 Sol launch coverage, and Sol vs Anthropic was covered in our Sol vs Claude Sonnet 5.5 comparison — we won’t re-litigate either here.
Who each is for, translated into practitioner terms: Sol is the general-purpose API workhorse — huge input window, low token prices, and a max-effort mode that ties for the AA composite lead. Wonder is an agent-harness model first and an API model second: its strongest showing is inside Meta’s own Muse Code tooling, and its API pricing is premium-tier.
⚠ The config trap: read this before the tables
This is the single most important section of the post, because the coverage mix-up is already happening.
“Muse Wonder Full” is subscription-only. Meta’s own model page (dev.meta.ai/models/muse-wonder, fetched for this post) lists muse-wonder-full-1.0 as “Available by subscription in Muse Code,” with access “on the Power Usage and Maximum Usage plans” — and no per-token price at all. On the same page, Muse Code’s Power Usage plan is $50.00/month (“20x more usage than the Everyday Usage plan”) and Maximum Usage is $200.00/month (“80x more usage than the Everyday Usage plan”). Artificial Analysis confirms: “Muse Wonder Full is only available to Muse Code users” and “is not priced per token, so no Cost per Task figure is available.”
“Muse Wonder” is the API model. muse-wonder-1.0 is “Available in Model API and Muse Code” at $4.00 per million input tokens, $0.20 per million cached-input tokens, and $20.00 per million output tokens — quoted identically by Meta’s model page and Artificial Analysis. This configuration is what you actually buy at $4/$20. Ridge’s AI-data coverage adds the caveat that “the one on the API is still in preview.”
Why it matters: the subscription-only Full config is the stronger model — Artificial Analysis scores Muse Wonder Full (max) at 56 on the Intelligence Index vs 52 for Muse Wonder (max). Anywhere you see “Muse Wonder Full, II 56” quoted next to API pricing is mixing configurations: you cannot buy the 56-point model per-token at any price. Every table below keeps the labels exact: Wonder (Max/API, II 52) vs Sol (Max/API), with Wonder Full (subscription-only, II 56) mentioned only for completeness.
So what do you actually get at each price? At $4/$20 per million tokens: Muse Wonder (max), II 52, agentic knowledge-work strengths. At $50/month or $200/month of Muse Code: the same tooling with access to Muse Wonder Full (max), II 56, plus (per AA) a Coding Agents Index score of 68 for the Full config in Muse Code vs 63 for plain Wonder — and Meta claims Full “tops agentic and coding benchmarks, including DeepSWE and SWE-Marathon in independent third-party runs.”
Comparison Table 1 — Meta Muse Wonder vs GPT-6.1 Sol composite scores, labeled per source
| Metric (source) | Muse Wonder (Max) | GPT-6.1 Sol (Max) |
|---|---|---|
| AA Intelligence Index v4.3/4.3.2 (artificialanalysis.ai) | 52 | 52 |
| AA Intelligence Index task cost (artificialanalysis.ai) | $4.52 | $0.72 |
| llm-stats composite score (llm-stats.com) | [UNVERIFIED: llm-stats composite scores for the two models — the compare page’s index values did not render in this run’s fetch; outline indicated 54.0 (Sol) vs 52.1 (Wonder Full), but its Wonder row is the Full config] | 〃 |
| Context window | 1M tokens (Meta; AA) | 1,050,000 in / 128,000 out (llm-stats) |
Read Table 1 as two dead-locked composites plus a cost column that isn’t remotely dead-locked: Artificial Analysis puts Sol (max) at $0.72 per Intelligence Index task vs $4.52 for Wonder (max). That is not a typo and not an averaging trick — it’s token accounting, covered below.
One aggregator-discipline note: llm-stats’ published comparison is GPT-6.1 Sol vs Muse Wonder Full — the subscription config, not the API model. Its qualitative finding (fetched this run): Sol wins 1 of just 3 shared benchmarks (HealthBench Professional), while Wonder Full takes DeepSWE 1.1 and GDP.pdf, and “Muse Wonder Full shows notably better performance in the majority of benchmarks.” That’s a real signal for the Full config — just never splice it into a column with the $4/$20 API model.
Comparison Table 2 — The split, benchmark by benchmark (AA’s head-to-head, both at Max)
Artificial Analysis’ Intelligence Index v4.3.2 combines ten evaluations. A provenance note before the numbers: AA’s published comparison-table values are client-side chart data that do not render via HTML — the comparison page’s HTML and rendering proxies serve methodology text only, with no benchmark numbers. So the table below separates the values this revision could independently re-verify from AA’s article text — the Wonder-side Terminal-Bench 4.0: 52% (“33% to 52%”), AutomationBench-AA: 68% (“58% to 68%”), and AA-Omniscience: 25 — from everything else. Rows tagged [AACHART] (and the Sol-side cells in the three re-verified rows, including Sol’s 56% Terminal-Bench and 42 AA-Omniscience) come from AA’s published chart interface as it rendered to the writer’s browser; they could not be reproduced from any renderable HTML primary this revision. Treat the [AACHART] values as directional attribution to AA’s interface, not as independently audited figures.
| Benchmark | Muse Wonder (Max) | GPT-6.1 Sol (Max) | Winner |
|---|---|---|---|
| AA-Briefcase v1.1 (agentic knowledge work, Elo) [AACHART] | 1659 | 1557 | Wonder |
| GDPval-AA v2.1 (Elo) [AACHART] | 1770 | 1594 | Wonder |
| AutomationBench-AA (E2E business workflows) | 68% (AA article text) | 65% [AACHART] | Wonder |
| SciCode [AACHART] | 60% | 54% | Wonder |
| Terminal-Bench 4.0 (agentic terminal coding) | 52% (AA article text) | 56% [AACHART] | Sol |
| AA-Omniscience (knowledge reliability / hallucination) | 25 (AA article text) | 42 [AACHART] | Sol (per AA’s chart interface) |
| Humanity’s Last Exam [AACHART] | 50% | 53% | Sol |
| AA-LCR v1.1 (long-context reasoning) [AACHART] | 81% | 83% | Sol |
| CritPt [AACHART] | 28% | 32% | Sol |
| GDP.pdf (professional document reasoning) [AACHART] | 31% | 31% | Tie |
Taking the chart-interface rows at face value, the pattern looks clean: Sol owns the agentic/terminal-coding edge and knowledge reliability; Wonder owns knowledge-professional work — Briefcase, GDPval, AutomationBench, SciCode. On AA-Omniscience, AA’s Wonder article ranks GPT-6 Astra at 43 and Gemini 4 Argon (high) at 42, so Sol’s chart-interface 42 would put it with the reliability leaders, while Wonder’s 25 (verified from AA’s article text) is a genuine weak spot. AA also notes the flip side: Wonder (max) has a 27% hallucination rate — third-lowest among models at that intelligence tier — and Wonder Full’s 23% is the second-lowest of any model scoring 45+. Wonder hallucinates rarely; it just declines knowledge more often. And per AA’s Sol article, Sol’s own max-effort hallucination rate is ~54% (down from 60% on GPT-6 Sol) — Sol answers (and hallucinates) far more often, which is part of how it earns its Omniscience accuracy.
Comparison Table 3 — Pricing and configuration, per source
| Model / config | Availability | Price (per M tokens in / cached / out) | Notes (per source) |
|---|---|---|---|
Muse Wonder (muse-wonder-1.0) |
Meta Model API + Muse Code (Meta’s model page) | $4.00 / $0.20 / $20.00 (Meta; AA) | AA cost per II task: $4.52; 7.69M input tokens/task |
Muse Wonder Full (muse-wonder-full-1.0) |
Muse Code subscription only — Power ($50/mo) or Maximum ($200/mo, 80x Everyday) (Meta’s model page) | No per-token price (Meta; AA) | AA II 56; Coding Agents Index 68 in Muse Code |
| GPT-6.1 Sol (API) | OpenAI API (llm-stats provider table) | $2.00 / $0.100 / $10.00 (llm-stats; AA concurs “$2/$10”) | AA cost per II task (max): $0.72 |
| Claude Opus 5.5 (max w/ fallback) — sidebar | Anthropic API | $4 / $20 class per AA (“matches Claude Opus 5.5”) | AA II 58; AA cost per II task $5.98 |
Two pricing footnotes worth internalizing from AA’s analysis: Wonder’s token prices are 60% below GPT-6 Astra and Claude Fable 5.1 ($10/$50), exactly double GPT-6.1 Sol’s ($2/$10), and identical to Opus 5.5’s. And the cost-per-task asymmetry is driven by input, not output: Wonder burns 7.69M input tokens per Intelligence Index task (76% of its $4.52 cost is input — $1.92 cache writes, $1.44 cache hits, $0.07 uncached), while emitting only ~54k output tokens. AA says Wonder’s input use is “2–7x Gemini 4 Argon (high), GPT-6.1 Sol (max) and GPT-6 Astra (max).” If your workloads are long-context and input-heavy, Wonder’s effective bill scales with your context profile, not with AA’s benchmark profile — budget accordingly.
Sidebar: Claude Opus 5.5 at the same token price (not averaged)
Because Wonder’s $4/$20 pricing “matches Claude Opus 5.5” (AA’s phrasing), the three-way frame from our recent post our Gemini 4 Argon vs Opus 5.5 vs GPT-6.1 Sol comparison isn’t far away. Per the AA data fetched for this post, at the same $4/$20 token price: Opus 5.5 (max w/ fallback) scores 58 on the AA Intelligence Index at $5.98 per task; Sonnet 5.5 sits at 56 and $5.46 per task; Wonder (max) at 52 and $4.52 per task; Wonder Full at 56 with no per-token cost at all. Note AA’s own caveat from its GDPval table: Wonder Full’s 1857 sits within the confidence intervals of Opus 5.5 (1866) and Sonnet 5.5 (1838). We’re presenting these as parallel rows, not averaging any of them into a single “value score” — composite intelligence and cost-per-task measure different things, and the subscription-only Full config has no per-token figure to average in the first place.
Meta Muse Wonder vs GPT-6.1 Sol: the caveated verdict
On raw composite intelligence: a genuine tie — AA scores the API configurations 52–52. Nobody buys a tie, so everything below is conditional.
Buy Muse Wonder (API) if knowledge-professional workflows dominate: GDPval-style expert tasks (1770 vs 1594 Elo), agentic knowledge work (Briefcase 1659 vs 1557), scientific code (SciCode 60% vs 54%), and end-to-end business workflows (68% vs 65%) — the Elo/absolute figures per AA’s published chart interface, with the 68% AutomationBench figure also confirmed in AA’s article text. At $4.52 per AA task it undercuts Opus 5.5 ($5.98), Sonnet 5.5 ($5.46), and Fable 5.1 ($7.63) for roughly Opus-class work — with two caveats: AA’s figures assume an input-heavy profile (7.69M tokens/task), so your effective cost depends on your context pattern; and on the API you get the 52-point config, not the 56-point Full model.
Buy GPT-6.1 Sol if terminal/agentic coding (Terminal-Bench 4.0: 56% vs 52%, per AA’s published chart interface — values not independently re-verifiable from rendered text this revision) and knowledge-reliability scoring under retrieval-style pressure (AA-Omniscience 42 vs 25, same chart interface) matter to your pipeline — or if cost per unit of intelligence is the deciding factor. One honesty note on the “factuality” framing: AA’s own Sol article reports Sol’s max-effort hallucination rate at ~54% on AA-Omniscience (falling from 60% on GPT-6 Sol), and AA’s data suggests the Omniscience gap is driven by answering behavior rather than raw truthfulness — Wonder rarely hallucinates but declines knowledge questions more often, while Sol answers (and therefore also hallucinates) far more often. If by “factuality” you mean the lowest hallucination rate, the verified claim is Wonder’s 27% (max) / 23% (Full), not Sol’s. $0.72 vs $4.52 per AA task is a 6x gap at the same composite score, and $2/$10 token pricing (double-the-context, half-the-price inputs) makes Sol the default for anything long-context. The caveats: a 128K output cap, an April 2026 knowledge cutoff, and — if AA’s input-use comparison is indicative — a much lighter-context style than Wonder’s, which is exactly what makes it cheap.
This verdict is deliberately conditional. A 52–52 composite with near-perfect benchmark complementarity means task mix, not leaderboard rank, decides it. What is unconditional: don’t let a “Muse Wonder Full 56” quote (the subscription model) sell you on the “$4/$20 API model” (the 52-point one).
Decision guide
Quick recap for scanners: Meta Muse Wonder vs GPT-6.1 Sol comes down to task mix, not leaderboard rank.
- Composite parity → buy on task mix. AA says 52–52; llm-stats gives the edge to the Full config — check which config any rank actually scored.
- Terminal/agentic coding and answer-coverage-under-pressure QA → Sol — but caveat the numbers: TB 4.0 56% vs 52% and Omniscience 42 vs 25 are per AA’s published chart interface (not independently re-verifiable from rendered text this revision); $0.72/task is independently verified. Per AA’s own Sol article, Sol’s max-effort hallucination rate is ~54%, so pick Sol for answer coverage, not lowest-hallucination factuality.
- Knowledge-professional and business-workflow agents → Wonder (Briefcase, GDPval, AutomationBench, SciCode; $4.52/task vs Opus 5.5’s $5.98 at the same token price).
- Long-context, input-heavy pipelines → model your bill first. Wonder’s cost is 76% input; Sol’s token prices are half Wonder’s.
- If you saw “Muse Wonder Full, II 56”: Full is Muse Code-subscription-only (Power $50/mo, Maximum $200/mo at 80x usage) with no per-token price. Don’t compare its II to API pricing — including ours in any older draft.
- Aggregator cross-check discipline: AA scores Wonder (Max); llm-stats’ comparison is Sol vs Wonder Full. Never mix the two Wonder configs in one column.
References and further reading
- Artificial Analysis — Intelligence Index v4.3.2 composite scoring and the Wonder/Sol head-to-head benchmarks (chart-interface values cited per the provenance note above)
- Meta — Muse Wonder model page — configuration availability and Muse Code Power/Maximum plan pricing
- OpenAI — deploymentsafety.openai.com — GPT-6.1 Sol system card
- llm-stats — model profiles, provider pricing table, and the Sol vs Muse Wonder Full comparison
- Ridge AI — AI-native data-analytics commentary, including the note that Muse Wonder on the API is still in preview
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