Agentic
Not comparable- Muse Spark
- 58.8
- Supported · #31/151
- Trinity-Large-Preview
- Not ranked
- Basis
- BenchAlign lane · 5 vs 0 public rows
- Reading
- Not comparable
Muse Spark vs Trinity-Large-Preview
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 55.39, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 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.
Long documents
Prompts that approach the documented context limit
Trinity-Large-Preview
Trinity-Large-Preview 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
Trinity-Large-Preview is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Trinity-Large-Preview is not ranked on the public lane for agentic, so no winner is named for agentic.
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: rate-fallback
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.
23 Muse Spark only
1 Shared
3 Trinity-Large-Preview only
- Shared results
- 1
- Muse Spark only
- 23
- Trinity-Large-Preview only
- 3
- Like-for-like categories
- 0 / 8
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.
Coding
Not comparable- Muse Spark
- 59.2
- Supported · #28/183
- Trinity-Large-Preview
- Not ranked
- Basis
- BenchAlign lane · 4 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Muse Spark
- 45.9
- Unranked · 3 rankable rows
- Trinity-Large-Preview
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Muse Spark
- 65.6
- Supported · #23/181
- Trinity-Large-Preview
- Not ranked
- Basis
- BenchAlign lane · 5 vs 3 public rows
- Reading
- Not comparable
Math
Not comparable- Muse Spark
- 55.3
- Unranked · 2 rankable rows
- Trinity-Large-Preview
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Muse Spark
- Not ranked
- Trinity-Large-Preview
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Muse Spark
- 76.8
- #14/48
- Trinity-Large-Preview
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Muse Spark
- 92.9
- #8/120
- Trinity-Large-Preview
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Muse Spark | Trinity-Large-Preview | Basis | Reading |
|---|---|---|---|---|
| Agentic | 58.8Supported · #31/151 | Not ranked | Not comparableBenchAlign lane · 5 vs 0 public rows | Not comparable |
| Coding | 59.2Supported · #28/183 | Not ranked | Not comparableBenchAlign lane · 4 vs 0 public rows | Not comparable |
| Reasoning | 45.9Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Knowledge | 65.6Supported · #23/181 | Not ranked | Not comparableBenchAlign lane · 5 vs 3 public rows | Not comparable |
| Math | 55.3Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 76.8#14/48 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Instruction following | 92.9#8/120 | 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) 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.
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
- Muse Spark
- API rate not published
- Fits in one request
- Trinity-Large-Preview
- $0.00075
- Fits in one request
Muse Spark has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Muse Spark
- API rate not published
- Fits in one request
- Trinity-Large-Preview
- $0.0155
- Fits in one request
Muse Spark has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Muse Spark
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- Trinity-Large-Preview
- $0.065
- Fits in one request
- Cached input priced at the published list-input rate
Trinity-Large-Preview has no published cached-input rate, so cached tokens use its listed input 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.
SpecificationMuse SparkTrinity-Large-Preview
Context window
Maximum documented context; output-token limits may be lower.
Muse Spark
262K
Trinity-Large-Preview
512K
API model ID
Muse Spark
Not sourced
Trinity-Large-Preview
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Muse Spark
No comparable hosted API rate
Trinity-Large-Preview
Not published
Documented inputs
Muse Spark
Not sourced
Trinity-Large-Preview
Not sourced
Documented outputs
Muse Spark
Not sourced
Trinity-Large-Preview
Not sourced
Provider availability
Muse Spark
Not sourced
Trinity-Large-Preview
Not sourced
Reasoning profile
Muse Spark
Reasoning
Trinity-Large-Preview
Non-Reasoning
Weight access
Muse Spark
Proprietary
Trinity-Large-Preview
Open Weight
License
Muse Spark
Proprietary
Trinity-Large-Preview
Open Weight
Release date
Muse Spark
2026-04-08
Trinity-Large-Preview
2026-01-27
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 55.39, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Trinity-Large-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 evidence27 rows
Agentic
Terminal-Bench 2.0
Muse Spark59%SourceTrinity-Large-Preview—Not directly comparable
τ²-bench results
Muse Spark91.5%SourceTrinity-Large-Preview—Not directly comparable
DeepSearchQA
Muse Spark74.8%SourceTrinity-Large-Preview—Not directly comparable
CyberGym
Muse Spark43.5%SourceTrinity-Large-Preview—Not directly comparable
Claw-Eval
Muse Spark63.8%SourceTrinity-Large-Preview—Not directly comparable
Coding
SWE-bench Verified
Muse Spark77.4%SourceTrinity-Large-Preview—Not directly comparable
SWE-bench Pro
Muse Spark52.4%SourceTrinity-Large-Preview—Not directly comparable
LiveCodeBench Pro
Muse Spark80.0%SourceTrinity-Large-Preview—Not directly comparable
Vibe Code Bench
Muse Spark19.67%SourceTrinity-Large-Preview—Not directly comparable
Reasoning
ARC-AGI-2
Muse Spark42.5%SourceTrinity-Large-Preview—Not directly comparable
Knowledge
GPQA-D
Muse Spark89.5%SourceTrinity-Large-Preview63.3%SourceMuse Spark leads this result
HLE
Muse Spark50.4%SourceTrinity-Large-Preview—Not directly comparable
HLE w/o tools
Muse Spark42.8%SourceTrinity-Large-Preview—Not directly comparable
HealthBench Hard
Muse Spark42.8%SourceTrinity-Large-Preview—Not directly comparable
MedXpertQA (Text)
Muse Spark52.6%SourceTrinity-Large-Preview—Not directly comparable
MMLU
Muse Spark—Trinity-Large-Preview87.2%SourceNot directly comparable
MMLU-Pro (Arcee)
Muse Spark—Trinity-Large-Preview75.2%SourceNot directly comparable
Math
Multimodal
CharXiv
Muse Spark86.4%SourceTrinity-Large-Preview—Not directly comparable
MMMU-Pro
Muse Spark80.4%SourceTrinity-Large-Preview—Not directly comparable
ERQA
Muse Spark64.7%SourceTrinity-Large-Preview—Not directly comparable
SimpleVQA
Muse Spark71.3%SourceTrinity-Large-Preview—Not directly comparable
ScreenSpot Pro
Muse Spark84.1%SourceTrinity-Large-Preview—Not directly comparable
ZeroBench
Muse Spark33.0%SourceTrinity-Large-Preview—Not directly comparable
MedXpertQA (MM)
Muse Spark78.4%SourceTrinity-Large-Preview—Not directly comparable
Frequently asked questions
Which is better, Muse Spark or Trinity-Large-Preview?
Muse Spark has the higher public score estimate, 68.39 versus 55.39, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Muse Spark or Trinity-Large-Preview?
Trinity-Large-Preview is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Muse Spark or Trinity-Large-Preview?
Trinity-Large-Preview is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Muse Spark or Trinity-Large-Preview?
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, Muse Spark or Trinity-Large-Preview?
Trinity-Large-Preview has the larger documented context window: 512K, compared with 262K.
Related comparisons
Last updated September 4, 2026
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