cd /news/large-language-models/don-t-trust-anyone-s-muse-wonder-ben… · home › topics › large-language-models › article
[ARTICLE · art-149864] src=dev.to ↗ pub= topic=large-language-models verified=true sentiment=· neutral

Don't Trust Anyone's Muse Wonder Benchmark. Race It Against Opus 5.5 on Your Repo

A developer has published a reproducible bake-off method for comparing Meta's Muse Wonder against Claude Opus 5.5 on a user's own repository, after noting that Meta's model card claims Wonder leads on long-horizon coding (78.8 vs 74.2 on DeepSWE, 53.1 vs 46.3 on SWE-Marathon) while Artificial Analysis's Intelligence Index puts Opus 5.5 ahead 58 to 52. The approach runs the same ticket through two agents in separate git worktrees and scores both with a named test command, using the muse-wonder-1.0 API model ID and Meta's documented ANTHROPIC_BASE_URL, ANTHROPIC_AUTH_TOKEN and ANTHROPIC_MODEL environment variables. The author notes Meta's own footnotes concede competitor runs were "best-effort" with prompts, tools and runtime possibly not optimally tuned.

by read10 min views3 publishedOct 12, 2026

Meta's chart says Muse Wonder beats Claude Opus 5.5 at long coding

jobs: 78.8 to 74.2 on DeepSWE, 53.1 to 46.3 on SWE-Marathon

(model card).

Artificial Analysis

gives Wonder 52 on its Intelligence Index. Opus 5.5

scores 58.

Both cost $4 per million input tokens and $20 per million output

(Wonder's price per

TechCrunch,

Opus's per the same Artificial Analysis article).

Meta's chart and the independent index can both hold, because they

measure different things, and neither ran on your code.

Meta launched Muse Wonder on October 12, 2026

(Meta newsroom).

In the Hacker News thread,

one commenter called it "much worse than Opus 5.5" after an

image-to-HTML test. That is one prompt. Nothing below is a Muse

Wonder result. What follows is the setup to get your own: the same

ticket handed to two agents in two git worktrees, and a script that

scores both.

Wonder wins five of Meta's 14 rows against Opus 5.5, mostly

long-horizon coding, legal agent work and Meta's internal memory

test. It loses nine, including ProgramBench, where Opus leads 70.5

to 50.0. The row-by-row view is in

the scorecard breakdown.

Meta's own footnotes tell you how far to trust any single row. For

competitor runs it did in-house, Meta says they "use common settings

where practical but remain best-effort: prompts, tools, and runtime

may not be optimally tuned for proprietary models." And from the

limitations section of the

model card:

"Performance can vary with quantization, serving configuration,

prompts, tools, and the agent harness."

No leaderboard can hold prompts, tools and harness fixed for your

setup, but a bake-off on your own repo does exactly that.

One ticket, two agents building the same thing, one scale to weigh them.

Choose a real ticket with a test that fails today (or write one

first), a fix that touches a few files, and an outcome you can judge.

Save the prompt to a file outside the repo so every agent gets the

same words, and name the test command in it. "Make

npm test -- slug pass without changing the tests" gives the agents

and you the same finish line.

Next, create a key for the Meta Model API and export it under the

name Meta's coding-agents docs

use. Put it in your shell profile (or load it from your secrets

manager) so any new terminal has it:

export MODEL_API_KEY="your-key-here"

The API model ID is muse-wonder-1.0. The API does not serve Muse

Wonder Full, the stronger sibling: it is for Muse Code subscribers

only

(TechCrunch).

I build Hermes IDE, a free desktop console

for coding agents, so the setup below uses it

(download it to follow along). The

plain-git version follows session B.

Press ⌘N (Ctrl+Shift+N on Windows and Linux), pick Claude Code in the

agent chip, pick Opus 5.5 in the model chip, paste the prompt and

press Enter. Claude Code uses its normal sign-in. The task starts in

its own git worktree on a new hermes/<task> branch, cut from the

branch you are on.

Meta documents three environment variables that point Claude Code at

its API: ANTHROPIC_BASE_URL, ANTHROPIC_AUTH_TOKEN and

ANTHROPIC_MODEL. The docs also tell you to set the Opus, Sonnet and

Haiku aliases and the subagent model to the same ID, "so no path

falls back to a Claude model". Skip those and some requests ask for

a Claude model ID instead of Muse Wonder, and session B stops being

a clean Muse run.

Seven variables make a long command, so put them in a wrapper. Save

this as claude-muse somewhere on your PATH and chmod +x it:

#!/usr/bin/env bash
export ANTHROPIC_BASE_URL="https://api.meta.ai"
export ANTHROPIC_AUTH_TOKEN="$MODEL_API_KEY"
m="muse-wonder-1.0"
export ANTHROPIC_MODEL="$m"
export ANTHROPIC_DEFAULT_OPUS_MODEL="$m"
export ANTHROPIC_DEFAULT_SONNET_MODEL="$m"
export ANTHROPIC_DEFAULT_HAIKU_MODEL="$m"
export CLAUDE_CODE_SUBAGENT_MODEL="$m"
exec claude "$@"

The base URL has no /v1, and the key goes in AUTH_TOKEN, not

API_KEY. Both details come from Meta's docs. Their worked example

uses muse-spark-1.3; muse-wonder-1.0 is the Wonder ID.

Now press ⌘N again and pick Custom agent ("Any command you

type"). Name it Claude Code · Muse Wonder and set the command to

claude-muse. The hint under the field says it plainly: "Hermes

starts this command in the session's terminal, as if you typed it."

Because it's a real shell, an env-prefixed one-liner works too. When

the CLI is up, paste the same prompt.

Sessions A and B now run the same harness on the same ticket. What

differs is the model and the API serving it. Meta's footnote on its

SWE-Marathon row calls that row "not a controlled common-harness

comparison". Your A-versus-B run is a common-harness one.

Real Hermes window, test build with stand-in agents. The same task in two sessions side by side, each on its own worktree branch. In your bake-off, the Custom agent takes the second slot.

The isolation part is one command per agent. From your repo:

git worktree add -b bakeoff/opus ../bakeoff-opus
git worktree add -b bakeoff/muse ../bakeoff-muse

Then run claude in ../bakeoff-opus (pick Opus 5.5 with /model)

and claude-muse in ../bakeoff-muse, each in its own terminal.

You lose a single place to watch both, and the review step later.

Hermes worktrees are plain git branches too, so nothing here locks

you in.

muse CLI Session B tests the model inside Anthropic's harness. If you want

Meta's harness too, install Muse Code. Meta's installer creates a

command named muse in ~/.local/bin. Read the script first if you

prefer, then run:

curl -fsSL https://dev.meta.ai/install.sh | bash

Add one more Custom agent with the command muse. Muse Code is a

subscription: the Power Usage plan

is $50 a month and includes Wonder Full. Meta's product page lists

macOS and Windows. One early report is negative: in the same Hacker

News thread, a commenter said the Muse Code sandbox "never worked

across Mac or Linux". Check it on your own machine before you pay

for a plan.

When both agents say they're done, score them the same way. This

script takes the base branch, a test command, and any number of

worktree paths. For each one it prints the branch, whether the tests

pass, how many files changed and how many lines went in and out

against the point where the branch left the base. Save it as

compare-worktrees.sh next to your repo and chmod +x it:

#!/usr/bin/env bash
set -u

base=$1
test_cmd=$2
shift 2

for wt in "$@"; do
  branch=$(git -C "$wt" rev-parse --abbrev-ref HEAD)
  fork=$(git -C "$wt" merge-base "$base" HEAD)

  new=$(git -C "$wt" ls-files -o --exclude-standard | grep -c .)

  (cd "$wt" && bash -c "$test_cmd") >/dev/null 2>&1
  code=$?
  if [ "$code" -eq 0 ]; then result=PASS; else result=FAIL; fi

  stats=$(git -C "$wt" diff --numstat "$fork")
  files=$(printf '%s' "$stats" | grep -c .)
  added=$(printf '%s\n' "$stats" | awk '{s+=$1} END {print s+0}')
  removed=$(printf '%s\n' "$stats" | awk '{s+=$2} END {print s+0}')

  echo "== $wt"
  echo "   branch:  $branch"
  echo "   tests:   $result (exit $code)"
  echo "   files:   $files changed, $new untracked"
  echo "   lines:   +$added -$removed vs $base"
done

It diffs the working tree against the fork point, so it counts edits

an agent made but never committed. Brand-new files git doesn't track

yet show up in the "untracked" count instead of the line totals. That

count runs before the tests, so files the test run writes don't

inflate it. Anything the agent's own test runs left behind still

counts unless your .gitignore covers it.

The output below is not from Muse Wonder or Opus 5.5. It comes from a

throwaway toy repo (git 2.52, Python 3.14, __pycache__/ in

.gitignore) with two hand-written fixes for a broken slugify()

standing in for agent output. Fix A rewrote the function with a

regex. Fix B stripped punctuation, still produced double hyphens on

extra spaces, and left a TODO in the test file:

$ git worktree add -b bakeoff/agent-a ../agent-a
Preparing worktree (new branch 'bakeoff/agent-a')
HEAD is now at 759ecee Initial commit
$ git worktree add -b bakeoff/agent-b ../agent-b
Preparing worktree (new branch 'bakeoff/agent-b')
HEAD is now at 759ecee Initial commit
$ ../compare-worktrees.sh main "python3 -m unittest -q" \
> ../agent-a ../agent-b
== ../agent-a
   branch:  bakeoff/agent-a
   tests:   PASS (exit 0)
   files:   1 changed, 0 untracked
   lines:   +5 -1 vs main
== ../agent-b
   branch:  bakeoff/agent-b
   tests:   FAIL (exit 1)
   files:   2 changed, 0 untracked
   lines:   +6 -1 vs main

In Hermes the worktrees are plain git too, so git worktree list in

your repo prints the paths to pass in.

The script covers the first three rows. The rest you note while you

watch:

A: Claude Code, Opus 5.5 B: Claude Code, Muse Wonder C: muse (optional)
Tests pass (exit code)
Lines +/- vs base
Files touched
Wall time to "done"
Cost
Review comments before you'd merge

Wall time.

Artificial Analysis

measured Wonder's time to first token at 19.27 seconds at launch,

against a 5.52-second median for comparable models. An agent makes

many calls per task, so expect that to show.

Cost. Meta's docs warn that agents pointed at a custom provider

often show $0 cost, because they lack pricing metadata. Don't read

cost from session B's terminal. Take the run's input, cached and

output token counts and do the math: input millions × $4, cached

input millions × $0.20 (the cached rate

Handy AI

reports), output millions × $20. The per-token prices match Opus

5.5, but per-task cost can still differ. Artificial Analysis counted

130M output tokens for Wonder to run its index, against an 85M

median. For session A, Hermes shows an estimated cost per session.

Press ⌘G (Ctrl+Shift+G on Windows and Linux) to open the Review Desk

and switch to By turn. You read each agent's work turn by turn

instead of as one final diff. Risk flags mark lockfiles, CI

workflows, secrets, new binaries and edits an agent makes to its own

config.

From hermes-ide.com: a recreation of the 2.0 app with demo data. The Review Desk by turn, with a line comment for Claude Code and a drafted commit message.

A line comment goes back to the agent that wrote the line. Count

those comments: that is the last row of your scorecard. Hermes knows

less about a Custom agent than a built-in one, so if session B's

turns look thin, read it By file, or fall back to the script's

numbers and a plain git diff against your base branch.

Land the winner from the Land sheet: commit, open a pull request or

merge locally, with undo afterwards. Then close the other session.

By hand, remove the losing worktree with

git worktree remove ../bakeoff-muse (or ../bakeoff-opus).

One ticket is an anecdote. Run three to five tickets of different

shapes before you call it, because a model that wins a refactor can

lose a bug hunt.

Hermes has no status signals for Custom agents. It doesn't know the

command's approval model, so the attention inbox can't tell when

session B or C is waiting on you. Watch their terminals. Session A

keeps full status.

You're also testing Muse Wonder, not Wonder Full. The API doesn't

serve Full, so only session C, on a plan that includes it, reaches

it. And session B is Anthropic's harness on Meta's model: fair for

comparing models, maybe not how Meta tuned Wonder to work, which is

why session C exists.

Hermes IDE is not affiliated with or endorsed by Meta, Anthropic,

OpenAI or Nous Research.

Pick the ticket you'd hand an agent this week, run it through both

sessions, and land the one you'd rather review again.

I build Hermes IDE. It is free, source-available, and runs on macOS,

Windows and Linux. It ships often: 2.2.10 came out on October 12,

If you're designing the agents themselves, the

AI Agents Pocket Guide

covers the patterns behind them.

── more in #large-language-models 4 stories · sorted by recency
── more on @meta 3 stories trending now
sponsored brought to you by zahid.host 4,200+ EU-deployed projects
reading about agents? ship yours in a single git push.

Run your AI side-project on zahid.host

EU-based hosting, git-push deploys, automatic HTTPS, no cold starts. Free tier with a custom domain — perfect for shipping the agent you just read about.

$git push zahid main
→ Live at https://your-agent.zahid.host ✓
Get free account → Pricing
from €0/mo · no card required
LIVE [news/don-t-trust-anyone-s…] indexed:0 read:10min 2026-10-12 · —