Agentic
Not comparable- Ling 3.0 Flash FP8
- Not measured
- Muse Spark
- 59.0
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Model comparison
Ling 3.0 Flash FP8 vs Muse Spark
Updated August 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
1 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
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.
3 Ling 3.0 Flash FP8 only
1 Shared
23 Muse Spark only
- Shared results
- 1
- Ling 3.0 Flash FP8 only
- 3
- Muse Spark only
- 23
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Not comparable- Ling 3.0 Flash FP8
- 40.4
- Muse Spark
- 67.8
- Weighted basis
- 1 vs 2 rows
- Reading
- Not comparable
Reasoning
Not comparable- Ling 3.0 Flash FP8
- Not measured
- Muse Spark
- 42.5
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Knowledge
Not comparable- Ling 3.0 Flash FP8
- 84.0
- Muse Spark
- 50.4
- Weighted basis
- 1 vs 1 rows
- Reading
- Not comparable
Math
Not comparable- Ling 3.0 Flash FP8
- Not measured
- Muse Spark
- 32.9
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Multilingual
Not comparable- Ling 3.0 Flash FP8
- Not measured
- Muse Spark
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Ling 3.0 Flash FP8
- Not measured
- Muse Spark
- 82.5
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Instruction following
Not comparable- Ling 3.0 Flash FP8
- 73.4
- Muse Spark
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
| Category | Ling 3.0 Flash FP8 | Muse Spark | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | 59.0 | Not comparable0 vs 1 rows | Not comparable |
| Coding | 40.4 | 67.8 | Not comparable1 vs 2 rows | Not comparable |
| Reasoning | Not measured | 42.5 | Not comparable0 vs 1 rows | Not comparable |
| Knowledge | 84.0 | 50.4 | Not comparable1 vs 1 rows | Not comparable |
| Math | Not measured | 32.9 | Not comparable0 vs 2 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 82.5 | Not comparable0 vs 2 rows | Not comparable |
| Instruction following | 73.4 | Not measured | Not comparable1 vs 0 rows | Not comparable |
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 FP8
- API rate not published
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
Ling 3.0 Flash FP8 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 FP8
- API rate not published
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
Ling 3.0 Flash FP8 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 FP8
- 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 FP8 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 Flash FP8Muse Spark
Context window
Maximum documented context; output-token limits may be lower.
Ling 3.0 Flash FP8
Muse Spark
262K
API model ID
Ling 3.0 Flash FP8
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 FP8
No comparable hosted API rate
InclusionAI Ling 3.0 Flash FP8 model cardMuse Spark
No comparable hosted API rate
Documented inputs
Ling 3.0 Flash FP8
Not sourced
Muse Spark
Not sourced
Documented outputs
Ling 3.0 Flash FP8
Not sourced
Muse Spark
Not sourced
Provider availability
Ling 3.0 Flash FP8
Not sourced
Muse Spark
Not sourced
Reasoning profile
Ling 3.0 Flash FP8
Reasoning
Muse Spark
Reasoning
Weight access
Ling 3.0 Flash FP8
Open Weight
Muse Spark
Proprietary
License
Ling 3.0 Flash FP8
Open Weight
Muse Spark
Proprietary
Release date
Ling 3.0 Flash FP8
2026-08-04
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- 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 evidence27 rows
Agentic
Terminal-Bench 2.0
Ling 3.0 Flash FP8—Muse Spark59%SourceNot directly comparable
τ²-bench results
Ling 3.0 Flash FP8—Muse Spark91.5%SourceNot directly comparable
DeepSearchQA
Ling 3.0 Flash FP8—Muse Spark74.8%SourceNot directly comparable
CyberGym
Ling 3.0 Flash FP8—Muse Spark43.5%SourceNot directly comparable
Claw-Eval
Ling 3.0 Flash FP8—Muse Spark63.8%SourceNot directly comparable
Coding
SciCode
Ling 3.0 Flash FP840.4%SourceMuse Spark—Not directly comparable
SWE-bench Verified
Ling 3.0 Flash FP8—Muse Spark77.4%SourceNot directly comparable
SWE-bench Pro
Ling 3.0 Flash FP8—Muse Spark52.4%SourceNot directly comparable
LiveCodeBench Pro
Ling 3.0 Flash FP8—Muse Spark80.0%SourceNot directly comparable
Vibe Code Bench
Ling 3.0 Flash FP8—Muse Spark19.67%SourceNot directly comparable
Reasoning
ARC-AGI-2
Ling 3.0 Flash FP8—Muse Spark42.5%SourceNot directly comparable
Knowledge
GPQA
Ling 3.0 Flash FP884%SourceMuse Spark—Not directly comparable
GPQA-D
Ling 3.0 Flash FP884.0%SourceMuse Spark89.5%SourceMuse Spark leads this result
HLE
Ling 3.0 Flash FP8—Muse Spark50.4%SourceNot directly comparable
HLE w/o tools
Ling 3.0 Flash FP8—Muse Spark42.8%SourceNot directly comparable
HealthBench Hard
Ling 3.0 Flash FP8—Muse Spark42.8%SourceNot directly comparable
MedXpertQA (Text)
Ling 3.0 Flash FP8—Muse Spark52.6%SourceNot directly comparable
Math
Multimodal
CharXiv
Ling 3.0 Flash FP8—Muse Spark86.4%SourceNot directly comparable
MMMU-Pro
Ling 3.0 Flash FP8—Muse Spark80.4%SourceNot directly comparable
ERQA
Ling 3.0 Flash FP8—Muse Spark64.7%SourceNot directly comparable
SimpleVQA
Ling 3.0 Flash FP8—Muse Spark71.3%SourceNot directly comparable
ScreenSpot Pro
Ling 3.0 Flash FP8—Muse Spark84.1%SourceNot directly comparable
ZeroBench
Ling 3.0 Flash FP8—Muse Spark33.0%SourceNot directly comparable
MedXpertQA (MM)
Ling 3.0 Flash FP8—Muse Spark78.4%SourceNot directly comparable
Instruction following
IFBench
Ling 3.0 Flash FP873.4%SourceMuse Spark—Not directly comparable
Frequently asked questions
Which is better, Ling 3.0 Flash FP8 or Muse Spark?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Ling 3.0 Flash FP8 or Muse Spark?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, Ling 3.0 Flash FP8 or Muse Spark?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, Ling 3.0 Flash FP8 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 FP8 or Muse Spark?
Both models list the same context window, 262K.
Related comparisons
Last updated August 4, 2026
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