Ling 3.1 Flash vs Muse Spark 1.1

Updated November 13, 2026. Rank says Ling 3.1 Flash is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Ling 3.1 Flash has the higher public point estimate, 64.65 versus 63.49. Their conditional score ranges overlap. These ranges do not establish rank confidence. 7 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Ling 3.1 Flash

InclusionAI

64.65/100

Estimated · Public rank #30

Conditional range 50.3–79.0

Model B

Muse Spark 1.1

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
Ling 3.1 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 3.1 Flash is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

56.8Ling 3.1 Flash51.4Muse Spark 1.1

Directional only · BenchAlign v5.8

Ling 3.1 Flash has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

Or describe your own job

The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.

What do you need a model for?

Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.

We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy

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.

HLEKnowledge
Normalized gap 24.7
Ling 3.1 Flash:37.4%
Muse Spark 1.1:62.1%
Ling 3.1 Flash source Muse Spark 1.1 source
SWE-bench ProCoding
Normalized gap 7.2
Ling 3.1 Flash:68.7%
Muse Spark 1.1:61.5%
Ling 3.1 Flash source Muse Spark 1.1 source

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, 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 3.1 Flash: 61.4; Estimated · #30/135
Muse Spark 1.1: 56.9; Supported · #36/135
Basis: BenchAlign v5.8 lane · 10 vs 14 public rows
Reading: Directional only

Coding

Directional only

Ling 3.1 Flash: 56.8; Estimated · #29/162
Muse Spark 1.1: 51.4; Supported · #41/162
Basis: BenchAlign v5.8 lane · 5 vs 4 public rows
Reading: Directional only

Reasoning

Directional only

Ling 3.1 Flash: 61.4; Estimated · #28/160
Muse Spark 1.1: 58.9; Estimated · #39/160
Basis: BenchAlign v5.8 lane · 0 vs 1 public rows
Reading: Directional only

Multimodal

Not comparable

Ling 3.1 Flash: Not ranked
Muse Spark 1.1: Not ranked
Basis: BenchAlign v5.8 lane · 0 vs 2 public rows
Reading: Not comparable

Knowledge

Not comparable

Ling 3.1 Flash: Not ranked
Muse Spark 1.1: 66.7; Supported · #23/180
Basis: BenchAlign v5.8 lane · 2 vs 5 public rows
Reading: Not comparable

Multilingual

Not comparable

Ling 3.1 Flash: Not ranked
Muse Spark 1.1: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Ling 3.1 Flash: Not ranked
Muse Spark 1.1: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Ling 3.1 Flash: Not ranked
Muse Spark 1.1: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Ling 3.1 Flash and Muse Spark 1.1
Category	Ling 3.1 Flash	Muse Spark 1.1	Basis	Reading
Agentic	61.4Estimated · #30/135	56.9Supported · #36/135	Directional onlyBenchAlign v5.8 lane · 10 vs 14 public rows	Directional only
Coding	56.8Estimated · #29/162	51.4Supported · #41/162	Directional onlyBenchAlign v5.8 lane · 5 vs 4 public rows	Directional only
Reasoning	61.4Estimated · #28/160	58.9Estimated · #39/160	Directional onlyBenchAlign v5.8 lane · 0 vs 1 public rows	Directional only
Multimodal	Not ranked	Not ranked	Not comparableBenchAlign v5.8 lane · 0 vs 2 public rows	Not comparable
Knowledge	Not ranked	66.7Supported · #23/180	Not comparableBenchAlign v5.8 lane · 2 vs 5 public rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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 3.1 Flash: API rate not published; Fits in one request
Muse Spark 1.1: API rate not published; Fits in one request

Ling 3.1 Flash 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

Ling 3.1 Flash: API rate not published; Fits in one request
Muse Spark 1.1: API rate not published; Fits in one request

Ling 3.1 Flash 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

Ling 3.1 Flash: 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

Ling 3.1 Flash has no comparable published API token rate. Muse Spark 1.1 has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

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

SpecificationLing 3.1 FlashMuse Spark 1.1

Context window

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

Ling 3.1 Flash

262K

InclusionAI Ling 3.1 Flash released configuration

Muse Spark 1.1

API model ID

Ling 3.1 Flash

inclusionai/ling-3.1-flash

Vercel AI Gateway Ling 3.1 Flash route

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.

Ling 3.1 Flash

No comparable hosted API rate

Vercel AI Gateway availability

Muse Spark 1.1

No comparable hosted API rate

Documented inputs

Ling 3.1 Flash

text

InclusionAI Ling 3.1 Flash model card

Muse Spark 1.1

text, image, video, document

Meta Muse Spark 1.1 model page

Documented outputs

Ling 3.1 Flash

text

InclusionAI Ling 3.1 Flash model card

Muse Spark 1.1

text

Meta Muse Spark 1.1 model page

Provider availability

Ling 3.1 Flash

Generally Available · Hugging Face weights, SGLang, vLLM, Vercel AI Gateway API via Novita

Vercel AI Gateway Ling 3.1 Flash route

Muse Spark 1.1

Not sourced

Reasoning profile

Ling 3.1 Flash

Reasoning

Muse Spark 1.1

Reasoning

Weight access

Ling 3.1 Flash

Open Weight

Muse Spark 1.1

Proprietary

License

Ling 3.1 Flash

Open Weight

Muse Spark 1.1

Proprietary

Release date

Ling 3.1 Flash

2026-09-29

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: Ling 3.1 Flash has the higher public point estimate, 64.65 versus 63.49. Their conditional score ranges overlap. These ranges do not establish rank confidence.
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.

Questions

Which is better, Ling 3.1 Flash or Muse Spark 1.1?

Ling 3.1 Flash has the higher public point estimate, 64.65 versus 63.49. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Ling 3.1 Flash or Muse Spark 1.1?

Ling 3.1 Flash scores higher for coding on the public lane, 56.8 to 51.4. Ling 3.1 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 3.1 Flash or Muse Spark 1.1?

Ling 3.1 Flash scores higher for agentic tasks on the public lane, 61.4 to 56.9. Ling 3.1 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 3.1 Flash 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, Ling 3.1 Flash or Muse Spark 1.1?

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

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Ling 3.1 Flash

API / mo (Novita AI)$900

Self-host / mo$27,962

Break-evenNot reached at 70% load

Muse Spark 1.1

API / moNo API price on file

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Model the full break-even

Benchmark evidence

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

Browse raw public benchmark evidence36 rows

Agentic

skillsBench
Ling 3.1 Flash68.7%
Source
Muse Spark 1.1—
Not directly comparable
AutomationBench
Ling 3.1 Flash52.5%
Source
Muse Spark 1.1—
Not directly comparable
CyberGym
Ling 3.1 Flash87.9%
Source
Muse Spark 1.159.0%
Source
Ling 3.1 Flash leads this result
Finance Agent v2
Ling 3.1 Flash57.9%
Source
Muse Spark 1.157.2%
Source
Ling 3.1 Flash leads this result
DRACO
Ling 3.1 Flash85.5%
Source
Muse Spark 1.1—
Not directly comparable
Terminal-Bench 2.1
Ling 3.1 Flash81.2%
Source
Muse Spark 1.180.0%
Source
Ling 3.1 Flash leads this result
τ³-bench results
Ling 3.1 Flash47.8%
Source
Muse Spark 1.1—
Not directly comparable
MCP Atlas
Ling 3.1 Flash83%
Source
Muse Spark 1.188.1%
Source
Muse Spark 1.1 leads this result
BrowseComp
Ling 3.1 Flash91.7%
Source
Muse Spark 1.1—
Not directly comparable
WideResearch
Ling 3.1 Flash83.3%
Source
Muse Spark 1.1—
Not directly comparable
Toolathlon
Ling 3.1 Flash—
Muse Spark 1.175.6%
Source
Not directly comparable
OSWorld-Verified
Ling 3.1 Flash—
Muse Spark 1.180.8%
Source
Not directly comparable
WebArena-Verified
Ling 3.1 Flash—
Muse Spark 1.169%
Source
Not directly comparable
DeepSearchQA
Ling 3.1 Flash—
Muse Spark 1.184.9%
Source
Not directly comparable
deepSwe
Ling 3.1 Flash—
Muse Spark 1.153.3%
Source
Not directly comparable
OSWorld 2.0
Ling 3.1 Flash—
Muse Spark 1.114.2%
Source
Not directly comparable
JobBench
Ling 3.1 Flash—
Muse Spark 1.154.7%
Source
Not directly comparable
Cybench
Ling 3.1 Flash—
Muse Spark 1.192.9%
Source
Not directly comparable
ExploitGym
Ling 3.1 Flash—
Muse Spark 1.10.8%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Ling 3.1 Flash—
Muse Spark 1.169.3%
Source
Not directly comparable

Coding

terminalBench4
Ling 3.1 Flash46.0%
Source
Muse Spark 1.1—
Not directly comparable
SWE-Atlas Codebase QnA
Ling 3.1 Flash64.5%
Source
Muse Spark 1.1—
Not directly comparable
SWE Multilingual
Ling 3.1 Flash88.5%
Source
Muse Spark 1.1—
Not directly comparable
SWE-bench Pro
Ling 3.1 Flash68.7%
Source
Muse Spark 1.161.5%
Source
Ling 3.1 Flash leads this result
DeepSWE
Ling 3.1 Flash59.7%
Source
Muse Spark 1.1—
Not directly comparable
Terminal-Bench 2.1
Ling 3.1 Flash—
Muse Spark 1.180.0%
Source
Not directly comparable
LiveCodeBench (Vals)
Ling 3.1 Flash—
Muse Spark 1.185.9%
Source
Not directly comparable
SWE-bench (Vals)
Ling 3.1 Flash—
Muse Spark 1.182.0%
Source
Not directly comparable

Reasoning

MRCR 1M
Ling 3.1 Flash—
Muse Spark 1.154.1%
Source
Not directly comparable

Multimodal

CharXiv
Ling 3.1 Flash—
Muse Spark 1.188.4%
Source
Not directly comparable
BabyVision
Ling 3.1 Flash—
Muse Spark 1.176.3%
Source
Not directly comparable

Knowledge

HealthBench Professional
Ling 3.1 Flash65.3%
Source
Muse Spark 1.159.3%
Source
Ling 3.1 Flash leads this result
HLE
Ling 3.1 Flash37.4%
Source
Muse Spark 1.162.1%
Source
Muse Spark 1.1 leads this result
HLE w/o tools
Ling 3.1 Flash—
Muse Spark 1.152.2%
Source
Not directly comparable
GPQA Diamond (Vals)
Ling 3.1 Flash—
Muse Spark 1.191.2%
Source
Not directly comparable
MMLU-Pro (Vals)
Ling 3.1 Flash—
Muse Spark 1.188.7%
Source
Not directly comparable

36 public results · 7 shared

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Last updated November 13, 2026

Ling 3.1 Flash vs Muse Spark 1.1

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

Or describe your own job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, Ling 3.1 Flash or Muse Spark 1.1?

Which is better for coding, Ling 3.1 Flash or Muse Spark 1.1?

Which is better for agentic tasks, Ling 3.1 Flash or Muse Spark 1.1?

Which costs less, Ling 3.1 Flash or Muse Spark 1.1?

Which has the larger context window, Ling 3.1 Flash or Muse Spark 1.1?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Related comparisons

Watch Ling 3.1 Flash vs Muse Spark 1.1