Muse Wonder 1.0
MetaKey muse-wonder-1.0Also reported as meta/muse-wonder-1.0
- Rank
- #102
- by active users
- Active users
- 3
- Down 2 from 5 active users in the previous 7 days. vs previous 7 days
- Adoption
- 0.5%
- of 610 accounts
- Tokens
- 2.9M
- cache included
- Est. cost
- <$1
- at public list prices
- First seen
- Oct 12, 2026
- in any submission
Nov 6–12, 2026 (UTC)610 accounts100% of tokens from an identified modelData through Nov 12, 2026, 08:00 UTCHow we count
Daily active users
Distinct accounts that used this model on each UTC day.
0 active users on Nov 12, 2026
Show daily values
| Date (UTC) | Active users | Tokens |
|---|---|---|
| Nov 6, 2026 | 1 | 176K |
| Nov 7, 2026 | 3 | 2.7M |
| Nov 8, 2026 | 0 | 0 |
| Nov 9, 2026 | 1 | 44.5K |
| Nov 10, 2026 | 0 | 0 |
| Nov 11, 2026 | 0 | 0 |
| Nov 12, 2026 | 0 | 0 |
Token mix
How the 2.9M tokens split. Cache reads are usually most of an agent's tokens and are priced far below input.
- Input10%
- Output0.4%
- Cache read89%
- Cache write0%
- Reasoning0.3%
Agents that carry Muse Wonder 1.0
Each agent's share of this model's tokens, and how many accounts used the model through it.
Muse Code91% of this model's tokens2active users
Oh My Pi9.0% of this model's tokens1active users
Benchmarks
The best result for Muse Wonder 1.0 in each benchmark Tokscale tracks. Each benchmark has its own unit, run conditions and source, so compare a score only with other models on the same benchmark. Rank is among the models Tokscale matched in that benchmark.
- Epoch Capabilities IndexOverall · Epoch AI161.7
Epoch Capabilities Index: 161.7
- Model
- Muse Wonder 1.0
- Listed as
- Muse Wonder
- Conditions
- Not reported
- Interval
- 159.4–164.3
- Unit
- ECI
- Model released
- Oct 12, 2026
- Retrieved
- Nov 12, 2026
Source · Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
Rank #15 of 158 matched modelsNo conditions reportedModel released Oct 12, 2026Source: epoch.ai (opens in a new tab) - CursorBenchAgentic coding · Cursor, collected by Epoch AI50.4%
CursorBench: 50.4%
- Model
- Muse Wonder 1.0
- Listed as
- Muse Wonder Max
- Conditions
- Max
- Unit
- score
- Model released
- Oct 12, 2026
- Retrieved
- Nov 12, 2026
Best of 6 reported results for this model.
Source · CursorBench results by Cursor, collected by Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
Show 5 more results with other run conditionsHide the other results
- Listed as Muse Wonder Extra High48.2%
CursorBench: 48.2%
- Model
- Muse Wonder 1.0
- Listed as
- Muse Wonder Extra High
- Conditions
- XHigh
- Unit
- score
- Model released
- Oct 12, 2026
- Retrieved
- Nov 12, 2026
Source · CursorBench results by Cursor, collected by Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
- Listed as Muse Wonder High46.1%
CursorBench: 46.1%
- Model
- Muse Wonder 1.0
- Listed as
- Muse Wonder High
- Conditions
- High
- Unit
- score
- Model released
- Oct 12, 2026
- Retrieved
- Nov 12, 2026
Source · CursorBench results by Cursor, collected by Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
- Listed as Muse Wonder Medium45.2%
CursorBench: 45.2%
- Model
- Muse Wonder 1.0
- Listed as
- Muse Wonder Medium
- Conditions
- Medium
- Unit
- score
- Model released
- Oct 12, 2026
- Retrieved
- Nov 12, 2026
Source · CursorBench results by Cursor, collected by Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
- Listed as Muse Wonder Low41.7%
CursorBench: 41.7%
- Model
- Muse Wonder 1.0
- Listed as
- Muse Wonder Low
- Conditions
- Low
- Unit
- score
- Model released
- Oct 12, 2026
- Retrieved
- Nov 12, 2026
Source · CursorBench results by Cursor, collected by Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
- Listed as Muse Wonder Minimal34.2%
CursorBench: 34.2%
- Model
- Muse Wonder 1.0
- Listed as
- Muse Wonder Minimal
- Conditions
- Minimal
- Unit
- score
- Model released
- Oct 12, 2026
- Retrieved
- Nov 12, 2026
Source · CursorBench results by Cursor, collected by Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
- WebDev ArenaWeb development · LMArena1,652
WebDev Arena: 1,652
- Model
- Muse Wonder 1.0
- Listed as
- Muse Wonder (Max)
- Conditions
- Max
- Interval
- 1638–1665
- Unit
- Arena score
- Published
- Oct 19, 2026
- Retrieved
- Oct 21, 2026
Source · LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset on Hugging Face), CC BY 4.0
- Agent ArenaAgents · LMArena+0.04
Agent Arena: +0.04
- Model
- Muse Wonder 1.0
- Listed as
- Muse Wonder (Max)
- Conditions
- Max · Arena Agent Mode
- Interval
- 0.02 to 0.05
- Unit
- net improvement
- Published
- Oct 19, 2026
- Retrieved
- Oct 21, 2026
Source · LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset on Hugging Face), CC BY 4.0
Rank #12 of 48 matched modelsMax · Arena Agent ModePublished Oct 19, 2026Source: huggingface.co (opens in a new tab) - FrontierMath Tiers 1–3 (v2)Reasoning · Epoch AI82.1%
FrontierMath Tiers 1–3 (v2): 82.1%
- Model
- Muse Wonder 1.0
- Listed as
- muse-wonder-1.0_max
- Conditions
- Max
- Unit
- % solved
- Run date
- Oct 12, 2026
- Retrieved
- Nov 12, 2026
Source · Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
Near its ceiling: the top score is 95.1%, and Epoch AI estimates the reachable maximum at 95%. Differences between top models are small.
Pricing
List prices from models.dev in US dollars per 1M tokens, retrieved Nov 12, 2026. Meta is the lab's own API and the reference price for the estimates below.
Price at real agent usageEstimate at list prices
What 1M tokens cost at the reference list price when they split into input, output and cache the way Tokscale users' agents used them. It is not what anyone paid.
- At this model's mix
- $0.73
- Est. per 1M tokens
89% cache reads - At the community mix
- $0.40
- Est. per 1M tokens
96% cache reads - 3:1 input/output blend
- $8
- Per 1M tokens at list prices
No cache
Cache reads were 89% of Muse Wonder 1.0 tokens in the last 7 days and 96% of all tokens. The 3:1 blend, (3 × input + output) ÷ 4, is the usual comparison and ignores cache.
Assumptions
- Meta list prices per 1M tokens: input $4, output $20, cache read $0.20.
- This model's mix is its own input, output, cache and reasoning tokens in the last 7 days.
- The community mix is the tokens of every identified model in the last 7 days.
- This is an estimate at list prices, not what anyone paid.
- Reasoning tokens are priced as output.
- Long-context tiers, batch pricing and discounts are not applied.
- Token mixes come from public Tokscale submissions. Hidden accounts are excluded.
- No cache-write price is listed, so cache writes are priced as input.
List prices by provider
- MetaReference
muse-wonder-1.0$4 input per 1M tokens$20 output per 1M tokens$0.20 cache read per 1M tokensCache write not listed1.05M tokens of context - Vercel AI Gateway
meta/muse-wonder-1.0$4 input per 1M tokens$20 output per 1M tokens$0.20 cache read per 1M tokensCache write not listed1.05M tokens of context
Specs
From models.dev (MIT), retrieved Nov 12, 2026, as the lab or the first listed provider reports them. A dash means models.dev does not list the value.
- Context window
- 1.05M tokens
- Max output
- 131K tokens
- Input
- Text, Image, Video, PDF, Audio
- Output
- Text
- Knowledge cutoff
- — not listed
- Release date
- Oct 12, 2026
- Open weights
- No
- Reasoning
- Yes
- Tool calling
- Yes
Top public profiles
Public profiles that used Muse Wonder 1.0 most in this period, by tokens. Hidden accounts are never listed.
- 1
Zenco@notzenco2.6Mtokens—est. cost1active daysno price · 1 active days
- 2
Brad Lishman@bradlishman264Ktokens—est. cost3active daysno price · 3 active days
- 3
Alec Z@offcall111Ktokens<$1est. cost1active days<$1 est. cost · 1 active days
More from Meta
Other Meta models in the same period, by active users.
- Muse Spark 1.3 Contributor2.3% adoption14active users
- Muse Spark 1.31.3% adoption8active users
- Muse Wonder Full 1.00.8% adoption5active users
- Muse Wonder Full 1.0 Contributor0.3% adoption2active users
- Muse Wonder 1.0 Contributor0.2% adoption1active users
- Muse Wonder 1.0 High0.2% adoption1active users
Sources and attribution
Each score keeps its own source, unit and run conditions. Tokscale does not combine benchmarks into one score, and a missing score means the model was not evaluated, not that it scored zero.