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BenchLM
Model A
Agents-A1

InternScience

61.3/100

Estimated · Public rank #53

90% interval 51.4–71.1

Agents-A1 vs Muse Spark 1.1

Updated August 22, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Muse Spark 1.1

Meta

76.7/100

Supported · Public rank #7

90% interval 72.6–80.9

Decision reading

Muse Spark 1.1 has the higher public score, 76.74 versus 61.25, and the 90% score intervals do not overlap.

1 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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.

  • Long documents

    Prompts that approach the documented context limit

    Muse Spark 1.1

    Muse Spark 1.1 has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

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

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

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
Agents-A1 only
5
Muse Spark 1.1 only
20
Like-for-like categories
1 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Knowledge

Like-for-like
Agents-A1
47.6
Muse Spark 1.1
62.1
Weighted basis
1 vs 1 rows
Reading
Muse Spark 1.1 leads

Agentic

Not comparable
Agents-A1
75.5
Muse Spark 1.1
80.4
Weighted basis
1 vs 2 rows
Reading
Not comparable

Coding

Not comparable
Agents-A1
Not measured
Muse Spark 1.1
61.5
Weighted basis
0 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
Agents-A1
60.2
Muse Spark 1.1
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
Agents-A1
Not measured
Muse Spark 1.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Agents-A1
Not measured
Muse Spark 1.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Agents-A1
Not measured
Muse Spark 1.1
88.4
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
Agents-A1
94.8
Muse Spark 1.1
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

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.

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

Agents-A1
API rate not published
Fits in one request
Muse Spark 1.1
API rate not published
Fits in one request

Agents-A1 has no comparable published API token rate. Muse Spark 1.1 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Agents-A1
API rate not published
Fits in one request
Muse Spark 1.1
API rate not published
Fits in one request

Agents-A1 has no comparable published API token rate. Muse Spark 1.1 has no comparable published API token rate.

Cache-heavy agent loop

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

Agents-A1
API rate not published
Fits in one request
Cached-input rate unavailable
Muse Spark 1.1
API rate not published
Fits in one request
Cached-input rate unavailable

Agents-A1 has no comparable published API token rate. Muse Spark 1.1 has no comparable published API token rate.

Specification differences

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

Context window

Maximum documented context; output-token limits may be lower.

Agents-A1

262K

Muse Spark 1.1

1M

API model ID

Agents-A1

Not sourced

Muse Spark 1.1

Not sourced

Cached-input rate

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

Agents-A1

No comparable hosted API rate

Muse Spark 1.1

No comparable hosted API rate

Documented inputs

Agents-A1

Not sourced

Muse Spark 1.1

Not sourced

Documented outputs

Agents-A1

Not sourced

Muse Spark 1.1

Not sourced

Provider availability

Agents-A1

Not sourced

Muse Spark 1.1

Not sourced

Reasoning profile

Agents-A1

Reasoning

Muse Spark 1.1

Reasoning

Weight access

Agents-A1

Open Weight

Muse Spark 1.1

Proprietary

License

Agents-A1

Open Weight

Muse Spark 1.1

Proprietary

Release date

Agents-A1

2026-06-26

Muse Spark 1.1

2026-07-09

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
Muse Spark 1.1 has the higher public score, 76.74 versus 61.25, and the 90% score intervals do not overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Muse Spark 1.1 has the larger documented window (1M).

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 evidence26 rows

Agentic

  • BrowseComp

    Agents-A175.5%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • HLE w/ tools

    Agents-A147.6%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • VITA-Bench

    Agents-A138.8%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • Terminal-Bench 2.0

    Agents-A1—
    Muse Spark 1.180%
    Source

    Not directly comparable

  • MCP Atlas

    Agents-A1—
    Muse Spark 1.188.1%
    Source

    Not directly comparable

  • Toolathlon

    Agents-A1—
    Muse Spark 1.175.6%
    Source

    Not directly comparable

  • OSWorld-Verified

    Agents-A1—
    Muse Spark 1.180.8%
    Source

    Not directly comparable

  • WebArena-Verified

    Agents-A1—
    Muse Spark 1.169%
    Source

    Not directly comparable

  • DeepSearchQA

    Agents-A1—
    Muse Spark 1.184.9%
    Source

    Not directly comparable

  • CyberGym

    Agents-A1—
    Muse Spark 1.159.0%
    Source

    Not directly comparable

  • Finance Agent v2

    Agents-A1—
    Muse Spark 1.157.2%
    Source

    Not directly comparable

  • deepSwe

    Agents-A1—
    Muse Spark 1.153.3%
    Source

    Not directly comparable

  • OSWorld 2.0

    Agents-A1—
    Muse Spark 1.114.2%
    Source

    Not directly comparable

  • JobBench

    Agents-A1—
    Muse Spark 1.154.7%
    Source

    Not directly comparable

  • Cybench

    Agents-A1—
    Muse Spark 1.192.9%
    Source

    Not directly comparable

  • ExploitGym

    Agents-A1—
    Muse Spark 1.10.8%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.0

    Agents-A1—
    Muse Spark 1.180.0%
    Source

    Not directly comparable

  • SWE-bench Pro

    Agents-A1—
    Muse Spark 1.161.5%
    Source

    Not directly comparable

Reasoning

  • LongBench v2

    Agents-A160.2%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • MRCR 1M

    Agents-A1—
    Muse Spark 1.154.1%
    Source

    Not directly comparable

Knowledge

  • HLE

    Agents-A147.6%
    Source
    Muse Spark 1.162.1%
    Source

    Muse Spark 1.1 leads this result

  • HLE w/o tools

    Agents-A1—
    Muse Spark 1.152.2%
    Source

    Not directly comparable

  • HealthBench Professional

    Agents-A1—
    Muse Spark 1.159.3%
    Source

    Not directly comparable

Multimodal

  • CharXiv

    Agents-A1—
    Muse Spark 1.188.4%
    Source

    Not directly comparable

  • BabyVision

    Agents-A1—
    Muse Spark 1.176.3%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Agents-A194.8%
    Source
    Muse Spark 1.1—

    Not directly comparable

Frequently asked questions

Which is better, Agents-A1 or Muse Spark 1.1?

Muse Spark 1.1 has the higher public score, 76.74 versus 61.25, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Agents-A1 or Muse Spark 1.1?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Agents-A1 or Muse Spark 1.1?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Agents-A1 or Muse Spark 1.1?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Agents-A1 or Muse Spark 1.1?

Muse Spark 1.1 has the larger documented context window: 1M, compared with 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 22, 2026

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