Muse Wonder 1.0

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

Rank
#76
by active users
Active users
17
Up 8 from 9 active users in the previous 30 days. vs previous 30 days
Adoption
1.8%
of 958 accounts
Tokens
408.2M
cache included
Est. cost
$170
at public list prices
First seen
Oct 12, 2026
in any submission

Oct 14 – Nov 12, 2026 (UTC)958 accounts100% of tokens from an identified modelData through Nov 12, 2026, 00:36 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 14, 2026 to Nov 12, 2026. Peak 4. Use the left and right arrow keys to read each day.420
Show daily values
Daily active users and tokens for Muse Wonder 1.0
Date (UTC)Active usersTokens
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
Oct 26, 202621.5M
Oct 27, 2026343.3M
Oct 28, 202600
Oct 29, 20261514K
Oct 30, 202600
Oct 31, 202629.6M
Nov 1, 2026122.3K
Nov 2, 202627.1M
Nov 3, 2026228.9M
Nov 4, 202622.6M
Nov 5, 202600
Nov 6, 20261176K
Nov 7, 202632.7M
Nov 8, 202600
Nov 9, 2026144.5K
Nov 10, 202600
Nov 11, 202600
Nov 12, 202600

Token mix

How the 408.2M tokens split. Cache reads are usually most of an agent's tokens and are priced far below input.

  • Input7.7%
  • 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 Code58% of this model's tokens10active users
  2. Codebuff11% of this model's tokens1active users
  3. OpenCode11% of this model's tokens1active users
  4. Junie9.6% of this model's tokens1active users
  5. Hermes Agent5.6% of this model's tokens1active users
  6. Claude Code3.4% of this model's tokens1active users
  7. ZCode2.0% of this model's tokens1active users
  8. Oh My Pi0.1% of this model's tokens1active users
  9. Aside<0.1% of this model's tokens1active users
  10. 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
    161.8
    Rank #12 of 160 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 #5 of 18 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 #15 of 86 matched modelsMaxRun date Oct 12, 2026Source: epoch.ai (opens in a new tab)

    Near its ceiling: the top score is 93.7%, 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 4.0, DeepSWE, FrontierSWE. A missing result is not a score of zero.

Pricing

List prices from models.dev in US dollars per 1M tokens, retrieved Oct 21, 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
$0.62
Est. per 1M tokens
92% 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 92% of Muse Wonder 1.0 tokens in the last 30 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 30 days.
  • The community mix is the tokens of every identified model in the last 30 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

  • 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 Oct 21, 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@siddicky90.5Mtokens$61est. cost14active days$61 est. cost · 14 active days
  2. 2Zeyu Fu@PeterPonyu53Mtokens$40est. cost8active days$40 est. cost · 8 active days
  3. 3Sunghyun Cho@anaclumos51.2Mtokens$5est. cost1active days$5 est. cost · 1 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. 6ualtinok@ualtinok39.2Mtokens$30est. cost2active days$30 est. cost · 2 active days
  7. 7Ziyang Fang@Fangziyang091028Mtokens$7est. cost1active days$7 est. cost · 1 active days
  8. 8Sam Foreman@saforem223Mtokens$6est. cost1active days$6 est. cost · 1 active days
  9. 9gordyf@gordyf14.3Mtokens<$1est. cost2active days<$1 est. cost · 2 active days
  10. 10Satyam Garodia@Satyamgarodia13.8Mtokens—est. cost4active daysno price · 4 active days

More from Meta

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

  1. Muse Spark 1.3 Contributor6.1% adoption58active users
  2. Muse Spark 1.3 Contributor Free4.9% adoption47active users
  3. Muse Spark 1.32.2% adoption21active users
  4. Muse Wonder Full 1.01.0% adoption10active users
  5. Muse Spark 1.2 Contributor0.6% adoption6active users
  6. Muse Wonder 1.0 Contributor0.5% adoption5active 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 Oct 21, 2026 · Data through Oct 15, 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 Oct 21, 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