Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Ling 3.0 Flash Fin is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Ling 3.0 Flash Fin vs Muse Spark
Updated October 13, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
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.
- Shared results
- 0
- Ling 3.0 Flash Fin only
- 3
- Muse Spark only
- 25
- Like-for-like categories
- 0 / 8
Supported: Muse SparkHow the comparison works
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
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Ling 3.0 Flash Fin is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documentedChat 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-ratesCache-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-ratesRepository 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.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
—Ling 3.0 Flash Fin50.2Muse Spark
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 Ling 3.0 Flash Fin only
25 Muse Spark only
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.
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, 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 Fin
- Not ranked
- Muse Spark
- 50.4
- Supported · #45/123
- Basis
- BenchAlign v5.8 lane · 3 vs 5 public rows
- Reading
- Not comparable
Coding
Not comparable- Ling 3.0 Flash Fin
- Not ranked
- Muse Spark
- 50.2
- Supported · #42/148
- Basis
- BenchAlign v5.8 lane · 0 vs 5 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Ling 3.0 Flash Fin
- Not ranked
- Muse Spark
- 57.1
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Ling 3.0 Flash Fin
- Not ranked
- Muse Spark
- 84.4
- #12/54
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Ling 3.0 Flash Fin
- Not ranked
- Muse Spark
- 61.8
- Supported · #43/180
- Basis
- BenchAlign v5.8 lane · 0 vs 7 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Ling 3.0 Flash Fin
- Not ranked
- Muse Spark
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Ling 3.0 Flash Fin
- Not ranked
- Muse Spark
- 93.3
- #10/127
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Ling 3.0 Flash Fin
- Not ranked
- Muse Spark
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Ling 3.0 Flash Fin | Muse Spark | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | 50.4Supported · #45/123 | Not comparableBenchAlign v5.8 lane · 3 vs 5 public rows | Not comparable |
| Coding | Not ranked | 50.2Supported · #42/148 | Not comparableBenchAlign v5.8 lane · 0 vs 5 public rows | Not comparable |
| Reasoning | Not ranked | 57.1Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | 84.4#12/54 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Knowledge | Not ranked | 61.8Supported · #43/180 | Not comparableBenchAlign v5.8 lane · 0 vs 7 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 93.3#10/127 | 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, 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 3.0 Flash Fin
- API rate not published
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
Ling 3.0 Flash Fin 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 Fin
- API rate not published
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
Ling 3.0 Flash Fin 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 Fin
- 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 3.0 Flash Fin has no comparable published API token rate. Muse Spark 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 3.0 Flash FinMuse Spark
Context window
Maximum documented context; output-token limits may be lower.
Ling 3.0 Flash Fin
Muse Spark
262K
API model ID
Ling 3.0 Flash Fin
inclusionai/ling-3.0-flash-fin
Vercel AI Gateway availabilityMuse 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 3.0 Flash Fin
No comparable hosted API rate
Vercel AI Gateway availabilityMuse Spark
No comparable hosted API rate
Documented inputs
Ling 3.0 Flash Fin
Not sourced
Muse Spark
Not sourced
Documented outputs
Ling 3.0 Flash Fin
Not sourced
Muse Spark
Not sourced
Provider availability
Ling 3.0 Flash Fin
Not sourced
Muse Spark
Not sourced
Reasoning profile
Ling 3.0 Flash Fin
Reasoning
Muse Spark
Reasoning
Weight access
Ling 3.0 Flash Fin
Proprietary
Muse Spark
Proprietary
License
Ling 3.0 Flash Fin
Proprietary
Muse Spark
Proprietary
Release date
Ling 3.0 Flash Fin
2026-08-28
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.
Questions
Which is better, Ling 3.0 Flash Fin 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 Fin or Muse Spark?
Ling 3.0 Flash Fin 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 Fin or Muse Spark?
Ling 3.0 Flash Fin 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 Fin 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 Fin or Muse Spark?
Both models list the same context window, 262K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence28 rows
Agentic
Finance Agent v2
Ling 3.0 Flash Fin59.8%SourceMuse Spark—Not directly comparable
APEX-Agents
Ling 3.0 Flash Fin29.2%SourceMuse Spark—Not directly comparable
SpreadsheetBench 2
Ling 3.0 Flash Fin21.8%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.0
Ling 3.0 Flash Fin—Muse Spark59%SourceNot directly comparable
τ²-bench results
Ling 3.0 Flash Fin—Muse Spark91.5%SourceNot directly comparable
DeepSearchQA
Ling 3.0 Flash Fin—Muse Spark74.8%SourceNot directly comparable
CyberGym
Ling 3.0 Flash Fin—Muse Spark43.5%SourceNot directly comparable
Claw-Eval
Ling 3.0 Flash Fin—Muse Spark63.8%SourceNot directly comparable
Coding
SWE-bench Verified
Ling 3.0 Flash Fin—Muse Spark77.4%SourceNot directly comparable
SWE-bench Pro
Ling 3.0 Flash Fin—Muse Spark52.4%SourceNot directly comparable
LiveCodeBench Pro
Ling 3.0 Flash Fin—Muse Spark80.0%SourceNot directly comparable
Vibe Code Bench
Ling 3.0 Flash Fin—Muse Spark19.67%SourceNot directly comparable
SWE-bench (Vals)
Ling 3.0 Flash Fin—Muse Spark74.4%SourceNot directly comparable
Reasoning
ARC-AGI-2
Ling 3.0 Flash Fin—Muse Spark42.5%SourceNot directly comparable
Multimodal
CharXiv
Ling 3.0 Flash Fin—Muse Spark86.4%SourceNot directly comparable
MMMU-Pro
Ling 3.0 Flash Fin—Muse Spark80.4%SourceNot directly comparable
ERQA
Ling 3.0 Flash Fin—Muse Spark64.7%SourceNot directly comparable
SimpleVQA
Ling 3.0 Flash Fin—Muse Spark71.3%SourceNot directly comparable
ScreenSpot Pro
Ling 3.0 Flash Fin—Muse Spark84.1%SourceNot directly comparable
ZeroBench
Ling 3.0 Flash Fin—Muse Spark33.0%SourceNot directly comparable
MedXpertQA (MM)
Ling 3.0 Flash Fin—Muse Spark78.4%SourceNot directly comparable
Knowledge
GPQA-D
Ling 3.0 Flash Fin—Muse Spark89.5%SourceNot directly comparable
HLE
Ling 3.0 Flash Fin—Muse Spark50.4%SourceNot directly comparable
HLE w/o tools
Ling 3.0 Flash Fin—Muse Spark42.8%SourceNot directly comparable
HealthBench Hard
Ling 3.0 Flash Fin—Muse Spark42.8%SourceNot directly comparable
MedXpertQA (Text)
Ling 3.0 Flash Fin—Muse Spark52.6%SourceNot directly comparable
GPQA Diamond (Vals)
Ling 3.0 Flash Fin—Muse Spark89.6%SourceNot directly comparable
MMLU-Pro (Vals)
Ling 3.0 Flash Fin—Muse Spark87.3%SourceNot directly comparable
28 public results · 0 shared
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Last updated October 13, 2026