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BenchLM
Model A
Atria Dawn Preview

Shanghai Artificial Intelligence Laboratory

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Evidence status unavailable

90% interval unavailable

Atria Dawn Preview vs Muse Spark 1.3

Updated September 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

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Model B
Muse Spark 1.3

Meta

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Evidence status unavailable

90% interval unavailable

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.

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

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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.

  • Long documents

    Prompts that approach the documented context limit

    Muse Spark 1.3

    Muse Spark 1.3 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

    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

  • 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
5
Atria Dawn Preview only
8
Muse Spark 1.3 only
7
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
Atria Dawn Preview
57.9
Estimated · #31/153
Muse Spark 1.3
Not ranked
Basis
BenchAlign lane · 11 vs 6 public rows
Reading
Not comparable

Coding

Not comparable
Atria Dawn Preview
51.6
Estimated · #51/152
Muse Spark 1.3
Not ranked
Basis
BenchAlign lane · 2 vs 4 public rows
Reading
Not comparable

Reasoning

Not comparable
Atria Dawn Preview
Not ranked
Muse Spark 1.3
78.0
#5/20
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Atria Dawn Preview
Not ranked
Muse Spark 1.3
Not ranked
Basis
BenchAlign lane · 0 vs 0 public rows
Reading
Not comparable

Math

Not comparable
Atria Dawn Preview
Not ranked
Muse Spark 1.3
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Atria Dawn Preview
Not ranked
Muse Spark 1.3
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Atria Dawn Preview
Not ranked
Muse Spark 1.3
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Atria Dawn Preview
Not ranked
Muse Spark 1.3
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.

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

Atria Dawn Preview
Self-hosted; infrastructure cost varies
Fits in one request
Muse Spark 1.3
$0.00338
Fits in one request

Atria Dawn Preview has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Atria Dawn Preview
Self-hosted; infrastructure cost varies
Fits in one request
Muse Spark 1.3
$0.07525
Fits in one request

Atria Dawn Preview has no comparable published API token rate.

Cache-heavy agent loop

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

Atria Dawn Preview
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
Muse Spark 1.3
$0.0975
Fits in one request

Atria Dawn Preview 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.

Reasoning profile

Atria Dawn Preview

Reasoning

Muse Spark 1.3

Reasoning

Weight access

Atria Dawn Preview

Open Weight

Muse Spark 1.3

Proprietary

License

Atria Dawn Preview

Open Weight

Muse Spark 1.3

Proprietary

Release date

Atria Dawn Preview

2026-09-14

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
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Muse Spark 1.3 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 evidence20 rows

Agentic

  • AutomationBench

    Atria Dawn Preview53.8%
    Source
    Muse Spark 1.349.4%
    Source

    Atria Dawn Preview leads this result

  • BFCL v4

    Atria Dawn Preview77.0%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • CyberGym

    Atria Dawn Preview86.5%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • DeepSearchQA

    Atria Dawn Preview96.0%
    Source
    Muse Spark 1.389.4%
    Source

    Atria Dawn Preview leads this result

  • BrowseComp

    Atria Dawn Preview92.5%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • skillsBench

    Atria Dawn Preview66.4%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • MLE-Bench Lite

    Atria Dawn Preview86.2%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • WideResearch

    Atria Dawn Preview81.9%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • τ³-bench results

    Atria Dawn Preview41.2%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • Terminal-Bench 2.1

    Atria Dawn Preview78.3%
    Source
    Muse Spark 1.388.8%
    Source

    Muse Spark 1.3 leads this result

  • JobBench

    Atria Dawn Preview50.3%
    Source
    Muse Spark 1.364.9%
    Source

    Muse Spark 1.3 leads this result

  • OSWorld 2.0

    Atria Dawn Preview—
    Muse Spark 1.366.9%
    Source

    Not directly comparable

  • ApprenticeBench

    Atria Dawn Preview—
    Muse Spark 1.319%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.1

    Atria Dawn Preview78.3%
    Source
    Muse Spark 1.388.8%
    Source

    Muse Spark 1.3 leads this result

  • SWE-bench Pro

    Atria Dawn Preview59.6%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • DeepSWE

    Atria Dawn Preview—
    Muse Spark 1.375.4%
    Source

    Not directly comparable

  • SWE-Atlas Codebase QnA

    Atria Dawn Preview—
    Muse Spark 1.359.4%
    Source

    Not directly comparable

  • cursorBench40

    Atria Dawn Preview—
    Muse Spark 1.341.6%
    Source

    Not directly comparable

Reasoning

  • MRCR v2 256K-512K

    Atria Dawn Preview—
    Muse Spark 1.398.5%
    Source

    Not directly comparable

  • MRCR v2 512K-1M

    Atria Dawn Preview—
    Muse Spark 1.398.1%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Atria Dawn Preview 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, Atria Dawn Preview 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, Atria Dawn Preview 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, Atria Dawn Preview or Muse Spark 1.3?

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, Atria Dawn Preview or Muse Spark 1.3?

Muse Spark 1.3 has the larger documented context window: 1M, compared with 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 14, 2026

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