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BenchLM
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Model A
Ling 3.0 Flash VL

InclusionAI

—

Evidence status unavailable

90% interval unavailable

Ling 3.0 Flash VL vs Muse Spark

Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

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Model B
Muse Spark

Meta

67.69/100

Supported · Public rank #30

90% interval 58.4–77.0

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Ling 3.0 Flash VL 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

    Ling 3.0 Flash VL 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

  • 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
0
Ling 3.0 Flash VL only
0
Muse Spark only
24
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
Ling 3.0 Flash VL
Not ranked
Muse Spark
56.1
Supported · #34/152
Basis
BenchAlign lane · 0 vs 5 public rows
Reading
Not comparable

Coding

Not comparable
Ling 3.0 Flash VL
Not ranked
Muse Spark
59.0
Supported · #25/151
Basis
BenchAlign lane · 0 vs 4 public rows
Reading
Not comparable

Reasoning

Not comparable
Ling 3.0 Flash VL
74.8
Unranked · 2 rankable rows
Muse Spark
45.1
Unranked · 3 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Ling 3.0 Flash VL
Not ranked
Muse Spark
65.4
Supported · #24/183
Basis
BenchAlign lane · 0 vs 5 public rows
Reading
Not comparable

Math

Not comparable
Ling 3.0 Flash VL
Not ranked
Muse Spark
55.3
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Ling 3.0 Flash VL
Not ranked
Muse Spark
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Ling 3.0 Flash VL
76.3
Unranked · 1 rankable row
Muse Spark
77.5
#14/48
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Ling 3.0 Flash VL
Not ranked
Muse Spark
93.2
#8/123
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

Ling 3.0 Flash VL
Self-hosted; infrastructure cost varies
Fits in one request
Muse Spark
API rate not published
Fits in one request

Ling 3.0 Flash VL has no comparable published API token rate. Muse Spark has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Ling 3.0 Flash VL
Self-hosted; infrastructure cost varies
Fits in one request
Muse Spark
API rate not published
Fits in one request

Ling 3.0 Flash VL has no comparable published API token rate. Muse Spark has no comparable published API token rate.

Cache-heavy agent loop

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

Ling 3.0 Flash VL
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
Muse Spark
API rate not published
Fits in one request
Cached-input rate unavailable

Ling 3.0 Flash VL has no comparable published API token rate. Muse Spark 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.

Cached-input rate

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

Ling 3.0 Flash VL

No comparable hosted API rate

InclusionAI Ling 3.0 Flash VL model card

Muse Spark

No comparable hosted API rate

Documented inputs

Ling 3.0 Flash VL

Not sourced

Muse Spark

Not sourced

Documented outputs

Ling 3.0 Flash VL

Not sourced

Muse Spark

Not sourced

Provider availability

Ling 3.0 Flash VL

Not sourced

Muse Spark

Not sourced

Reasoning profile

Ling 3.0 Flash VL

Reasoning

Muse Spark

Reasoning

Weight access

Ling 3.0 Flash VL

Open Weight

Muse Spark

Proprietary

License

Ling 3.0 Flash VL

Open Weight

Muse Spark

Proprietary

Release date

Ling 3.0 Flash VL

2026-09-04

Muse Spark

2026-04-08

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Both models list 262K.

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 evidence24 rows

Agentic

  • Terminal-Bench 2.0

    Ling 3.0 Flash VL—
    Muse Spark59%
    Source

    Not directly comparable

  • τ²-bench results

    Ling 3.0 Flash VL—
    Muse Spark91.5%
    Source

    Not directly comparable

  • DeepSearchQA

    Ling 3.0 Flash VL—
    Muse Spark74.8%
    Source

    Not directly comparable

  • CyberGym

    Ling 3.0 Flash VL—
    Muse Spark43.5%
    Source

    Not directly comparable

  • Claw-Eval

    Ling 3.0 Flash VL—
    Muse Spark63.8%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Ling 3.0 Flash VL—
    Muse Spark77.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    Ling 3.0 Flash VL—
    Muse Spark52.4%
    Source

    Not directly comparable

  • LiveCodeBench Pro

    Ling 3.0 Flash VL—
    Muse Spark80.0%
    Source

    Not directly comparable

  • Vibe Code Bench

    Ling 3.0 Flash VL—
    Muse Spark19.67%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Ling 3.0 Flash VL—
    Muse Spark42.5%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    Ling 3.0 Flash VL—
    Muse Spark89.5%
    Source

    Not directly comparable

  • HLE

    Ling 3.0 Flash VL—
    Muse Spark50.4%
    Source

    Not directly comparable

  • HLE w/o tools

    Ling 3.0 Flash VL—
    Muse Spark42.8%
    Source

    Not directly comparable

  • HealthBench Hard

    Ling 3.0 Flash VL—
    Muse Spark42.8%
    Source

    Not directly comparable

  • MedXpertQA (Text)

    Ling 3.0 Flash VL—
    Muse Spark52.6%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Ling 3.0 Flash VL—
    Muse Spark39.000%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Ling 3.0 Flash VL—
    Muse Spark14.600%
    Source

    Not directly comparable

Multimodal

  • CharXiv

    Ling 3.0 Flash VL—
    Muse Spark86.4%
    Source

    Not directly comparable

  • MMMU-Pro

    Ling 3.0 Flash VL—
    Muse Spark80.4%
    Source

    Not directly comparable

  • ERQA

    Ling 3.0 Flash VL—
    Muse Spark64.7%
    Source

    Not directly comparable

  • SimpleVQA

    Ling 3.0 Flash VL—
    Muse Spark71.3%
    Source

    Not directly comparable

  • ScreenSpot Pro

    Ling 3.0 Flash VL—
    Muse Spark84.1%
    Source

    Not directly comparable

  • ZeroBench

    Ling 3.0 Flash VL—
    Muse Spark33.0%
    Source

    Not directly comparable

  • MedXpertQA (MM)

    Ling 3.0 Flash VL—
    Muse Spark78.4%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Ling 3.0 Flash VL or Muse Spark?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Ling 3.0 Flash VL or Muse Spark?

Ling 3.0 Flash VL is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Ling 3.0 Flash VL or Muse Spark?

Ling 3.0 Flash VL is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Ling 3.0 Flash VL or Muse Spark?

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, Ling 3.0 Flash VL or Muse Spark?

Both models list the same context window, 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 10, 2026

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