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BenchLM
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Gemini 4 Argon vs Muse Ember

Updated November 9, 2026. Rank says Gemini 4 Argon is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Gemini 4 Argon has the higher public point estimate, 81.6 versus 56.24. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Google logo
Gemini 4 Argon

Google

81.6/100

Supported · Public rank #6

90% interval 78.1–85.1

Model B
Meta logo
Muse Ember

Meta

56.24/100

Estimated · Public rank #64

Conditional range 41.9–70.6

Shared results
1
Gemini 4 Argon only
34
Muse Ember only
3
Like-for-like categories
0 / 8
Supported: Gemini 4 Argon · Estimated: Muse Ember. Conditional ranges do not establish rank confidence.How the comparison works

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Chat turn cost

    1K fresh input + 500 output tokens

    Muse Ember

    Muse Ember has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Muse Ember

    Muse Ember has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    Muse Ember

    Muse Ember has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Muse Ember is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    Muse Ember is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

72.0Gemini 4 Argon—Muse Ember

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Not comparable
Gemini 4 Argon
77.7
Supported · #4/132
Muse Ember
Not ranked
Basis
BenchAlign v5.8 lane · 12 vs 3 public rows
Reading
Not comparable

Coding

Not comparable
Gemini 4 Argon
72.0
Supported · #7/158
Muse Ember
Not ranked
Basis
BenchAlign v5.8 lane · 7 vs 0 public rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 4 Argon
77.6
Supported · #5/157
Muse Ember
Not ranked
Basis
BenchAlign v5.8 lane · 7 vs 0 public rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 4 Argon
Not ranked
Muse Ember
Not ranked
Basis
BenchAlign v5.8 lane · 4 vs 1 public rows
Reading
Not comparable

Knowledge

Not comparable
Gemini 4 Argon
79.4
Supported · #9/179
Muse Ember
Not ranked
Basis
BenchAlign v5.8 lane · 2 vs 0 public rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 4 Argon
Not ranked
Muse Ember
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 4 Argon
Not ranked
Muse Ember
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 4 Argon
89.3
Unranked · 2 rankable rows
Muse Ember
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

Gemini 4 Argon
$0.007
Fits in one request
Muse Ember
$0.00035
Fits in one request

Muse Ember has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 4 Argon
$0.13
Fits in one request
Muse Ember
$0.0065
Fits in one request

Muse Ember has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Gemini 4 Argon
$0.16
Fits in one request
Muse Ember
$0.0078
Fits in one request

Muse Ember has the lower modeled cost

Costs use the listed standard API rates.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Reasoning profile

Gemini 4 Argon

Reasoning

Muse Ember

Reasoning

Weight access

Gemini 4 Argon

Proprietary

Muse Ember

Proprietary

License

Gemini 4 Argon

Proprietary

Muse Ember

Proprietary

Release date

Gemini 4 Argon

2026-09-30

Muse Ember

2026-11-09

If you already use one of these models

Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
Gemini 4 Argon has the higher public point estimate, 81.6 versus 56.24. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
Workload cost
Repository review: $0.13 vs $0.0065. Cache-heavy agent loop: $0.16 vs $0.0078.
Context tradeoff
Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Gemini 4 Argon or Muse Ember?

Gemini 4 Argon has the higher public point estimate, 81.6 versus 56.24. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemini 4 Argon or Muse Ember?

Muse Ember is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Gemini 4 Argon or Muse Ember?

Muse Ember is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemini 4 Argon or Muse Ember?

For the stated presets, chat costs $0.007 on Gemini 4 Argon and $0.00035 on Muse Ember; repository review costs $0.13 and $0.0065; the cache-heavy agent loop costs $0.16 and $0.0078. Costs use the listed standard API rates.

Which has the larger context window, Gemini 4 Argon or Muse Ember?

Both models list the same context window, 1M.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence38 rows

Agentic

  • AutomationBench

    Gemini 4 Argon51.3%
    Source
    Muse Ember—

    Not directly comparable

  • Finance Agent v2

    Gemini 4 Argon65.4%
    Source
    Muse Ember—

    Not directly comparable

  • Terminal-Bench 4.0

    Gemini 4 Argon57.40%
    Source
    Muse Ember40.90%
    Source

    Gemini 4 Argon leads this result

  • Agents' Last Exam

    Gemini 4 Argon39.5%
    Source
    Muse Ember—

    Not directly comparable

  • OSWorld 2.0

    Gemini 4 Argon69.2%
    Source
    Muse Ember—

    Not directly comparable

  • CWE-bench v1

    Gemini 4 Argon68.0%
    Source
    Muse Ember—

    Not directly comparable

  • Terminal-Bench-Science 0.1 (6x verifier timeout)

    Gemini 4 Argon57.6%
    Source
    Muse Ember—

    Not directly comparable

  • MazeBench

    Gemini 4 Argon12%
    Source
    Muse Ember—

    Not directly comparable

  • OSWorld 2.1 Offline

    Gemini 4 Argon74.7%
    Source
    Muse Ember—

    Not directly comparable

  • ExploitGym

    Gemini 4 Argon39.5%
    Source
    Muse Ember—

    Not directly comparable

  • RoboDojo Sim

    Gemini 4 Argon34.2%
    Source
    Muse Ember—

    Not directly comparable

  • CWE-bench

    Gemini 4 Argon65.0%
    Source
    Muse Ember—

    Not directly comparable

  • OSWorld 2.1

    Gemini 4 Argon—
    Muse Ember69.5%
    Source

    Not directly comparable

  • Agents' Last Exam (Linux)

    Gemini 4 Argon—
    Muse Ember61.6%
    Source

    Not directly comparable

Coding

  • ScarfBench

    Gemini 4 Argon75.2%
    Source
    Muse Ember—

    Not directly comparable

  • DeepSWE

    Gemini 4 Argon77.9%
    Source
    Muse Ember—

    Not directly comparable

  • FrontierSWE v2

    Gemini 4 Argon55.0%
    Source
    Muse Ember—

    Not directly comparable

  • Vibe Code Bench

    Gemini 4 Argon91.90%
    Source
    Muse Ember—

    Not directly comparable

  • PostTrainBench v1.1

    Gemini 4 Argon45.3%
    Source
    Muse Ember—

    Not directly comparable

  • Bug Hunt Bench

    Gemini 4 Argon40.7 fixes
    Source
    Muse Ember—

    Not directly comparable

  • FrontierCode 2.0

    Gemini 4 Argon26.3%
    Source
    Muse Ember—

    Not directly comparable

Reasoning

  • STEMBench

    Gemini 4 Argon2.44
    Source
    Muse Ember—

    Not directly comparable

  • Graphwalks BFS 128K

    Gemini 4 Argon99.7%
    Source
    Muse Ember—

    Not directly comparable

  • GraphWalks BFS 256K–1M

    Gemini 4 Argon84.2%
    Source
    Muse Ember—

    Not directly comparable

  • ARC-AGI-1

    Gemini 4 Argon98.50%
    Source
    Muse Ember—

    Not directly comparable

  • ARC-AGI-2

    Gemini 4 Argon91.3%
    Source
    Muse Ember—

    Not directly comparable

  • ARC-AGI-3

    Gemini 4 Argon46.3%
    Source
    Muse Ember—

    Not directly comparable

  • ARC-AGI-3 Provider Adapter

    Gemini 4 Argon97.30%
    Source
    Muse Ember—

    Not directly comparable

Multimodal

  • ExtractBench Pro

    Gemini 4 Argon30%
    Source
    Muse Ember—

    Not directly comparable

  • Chartography (no tools)

    Gemini 4 Argon71.6%
    Source
    Muse Ember—

    Not directly comparable

  • LVBench

    Gemini 4 Argon91.7%
    Source
    Muse Ember—

    Not directly comparable

  • Blueprint-Bench 2

    Gemini 4 Argon54.4%
    Source
    Muse Ember—

    Not directly comparable

  • PerceptionBench (tools)

    Gemini 4 Argon—
    Muse Ember80.6%
    Source

    Not directly comparable

Knowledge

  • LABBench2

    Gemini 4 Argon88.8%
    Source
    Muse Ember—

    Not directly comparable

  • Morphology-to-molecule matching

    Gemini 4 Argon25.2%
    Source
    Muse Ember—

    Not directly comparable

Instruction following

  • Gray Swan IPI (15 attempts)

    Gemini 4 Argon0.7%
    Source
    Muse Ember—

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Gemini 4 Argon85.965%
    Source
    Muse Ember—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Gemini 4 Argon78.049%
    Source
    Muse Ember—

    Not directly comparable

38 public results · 1 shared

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Last updated November 9, 2026