Muse Wonder 1.0

MetaKey muse-wonder-1.0Also reported as meta/muse-wonder-1.0

Rank
#369
by active users
Active users
21
All time
Adoption
0.9%
of 2,457 accounts
Tokens
517.6M
cache included
Est. cost
$213
at public list prices
First seen
Oct 12, 2026
in any submission

Oct 10, 2024 – Nov 12, 2026 (UTC)2,457 accounts100% of tokens from an identified modelData through Nov 12, 2026, 07:51 UTCHow we count

Daily active users

Distinct accounts that used this model on each UTC day.

0 active users on Nov 12, 2026

Daily active users for Muse Wonder 1.0, Oct 10, 2024 to Nov 12, 2026. Peak 6. Use the left and right arrow keys to read each day.630
Show daily values
Daily active users and tokens for Muse Wonder 1.0
Date (UTC)Active usersTokens
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Oct 12, 2026669.7M
Oct 13, 2026639.7M
Oct 14, 2026443.6M
Oct 15, 2026360.4M
Oct 16, 2026323M
Oct 17, 2026427.6M
Oct 18, 2026313.9M
Oct 19, 2026350.1M
Oct 20, 202624.4M
Oct 21, 202600
Oct 22, 2026310.8M
Oct 23, 2026237.6M
Oct 24, 2026113.3M
Oct 25, 2026227.1M
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Oct 29, 20261514K
Oct 30, 202600
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Nov 1, 2026122.3K
Nov 2, 202627.1M
Nov 3, 2026228.9M
Nov 4, 202622.6M
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Nov 6, 20261176K
Nov 7, 202632.7M
Nov 8, 202600
Nov 9, 2026144.5K
Nov 10, 202600
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Nov 12, 202600

Token mix

How the 517.6M tokens split. Cache reads are usually most of an agent's tokens and are priced far below input.

  • Input7.4%
  • Output0.4%
  • Cache read92%
  • 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.

  1. Muse Code66% of this model's tokens13active users
  2. Codebuff8.5% of this model's tokens1active users
  3. OpenCode8.4% of this model's tokens2active users
  4. Junie7.6% of this model's tokens1active users
  5. Hermes Agent4.4% of this model's tokens1active users
  6. Claude Code2.7% of this model's tokens1active users
  7. ZCode1.6% of this model's tokens1active users
  8. Aside0.5% of this model's tokens1active users
  9. Oh My Pi0.3% of this model's tokens2active users
  10. Reasonix0.2% of this model's tokens1active users
  11. Pi<0.1% 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.

All benchmarks
  • Epoch Capabilities IndexOverall · Epoch AI
    161.7
    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 AI
    50.4%
    Rank #7 of 21 matched modelsMaxModel released Oct 12, 2026Source: cursor.com (opens in a new tab)
    Show 5 more results with other run conditionsHide the other results
    • Listed as Muse Wonder Extra High
      48.2%
      XHighModel released Oct 12, 2026Source: cursor.com (opens in a new tab)
    • Listed as Muse Wonder High
      46.1%
      HighModel released Oct 12, 2026Source: cursor.com (opens in a new tab)
    • Listed as Muse Wonder Medium
      45.2%
      MediumModel released Oct 12, 2026Source: cursor.com (opens in a new tab)
    • Listed as Muse Wonder Low
      41.7%
      LowModel released Oct 12, 2026Source: cursor.com (opens in a new tab)
    • Listed as Muse Wonder Minimal
      34.2%
      MinimalModel released Oct 12, 2026Source: cursor.com (opens in a new tab)
  • WebDev ArenaWeb development · LMArena
    1,652
    Rank #15 of 110 matched modelsMaxPublished Oct 19, 2026Source: huggingface.co (opens in a new tab)
  • Agent ArenaAgents · LMArena
    +0.04
    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 AI
    82.1%
    Rank #18 of 90 matched modelsMaxRun date Oct 12, 2026Source: epoch.ai (opens in a new tab)

    Near its ceiling: the top score is 95.1%, and Epoch AI estimates the reachable maximum at 95%. Differences between top models are small.

Not evaluated: GPQA Diamond, Humanity's Last Exam, SimpleQA Verified, Terminal-Bench 4.1, DeepSWE, FrontierSWE. A missing result is not a score of zero.

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
$0.61
Est. per 1M tokens
92% cache reads
At the community mix
$0.56
Est. per 1M tokens
93% cache reads
3:1 input/output blend
$8
Per 1M tokens at list prices
No cache

Cache reads were 92% of Muse Wonder 1.0 tokens across all time and 93% 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 across all time.
  • The community mix is the tokens of every identified model across all time.
  • 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

  • MetaReferencemuse-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 Gatewaymeta/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. 1siddicky@siddicky141.2Mtokens$87est. cost16active days$87 est. cost · 16 active days
  2. 2Sunghyun Cho@anaclumos59.3Mtokens$7est. cost2active days$7 est. cost · 2 active days
  3. 3Zeyu Fu@PeterPonyu53Mtokens$40est. cost8active days$40 est. cost · 8 active days
  4. 4Baptiste@BaptisteFaisy44.1Mtokens—est. cost2active daysno price · 2 active days
  5. 5Sachee@comicsansbestfont43.1Mtokens$16est. cost1active days$16 est. cost · 1 active days
  6. 6Jeff Kazzee@Jeff-Kazzee42.1Mtokens$4est. cost1active days$4 est. cost · 1 active days
  7. 7ualtinok@ualtinok39.2Mtokens$30est. cost2active days$30 est. cost · 2 active days
  8. 8Ziyang Fang@Fangziyang091028.1Mtokens$8est. cost2active days$8 est. cost · 2 active days
  9. 9Sam Foreman@saforem223Mtokens$6est. cost1active days$6 est. cost · 1 active days
  10. 10gordyf@gordyf14.3Mtokens<$1est. cost2active days<$1 est. cost · 2 active days

More from Meta

Other Meta models in the same period, by active users.

  1. Muse Spark 1.3 Contributor Free8.4% adoption206active users
  2. Muse Spark 1.3 Contributor5.6% adoption138active users
  3. Muse Spark 1.2 Contributor Free4.2% adoption103active users
  4. Muse Spark 1.2 Contributor3.4% adoption84active users
  5. Muse Spark 1.31.9% adoption46active users
  6. Muse Spark 1.21.1% adoption27active users
All models

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.

  • Epoch AIEpoch Capabilities Index, Epoch's own runs and the external results it collects.CC BY 4.0 · Retrieved Nov 12, 2026 · Data through Nov 9, 2026Epoch AI, ‘Capabilities & benchmarking’. Published online at epoch.ai. Retrieved from https://epoch.ai/benchmarks.
  • LMArenaWebDev Arena and Agent Arena leaderboards.CC BY 4.0 · Retrieved Oct 21, 2026 · Data through Oct 19, 2026LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset), Hugging Face.
  • models.devSpecs and list prices per provider.MIT · Retrieved Nov 12, 2026models.dev, an open catalog of AI models (github.com/anomalyco/models.dev).
  • TokscaleActive users, tokens and token mix from public Tokscale submissions. Hidden accounts are excluded.How we count