Agentic
Like-for-like- Ling 3.0 Flash
- 40.0
- Supported · #121/151
- Muse Spark
- 58.8
- Supported · #31/151
- Basis
- BenchAlign lane · 7 vs 5 public rows
- Reading
- Muse Spark leads · intervals overlap
Ling 3.0 Flash vs Muse Spark
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Muse Spark has the higher public score estimate, 68.39 versus 52.2, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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.
Agentic work
Tool use, computer use, and multi-step task completion
Muse Spark
Muse Spark leads on the public agentic lane, 58.8 to 40, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Ling 3.0 Flash is scored on Estimated evidence for coding, 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.
19 Ling 3.0 Flash only
3 Shared
21 Muse Spark only
- Shared results
- 3
- Ling 3.0 Flash only
- 19
- Muse Spark only
- 21
- Like-for-like categories
- 2 / 8
2 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.
Knowledge
Like-for-like- Ling 3.0 Flash
- 45.9
- Supported · #112/181
- Muse Spark
- 65.7
- Supported · #23/181
- Basis
- BenchAlign lane · 5 vs 5 public rows
- Reading
- Muse Spark leads · intervals overlap
Coding
Directional only- Ling 3.0 Flash
- 42.8
- Estimated · #126/183
- Muse Spark
- 59.2
- Supported · #28/183
- Basis
- BenchAlign lane · 6 vs 4 public rows
- Reading
- Directional only
Instruction following
Directional only- Ling 3.0 Flash
- 75.6
- #58/120
- Muse Spark
- 92.9
- #8/120
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Ling 3.0 Flash
- 69.2
- 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 3.0 Flash
- 73.7
- Unranked · 3 rankable rows
- Muse Spark
- 55.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Ling 3.0 Flash
- Not ranked
- Muse Spark
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Ling 3.0 Flash
- Not ranked
- Muse Spark
- 77.5
- #14/48
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | Ling 3.0 Flash | Muse Spark | Basis | Reading |
|---|---|---|---|---|
| Agentic | 40.0Supported · #121/151 | 58.8Supported · #31/151 | Like-for-likeBenchAlign lane · 7 vs 5 public rows | Muse Spark leads · intervals overlap |
| Knowledge | 45.9Supported · #112/181 | 65.7Supported · #23/181 | Like-for-likeBenchAlign lane · 5 vs 5 public rows | Muse Spark leads · intervals overlap |
| Coding | 42.8Estimated · #126/183 | 59.2Supported · #28/183 | Directional onlyBenchAlign lane · 6 vs 4 public rows | Directional only |
| Instruction following | 75.6#58/120 | 92.9#8/120 | Directional onlyProvisional lane · 1 vs 0 weighted rows | Directional only |
| Reasoning | 69.2Unranked · 2 rankable rows | 45.9Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | 73.7Unranked · 3 rankable rows | 55.3Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 77.5#14/48 | Not comparableProvisional lane · 0 vs 2 weighted rows | 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.
HLE
Knowledge
SWE-bench Pro
Coding
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
- API rate not published
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
Ling 3.0 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 3.0 Flash
- API rate not published
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
Ling 3.0 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 3.0 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 3.0 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.
SpecificationLing 3.0 FlashMuse Spark
Context window
Maximum documented context; output-token limits may be lower.
Ling 3.0 Flash
Muse Spark
262K
API model ID
Ling 3.0 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 3.0 Flash
No comparable hosted API rate
InclusionAI Ling 3.0 Flash model cardMuse Spark
No comparable hosted API rate
Documented inputs
Ling 3.0 Flash
Not sourced
Muse Spark
Not sourced
Documented outputs
Ling 3.0 Flash
Not sourced
Muse Spark
Not sourced
Provider availability
Ling 3.0 Flash
Not sourced
Muse Spark
Not sourced
Reasoning profile
Ling 3.0 Flash
Reasoning
Muse Spark
Reasoning
Weight access
Ling 3.0 Flash
Open Weight
Muse Spark
Proprietary
License
Ling 3.0 Flash
Open Weight
Muse Spark
Proprietary
Release date
Ling 3.0 Flash
2026-07-23
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
- Muse Spark has the higher public score estimate, 68.39 versus 52.2, but the 90% score intervals overlap.
- 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 evidence43 rows
Agentic
MCP Atlas
Ling 3.0 Flash65.5%SourceMuse Spark—Not directly comparable
skillsBench
Ling 3.0 Flash44.8%SourceMuse Spark—Not directly comparable
BFCL v4
Ling 3.0 Flash73.0%SourceMuse Spark—Not directly comparable
WideResearch
Ling 3.0 Flash73.6%SourceMuse Spark—Not directly comparable
BrowseComp
Ling 3.0 Flash72.2%SourceMuse Spark—Not directly comparable
DRACO
Ling 3.0 Flash70.4%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.1 (Vals)
Ling 3.0 Flash50.2%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.0
Ling 3.0 Flash—Muse Spark59%SourceNot directly comparable
τ²-bench results
Ling 3.0 Flash—Muse Spark91.5%SourceNot directly comparable
DeepSearchQA
Ling 3.0 Flash—Muse Spark74.8%SourceNot directly comparable
CyberGym
Ling 3.0 Flash—Muse Spark43.5%SourceNot directly comparable
Claw-Eval
Ling 3.0 Flash—Muse Spark63.8%SourceNot directly comparable
Coding
SWE-bench Pro
Ling 3.0 Flash56.6%SourceMuse Spark52.4%SourceLing 3.0 Flash leads this result
SWE Multilingual
Ling 3.0 Flash72.4%SourceMuse Spark—Not directly comparable
LiveCodeBench v5
Ling 3.0 Flash82.8%SourceMuse Spark—Not directly comparable
SciCode
Ling 3.0 Flash41.2%SourceMuse Spark—Not directly comparable
LiveCodeBench (Vals)
Ling 3.0 Flash84.0%SourceMuse Spark—Not directly comparable
SWE-bench (Vals)
Ling 3.0 Flash65.2%SourceMuse Spark—Not directly comparable
SWE-bench Verified
Ling 3.0 Flash—Muse Spark77.4%SourceNot directly comparable
LiveCodeBench Pro
Ling 3.0 Flash—Muse Spark80.0%SourceNot directly comparable
Vibe Code Bench
Ling 3.0 Flash—Muse Spark19.67%SourceNot directly comparable
Reasoning
ARC-AGI-2
Ling 3.0 Flash—Muse Spark42.5%SourceNot directly comparable
Knowledge
GPQA
Ling 3.0 Flash85.0%SourceMuse Spark—Not directly comparable
GPQA-D
Ling 3.0 Flash85.0%SourceMuse Spark89.5%SourceMuse Spark leads this result
HLE
Ling 3.0 Flash22.7%SourceMuse Spark50.4%SourceMuse Spark leads this result
GPQA Diamond (Vals)
Ling 3.0 Flash84.8%SourceMuse Spark—Not directly comparable
MMLU-Pro (Vals)
Ling 3.0 Flash82.0%SourceMuse Spark—Not directly comparable
HLE w/o tools
Ling 3.0 Flash—Muse Spark42.8%SourceNot directly comparable
HealthBench Hard
Ling 3.0 Flash—Muse Spark42.8%SourceNot directly comparable
MedXpertQA (Text)
Ling 3.0 Flash—Muse Spark52.6%SourceNot directly comparable
Math
AIME26
Ling 3.0 Flash93.2%SourceMuse Spark—Not directly comparable
HMMT Feb 2026
Ling 3.0 Flash87.0%SourceMuse Spark—Not directly comparable
IMOAnswerBench
Ling 3.0 Flash83.7%SourceMuse Spark—Not directly comparable
FrontierMath v2 (Tiers 1-3)
Ling 3.0 Flash—Muse Spark39.000%SourceNot directly comparable
FrontierMath v2 (Tier 4)
Ling 3.0 Flash—Muse Spark14.600%SourceNot directly comparable
Multimodal
CharXiv
Ling 3.0 Flash—Muse Spark86.4%SourceNot directly comparable
MMMU-Pro
Ling 3.0 Flash—Muse Spark80.4%SourceNot directly comparable
ERQA
Ling 3.0 Flash—Muse Spark64.7%SourceNot directly comparable
SimpleVQA
Ling 3.0 Flash—Muse Spark71.3%SourceNot directly comparable
ScreenSpot Pro
Ling 3.0 Flash—Muse Spark84.1%SourceNot directly comparable
ZeroBench
Ling 3.0 Flash—Muse Spark33.0%SourceNot directly comparable
MedXpertQA (MM)
Ling 3.0 Flash—Muse Spark78.4%SourceNot directly comparable
Instruction following
IFBench
Ling 3.0 Flash74.5%SourceMuse Spark—Not directly comparable
Frequently asked questions
Which is better, Ling 3.0 Flash or Muse Spark?
Muse Spark has the higher public score estimate, 68.39 versus 52.2, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Ling 3.0 Flash or Muse Spark?
Muse Spark scores higher for coding on the public lane, 59.2 to 42.8. Ling 3.0 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 3.0 Flash or Muse Spark?
Muse Spark leads the public agentic tasks lane, 58.8 to 40, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, Ling 3.0 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 3.0 Flash or Muse Spark?
Both models list the same context window, 262K.
Related comparisons
Last updated September 4, 2026
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