Coding
Like-for-like- dots3-note Preview
- 71.7
- Muse Spark
- 67.8
- Weighted basis
- 2 vs 2 rows
- Reading
- dots3-note Preview leads
dots3-note Preview vs Muse Spark
Updated August 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
9 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.
Coding work
Code generation, repair, and software-engineering tasks
dots3-note Preview
dots3-note Preview leads on the same 2 weighted benchmark rows.
Confidence: limited
Long documents
Prompts that approach the documented context limit
dots3-note Preview
dots3-note Preview has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
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
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.
22 dots3-note Preview only
9 Shared
15 Muse Spark only
- Shared results
- 9
- dots3-note Preview only
- 22
- Muse Spark only
- 15
- Like-for-like categories
- 3 / 8
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Reasoning
Like-for-like- dots3-note Preview
- 81.4
- Muse Spark
- 42.5
- Weighted basis
- 1 vs 1 rows
- Reading
- dots3-note Preview leads
Knowledge
Like-for-like- dots3-note Preview
- 52.6
- Muse Spark
- 50.4
- Weighted basis
- 1 vs 1 rows
- Reading
- dots3-note Preview leads
Multimodal
Directional only- dots3-note Preview
- 79.1
- Muse Spark
- 82.5
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Agentic
Not comparable- dots3-note Preview
- 83.3
- Muse Spark
- 59.0
- Weighted basis
- 1 vs 1 rows
- Reading
- Not comparable
Math
Not comparable- dots3-note Preview
- Not measured
- Muse Spark
- 32.9
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Multilingual
Not comparable- dots3-note Preview
- Not measured
- Muse Spark
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- dots3-note Preview
- 85.1
- Muse Spark
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
| Category | dots3-note Preview | Muse Spark | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 71.7 | 67.8 | Like-for-like2 vs 2 rows | dots3-note Preview leads |
| Reasoning | 81.4 | 42.5 | Like-for-like1 vs 1 rows | dots3-note Preview leads |
| Knowledge | 52.6 | 50.4 | Like-for-like1 vs 1 rows | dots3-note Preview leads |
| Multimodal | 79.1 | 82.5 | Directional only1 vs 2 rows | Directional only |
| Agentic | 83.3 | 59.0 | 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 |
| Instruction following | 85.1 | Not measured | Not comparable2 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.
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.
ARC-AGI-2
Reasoning
dots3-note Preview: 81.4%Muse Spark: 42.5%Normalized gap 38.9dots3-note Preview source Muse Spark sourceSWE-bench Pro
Coding
dots3-note Preview: 61%Muse Spark: 52.4%Normalized gap 8.6dots3-note Preview source Muse Spark sourceHLE
Knowledge
dots3-note Preview: 52.6%Muse Spark: 50.4%Normalized gap 2.2dots3-note Preview source Muse Spark sourceMMMU-Pro
Multimodal
dots3-note Preview: 79.1%Muse Spark: 80.4%Normalized gap 1.3dots3-note Preview source Muse Spark sourceSWE-bench Verified
Coding
dots3-note Preview: 78.4%Muse Spark: 77.4%Normalized gap 1.0dots3-note Preview source Muse Spark source
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
- dots3-note Preview
- Self-hosted; infrastructure cost varies
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
dots3-note Preview has no comparable published API token rate. Muse Spark has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- dots3-note Preview
- Self-hosted; infrastructure cost varies
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
dots3-note Preview 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
- dots3-note Preview
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
- Muse Spark
- API rate not published
- Fits in one request
- Cached-input rate unavailable
dots3-note Preview 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.
Specificationdots3-note PreviewMuse Spark
Context window
Maximum documented context; output-token limits may be lower.
dots3-note Preview
Muse Spark
262K
API model ID
dots3-note Preview
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.
dots3-note Preview
No comparable hosted API rate
dots3-note Preview model cardMuse Spark
No comparable hosted API rate
Documented inputs
dots3-note Preview
Not sourced
Muse Spark
Not sourced
Documented outputs
dots3-note Preview
Not sourced
Muse Spark
Not sourced
Provider availability
dots3-note Preview
Not sourced
Muse Spark
Not sourced
Reasoning profile
dots3-note Preview
Reasoning
Muse Spark
Reasoning
Weight access
dots3-note Preview
Open Weight
Muse Spark
Proprietary
License
dots3-note Preview
Open Weight
Muse Spark
Proprietary
Release date
dots3-note Preview
2026-08-14
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
- dots3-note Preview has the larger documented window (512K).
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 evidence46 rows
Agentic
Claw-Eval
dots3-note Preview73.4%SourceMuse Spark63.8%Sourcedots3-note Preview leads this result
Terminal-Bench 2.1
dots3-note Preview75.1%SourceMuse Spark—Not directly comparable
Toolathlon-Verified
dots3-note Preview55.6%SourceMuse Spark—Not directly comparable
skillsBench
dots3-note Preview52.8%SourceMuse Spark—Not directly comparable
APEX-Agents
dots3-note Preview30.8%SourceMuse Spark—Not directly comparable
BrowseComp
dots3-note Preview83.3%SourceMuse Spark—Not directly comparable
HLE w/ tools
dots3-note Preview52.6%SourceMuse Spark—Not directly comparable
DeepSearchQA
dots3-note Preview92.1%SourceMuse Spark74.8%Sourcedots3-note Preview leads this result
WideResearch
dots3-note Preview78.9%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.0
dots3-note Preview—Muse Spark59%SourceNot directly comparable
τ²-bench results
dots3-note Preview—Muse Spark91.5%SourceNot directly comparable
CyberGym
dots3-note Preview—Muse Spark43.5%SourceNot directly comparable
Coding
Codeforces
dots3-note Preview3056.0SourceMuse Spark—Not directly comparable
LiveCodeBench v6
dots3-note Preview91.5%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.1
dots3-note Preview75.1%SourceMuse Spark—Not directly comparable
SWE-bench Verified
dots3-note Preview78.4%SourceMuse Spark77.4%Sourcedots3-note Preview leads this result
SWE Multilingual
dots3-note Preview75.7%SourceMuse Spark—Not directly comparable
SWE-bench Pro
dots3-note Preview61%SourceMuse Spark52.4%Sourcedots3-note Preview leads this result
NL2Repo
dots3-note Preview49.8%SourceMuse Spark—Not directly comparable
LiveCodeBench Pro
dots3-note Preview—Muse Spark80.0%SourceNot directly comparable
Vibe Code Bench
dots3-note Preview—Muse Spark19.67%SourceNot directly comparable
Reasoning
Knowledge
HLE
dots3-note Preview52.6%SourceMuse Spark50.4%Sourcedots3-note Preview leads this result
GPQA-D
dots3-note Preview—Muse Spark89.5%SourceNot directly comparable
HLE w/o tools
dots3-note Preview—Muse Spark42.8%SourceNot directly comparable
HealthBench Hard
dots3-note Preview—Muse Spark42.8%SourceNot directly comparable
MedXpertQA (Text)
dots3-note Preview—Muse Spark52.6%SourceNot directly comparable
Math
Multimodal
SimpleVQA
dots3-note Preview72.5%SourceMuse Spark71.3%Sourcedots3-note Preview leads this result
MMMU-Pro
dots3-note Preview79.1%SourceMuse Spark80.4%SourceMuse Spark leads this result
MathVision
dots3-note Preview87.7%SourceMuse Spark—Not directly comparable
ZeroBench
dots3-note Preview19.0%SourceMuse Spark33.0%SourceMuse Spark leads this result
CharXiv w/o tools
dots3-note Preview83.1%SourceMuse Spark—Not directly comparable
GDP.pdf (no tools)
dots3-note Preview60.7%SourceMuse Spark—Not directly comparable
PerceptionBench
dots3-note Preview53.4%SourceMuse Spark—Not directly comparable
BabyVision
dots3-note Preview50.0%SourceMuse Spark—Not directly comparable
MMVU
dots3-note Preview79.9%SourceMuse Spark—Not directly comparable
VideoMMMU
dots3-note Preview86.8%SourceMuse Spark—Not directly comparable
CharXiv
dots3-note Preview—Muse Spark86.4%SourceNot directly comparable
ERQA
dots3-note Preview—Muse Spark64.7%SourceNot directly comparable
ScreenSpot Pro
dots3-note Preview—Muse Spark84.1%SourceNot directly comparable
MedXpertQA (MM)
dots3-note Preview—Muse Spark78.4%SourceNot directly comparable
Instruction following
Frequently asked questions
Which is better, dots3-note Preview 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, dots3-note Preview or Muse Spark?
dots3-note Preview leads the like-for-like coding comparison across 2 shared weighted benchmark rows.
Which is better for agentic tasks, dots3-note Preview 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, dots3-note Preview 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, dots3-note Preview or Muse Spark?
dots3-note Preview has the larger documented context window: 512K, compared with 262K.
Related comparisons
Last updated August 14, 2026
Watch dots3-note Preview vs Muse Spark
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.