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BenchLM

Muse Spark 1.2 vs Pareto 26.9

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.

1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Meta logo
Model A
Muse Spark 1.2

Meta

70.28/100

Estimated · Public rank #16

90% interval 58.9–76.0

Model B
Pareto 26.9

Unbiased

—

Evidence status unavailable

90% interval unavailable

Updated September 18, 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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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 1.2

    Muse Spark 1.2 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 1.2

    Muse Spark 1.2 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 1.2

    Muse Spark 1.2 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

    Pareto 26.9 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

    Pareto 26.9 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

    Not enough matched evidence

    A complete context comparison is not sourced.

    Confidence: limited

What is actually comparable

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

Shared results
1
Muse Spark 1.2 only
7
Pareto 26.9 only
3
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, 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
Muse Spark 1.2
61.0
Supported · #16/154
Pareto 26.9
Not ranked
Basis
BenchAlign lane · 2 vs 1 public rows
Reading
Not comparable

Coding

Not comparable
Muse Spark 1.2
60.0
Supported · #21/154
Pareto 26.9
Not ranked
Basis
BenchAlign lane · 5 vs 1 public rows
Reading
Not comparable

Reasoning

Not comparable
Muse Spark 1.2
75.3
Unranked · 2 rankable rows
Pareto 26.9
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Muse Spark 1.2
70.7
Estimated · #10/184
Pareto 26.9
Not ranked
Basis
BenchAlign lane · 1 vs 1 public rows
Reading
Not comparable

Math

Not comparable
Muse Spark 1.2
Not ranked
Pareto 26.9
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Muse Spark 1.2
Not ranked
Pareto 26.9
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Muse Spark 1.2
Not ranked
Pareto 26.9
Not ranked
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Muse Spark 1.2
Not ranked
Pareto 26.9
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

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

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.

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

Muse Spark 1.2
$0.00338
Fits in one request
Pareto 26.9
$0.00625
Fit state unavailable

Muse Spark 1.2 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Muse Spark 1.2
$0.07525
Fits in one request
Pareto 26.9
$0.1475
Fit state unavailable

Muse Spark 1.2 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Muse Spark 1.2
$0.0975
Fits in one request
Pareto 26.9
$0.175
Fit state unavailable

Muse Spark 1.2 has the lower modeled cost

Costs use the listed standard API rates.

Specification differences

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

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Muse Spark 1.2

$0.15 per 1M cached input tokens

Meta: Muse Spark 1.2 model page

Pareto 26.9

$0.25 per 1M cached input tokens

Unbiased pricing

Documented inputs

Muse Spark 1.2

Not sourced

Pareto 26.9

Not sourced

Documented outputs

Muse Spark 1.2

Not sourced

Pareto 26.9

Not sourced

Provider availability

Muse Spark 1.2

Not sourced

Pareto 26.9

Not sourced

Reasoning profile

Muse Spark 1.2

Reasoning

Pareto 26.9

Reasoning

Weight access

Muse Spark 1.2

Proprietary

Pareto 26.9

Proprietary

License

Muse Spark 1.2

Proprietary

Pareto 26.9

Proprietary

Release date

Muse Spark 1.2

2026-08-05

Pareto 26.9

2026-09-17

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: $0.07525 vs $0.1475. Cache-heavy agent loop: $0.0975 vs $0.175.
Context tradeoff
A complete documented context comparison is not available.

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

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

Browse raw public benchmark evidence11 rows

Agentic

  • Terminal-Bench 2.1

    Muse Spark 1.282.9%
    Source
    Pareto 26.9—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Muse Spark 1.269.7%
    Source
    Pareto 26.9—

    Not directly comparable

  • Terminal-Bench 4.0

    Muse Spark 1.2—
    Pareto 26.951.00%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.1

    Muse Spark 1.282.9%
    Source
    Pareto 26.9—

    Not directly comparable

  • DeepSWE

    Muse Spark 1.259.3%
    Source
    Pareto 26.974.0%
    Source

    Pareto 26.9 leads this result

  • VulcanBench v3

    Muse Spark 1.287.0%
    Source
    Pareto 26.9—

    Not directly comparable

  • FrontierSWE v2

    Muse Spark 1.212.0%
    Source
    Pareto 26.9—

    Not directly comparable

  • SWE-bench (Vals)

    Muse Spark 1.286.6%
    Source
    Pareto 26.9—

    Not directly comparable

Knowledge

  • MMLU-Pro (Vals)

    Muse Spark 1.288.3%
    Source
    Pareto 26.9—

    Not directly comparable

  • HLE w/o tools

    Muse Spark 1.2—
    Pareto 26.949%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Muse Spark 1.2—
    Pareto 26.978%
    Source

    Not directly comparable

Questions

Which is better, Muse Spark 1.2 or Pareto 26.9?

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, Muse Spark 1.2 or Pareto 26.9?

Pareto 26.9 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Muse Spark 1.2 or Pareto 26.9?

Pareto 26.9 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Muse Spark 1.2 or Pareto 26.9?

For the stated presets, chat costs $0.00337 on Muse Spark 1.2 and $0.00625 on Pareto 26.9; repository review costs $0.07525 and $0.1475; the cache-heavy agent loop costs $0.0975 and $0.175. Costs use the listed standard API rates.

Which has the larger context window, Muse Spark 1.2 or Pareto 26.9?

A complete documented context-window comparison is not available.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 18, 2026

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