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Gemini 4 Argon vs Muse Spark 1.3

Updated November 13, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 12 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

79.03/100

Supported · Public rank #8

90% interval 75.4–82.7

Model B
Meta logo
Muse Spark 1.3

Meta

—

Evidence status unavailable

90% interval unavailable

API price per 1M tokens

Gemini 4 Argon: $2 input / $10 output

Muse Spark 1.3: $1.25 input / $4.25 output

Context window

Gemini 4 Argon: 1M

Muse Spark 1.3: 1M

Released

Gemini 4 Argon: September 30, 2026

Muse Spark 1.3: September 2, 2026

Shared results
12
Gemini 4 Argon only
27
Muse Spark 1.3 only
9
Like-for-like categories
0 / 8
Supported: Gemini 4 ArgonHow 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 Spark 1.3

    Muse Spark 1.3 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 Spark 1.3

    Muse Spark 1.3 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 Spark 1.3

    Muse Spark 1.3 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 Spark 1.3 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 Spark 1.3 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.

71.8Gemini 4 Argon—Muse Spark 1.3

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

Or describe your own job

The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.

Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.

We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy

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.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

Bars run 0–100 on each benchmark’s normalized display scale

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
74.9
Supported · #6/135
Muse Spark 1.3
Not ranked
Basis
BenchAlign v5.8 lane · 13 vs 10 public rows
Reading
Not comparable

Coding

Not comparable
Gemini 4 Argon
71.8
Supported · #9/162
Muse Spark 1.3
Not ranked
Basis
BenchAlign v5.8 lane · 9 vs 6 public rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 4 Argon
76.5
Supported · #7/160
Muse Spark 1.3
Not ranked
Basis
BenchAlign v5.8 lane · 7 vs 2 public rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 4 Argon
Not ranked
Muse Spark 1.3
Not ranked
Basis
BenchAlign v5.8 lane · 5 vs 0 public rows
Reading
Not comparable

Knowledge

Not comparable
Gemini 4 Argon
79.2
Supported · #8/180
Muse Spark 1.3
Not ranked
Basis
BenchAlign v5.8 lane · 2 vs 0 public rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 4 Argon
Not ranked
Muse Spark 1.3
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 4 Argon
Not ranked
Muse Spark 1.3
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 4 Argon
87.4
Unranked · 2 rankable rows
Muse Spark 1.3
73.6
Unranked · 2 rankable rows
Basis
Provisional lane · 2 vs 2 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 Spark 1.3
$0.00338
Fits in one request

Muse Spark 1.3 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 Spark 1.3
$0.07525
Fits in one request

Muse Spark 1.3 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 Spark 1.3
$0.0975
Fits in one request

Muse Spark 1.3 has the lower modeled cost

Costs use the listed standard API rates.

Your workload

Enter a month of tokens to price it at both models’ listed API rates.

Gemini 4 Argon
$40.00
per month
Muse Spark 1.3
$21.00
per month

Standard listed rates, no batch or volume discounts.

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

Price against score, among all priced models

Overall score against output price per 1M tokens, one model per family. Pricing as of November 12, 2026.

Gemini 4 Argon

20406080100$0.10$0.50$1$5$10$50$100Output price, $ per 1M tokens (log)Overall score
  • 2 models at the same price score higher than Gemini 4 Argon. The nearest is Gemini 4 Carbon (score 89.2, also $10.00 per 1M output tokens).
  • Muse Spark 1.3 has no overall score yet, so it is not plotted.

Gray dots are the other priced models. The dark step line is the efficiency frontier: for a model under it, another model costs no more and scores at least as high.

See Gemini 4 Argon on the full price vs performance chart

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 Spark 1.3

Reasoning

Weight access

Gemini 4 Argon

Proprietary

Muse Spark 1.3

Proprietary

License

Gemini 4 Argon

Proprietary

Muse Spark 1.3

Proprietary

Release date

Gemini 4 Argon

2026-09-30

Muse Spark 1.3

2026-09-02

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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost
Repository review: $0.13 vs $0.07525. Cache-heavy agent loop: $0.16 vs $0.0975.
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 Spark 1.3?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemini 4 Argon or Muse Spark 1.3?

Muse Spark 1.3 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 Spark 1.3?

Muse Spark 1.3 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 Spark 1.3?

For the stated presets, chat costs $0.007 on Gemini 4 Argon and $0.00337 on Muse Spark 1.3; repository review costs $0.13 and $0.07525; the cache-heavy agent loop costs $0.16 and $0.0975. Costs use the listed standard API rates.

Which has the larger context window, Gemini 4 Argon or Muse Spark 1.3?

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 evidence48 rows

Agentic

  • AutomationBench

    Gemini 4 Argon51.3%
    Source
    Muse Spark 1.349.4%
    Source

    Gemini 4 Argon leads this result

  • Finance Agent v2

    Gemini 4 Argon65.4%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • Terminal-Bench 4.0

    Gemini 4 Argon57.40%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • Agents' Last Exam

    Gemini 4 Argon39.5%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • OSWorld 2.0

    Gemini 4 Argon69.2%
    Source
    Muse Spark 1.366.9%
    Source

    Gemini 4 Argon leads this result

  • CWE-bench v1

    Shared source
    Gemini 4 Argon68.0%
    Muse Spark 1.355.0%

    Gemini 4 Argon leads this result

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

    Gemini 4 Argon57.6%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • MazeBench

    Gemini 4 Argon12%
    Source
    Muse Spark 1.30%
    Source

    Gemini 4 Argon leads this result

  • OSWorld 2.1 Offline

    Gemini 4 Argon74.7%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • ExploitGym

    Gemini 4 Argon39.5%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • RoboDojo Sim

    Gemini 4 Argon34.2%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • CWE-bench

    Gemini 4 Argon65.0%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Gemini 4 Argon57.7%
    Source
    Muse Spark 1.372.3%
    Source

    Muse Spark 1.3 leads this result

  • Terminal-Bench 4.1

    Gemini 4 Argon—
    Muse Spark 1.314.90%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Gemini 4 Argon—
    Muse Spark 1.388.8%
    Source

    Not directly comparable

  • JobBench

    Gemini 4 Argon—
    Muse Spark 1.364.9%
    Source

    Not directly comparable

  • DeepSearchQA

    Gemini 4 Argon—
    Muse Spark 1.389.4%
    Source

    Not directly comparable

  • ApprenticeBench

    Gemini 4 Argon—
    Muse Spark 1.319%
    Source

    Not directly comparable

Coding

  • ScarfBench

    Gemini 4 Argon75.2%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • DeepSWE

    Gemini 4 Argon77.9%
    Source
    Muse Spark 1.375.4%
    Source

    Gemini 4 Argon leads this result

  • FrontierSWE v2

    Shared source
    Gemini 4 Argon55.0%
    Muse Spark 1.325.8%

    Gemini 4 Argon leads this result

  • Vibe Code Bench

    Gemini 4 Argon91.90%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • PostTrainBench v1.1

    Gemini 4 Argon45.3%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • Bug Hunt Bench

    Shared source
    Gemini 4 Argon40.7 fixes
    Muse Spark 1.332.2 fixes

    Gemini 4 Argon leads this result

  • FrontierCode 2.0

    Gemini 4 Argon26.3%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • CursorBench 4.0

    Shared source
    Gemini 4 Argon52.9%
    Muse Spark 1.341.6%

    Gemini 4 Argon leads this result

  • PostTrainBench

    Gemini 4 Argon40.4%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • Terminal-Bench 2.1

    Gemini 4 Argon—
    Muse Spark 1.388.8%
    Source

    Not directly comparable

  • SWE-Atlas Codebase QnA

    Gemini 4 Argon—
    Muse Spark 1.359.4%
    Source

    Not directly comparable

Reasoning

  • STEMBench

    Gemini 4 Argon2.44
    Source
    Muse Spark 1.3—

    Not directly comparable

  • Graphwalks BFS 128K

    Gemini 4 Argon99.7%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • GraphWalks BFS 256K–1M

    Gemini 4 Argon84.2%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • ARC-AGI-1

    Gemini 4 Argon98.50%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • ARC-AGI-2

    Gemini 4 Argon91.3%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • ARC-AGI-3

    Gemini 4 Argon46.3%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • ARC-AGI-3 Provider Adapter

    Gemini 4 Argon97.30%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • MRCR v2 256K-512K

    Gemini 4 Argon—
    Muse Spark 1.398.5%
    Source

    Not directly comparable

  • MRCR v2 512K-1M

    Gemini 4 Argon—
    Muse Spark 1.398.1%
    Source

    Not directly comparable

Multimodal

  • ParseBench-2

    Gemini 4 Argon84.20%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • ExtractBench Pro

    Gemini 4 Argon30%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • Chartography (no tools)

    Gemini 4 Argon71.6%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • LVBench

    Gemini 4 Argon91.7%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • Blueprint-Bench 2

    Gemini 4 Argon54.4%
    Source
    Muse Spark 1.3—

    Not directly comparable

Knowledge

  • LABBench2

    Gemini 4 Argon88.8%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • Morphology-to-molecule matching

    Gemini 4 Argon25.2%
    Source
    Muse Spark 1.3—

    Not directly comparable

Instruction following

  • Gray Swan IPI (15 attempts)

    Shared source
    Gemini 4 Argon0.7%
    Muse Spark 1.315.9%

    Gemini 4 Argon leads this result

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    Gemini 4 Argon85.965%
    Muse Spark 1.374.035%

    Gemini 4 Argon leads this result

  • FrontierMath v2 (Tier 4)

    Shared source
    Gemini 4 Argon78.049%
    Muse Spark 1.346.341%

    Gemini 4 Argon leads this result

48 public results · 12 shared

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Last updated November 13, 2026