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BenchLM
InclusionAI logo
Model A
Ling 2.6 Flash

InclusionAI

43.68/100

Estimated · Public rank #176

90% interval 32.2–55.2

Ling 2.6 Flash vs Muse Spark

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Meta logo
Model B
Muse Spark

Meta

68.39/100

Supported · Public rank #30

90% interval 60.0–76.8

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 2.6 Flash is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    Ling 2.6 Flash is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    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 2.6 Flash only
0
Muse Spark only
24
Like-for-like categories
0 / 8

4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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

Directional only
Ling 2.6 Flash
39.9
Estimated · #122/151
Muse Spark
58.8
Supported · #31/151
Basis
BenchAlign lane · 0 vs 5 public rows
Reading
Directional only

Coding

Directional only
Ling 2.6 Flash
42.7
Estimated · #127/183
Muse Spark
59.2
Supported · #28/183
Basis
BenchAlign lane · 0 vs 4 public rows
Reading
Directional only

Knowledge

Directional only
Ling 2.6 Flash
41.4
Estimated · #132/181
Muse Spark
65.6
Supported · #23/181
Basis
BenchAlign lane · 0 vs 5 public rows
Reading
Directional only

Instruction following

Directional only
Ling 2.6 Flash
46.6
#86/120
Muse Spark
92.9
#8/120
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Reasoning

Not comparable
Ling 2.6 Flash
41.2
Unranked · 2 rankable rows
Muse Spark
45.9
Unranked · 3 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Math

Not comparable
Ling 2.6 Flash
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 2.6 Flash
Not ranked
Muse Spark
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Ling 2.6 Flash
Not ranked
Muse Spark
76.8
#14/48
Basis
Provisional lane · 0 vs 2 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 2.6 Flash
API rate not published
Fits in one request
Muse Spark
API rate not published
Fits in one request

Ling 2.6 Flash 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 2.6 Flash
API rate not published
Fits in one request
Muse Spark
API rate not published
Fits in one request

Ling 2.6 Flash 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 2.6 Flash
API rate not published
Fits in one request
Cached-input rate unavailable
Muse Spark
API rate not published
Fits in one request
Cached-input rate unavailable

Ling 2.6 Flash 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.

Context window

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

Ling 2.6 Flash

262K

Muse Spark

262K

API model ID

Ling 2.6 Flash

Not sourced

Muse Spark

Not sourced

Cached-input rate

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

Ling 2.6 Flash

No comparable hosted API rate

Muse Spark

No comparable hosted API rate

Documented inputs

Ling 2.6 Flash

Not sourced

Muse Spark

Not sourced

Documented outputs

Ling 2.6 Flash

Not sourced

Muse Spark

Not sourced

Provider availability

Ling 2.6 Flash

Not sourced

Muse Spark

Not sourced

Reasoning profile

Ling 2.6 Flash

Non-Reasoning

Muse Spark

Reasoning

Weight access

Ling 2.6 Flash

Open Weight

Muse Spark

Proprietary

License

Ling 2.6 Flash

Open Weight

Muse Spark

Proprietary

Release date

Ling 2.6 Flash

2026-04-21

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 2.6 Flash—
    Muse Spark59%
    Source

    Not directly comparable

  • τ²-bench results

    Ling 2.6 Flash—
    Muse Spark91.5%
    Source

    Not directly comparable

  • DeepSearchQA

    Ling 2.6 Flash—
    Muse Spark74.8%
    Source

    Not directly comparable

  • CyberGym

    Ling 2.6 Flash—
    Muse Spark43.5%
    Source

    Not directly comparable

  • Claw-Eval

    Ling 2.6 Flash—
    Muse Spark63.8%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Ling 2.6 Flash—
    Muse Spark77.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    Ling 2.6 Flash—
    Muse Spark52.4%
    Source

    Not directly comparable

  • LiveCodeBench Pro

    Ling 2.6 Flash—
    Muse Spark80.0%
    Source

    Not directly comparable

  • Vibe Code Bench

    Ling 2.6 Flash—
    Muse Spark19.67%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Ling 2.6 Flash—
    Muse Spark42.5%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    Ling 2.6 Flash—
    Muse Spark89.5%
    Source

    Not directly comparable

  • HLE

    Ling 2.6 Flash—
    Muse Spark50.4%
    Source

    Not directly comparable

  • HLE w/o tools

    Ling 2.6 Flash—
    Muse Spark42.8%
    Source

    Not directly comparable

  • HealthBench Hard

    Ling 2.6 Flash—
    Muse Spark42.8%
    Source

    Not directly comparable

  • MedXpertQA (Text)

    Ling 2.6 Flash—
    Muse Spark52.6%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Ling 2.6 Flash—
    Muse Spark39.000%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Ling 2.6 Flash—
    Muse Spark14.600%
    Source

    Not directly comparable

Multimodal

  • CharXiv

    Ling 2.6 Flash—
    Muse Spark86.4%
    Source

    Not directly comparable

  • MMMU-Pro

    Ling 2.6 Flash—
    Muse Spark80.4%
    Source

    Not directly comparable

  • ERQA

    Ling 2.6 Flash—
    Muse Spark64.7%
    Source

    Not directly comparable

  • SimpleVQA

    Ling 2.6 Flash—
    Muse Spark71.3%
    Source

    Not directly comparable

  • ScreenSpot Pro

    Ling 2.6 Flash—
    Muse Spark84.1%
    Source

    Not directly comparable

  • ZeroBench

    Ling 2.6 Flash—
    Muse Spark33.0%
    Source

    Not directly comparable

  • MedXpertQA (MM)

    Ling 2.6 Flash—
    Muse Spark78.4%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Ling 2.6 Flash 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 2.6 Flash or Muse Spark?

Muse Spark scores higher for coding on the public lane, 59.2 to 42.7. Ling 2.6 Flash is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Ling 2.6 Flash or Muse Spark?

Muse Spark scores higher for agentic tasks on the public lane, 58.8 to 39.9. Ling 2.6 Flash is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Ling 2.6 Flash 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 2.6 Flash 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 4, 2026

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