Agentic
Like-for-like- Muse Spark
- 59.0
- Ornith-1.0-9B
- 43.1
- Weighted basis
- 1 vs 1 rows
- Reading
- Muse Spark leads
Muse Spark vs Ornith-1.0-9B
Updated August 13, 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.
4 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
Muse Spark
Muse Spark leads on the same 2 weighted benchmark rows.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Muse Spark
Muse Spark leads on the same 1 weighted benchmark row.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Muse Spark
Muse Spark has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
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.
20 Muse Spark only
4 Shared
3 Ornith-1.0-9B only
- Shared results
- 4
- Muse Spark only
- 20
- Ornith-1.0-9B only
- 3
- Like-for-like categories
- 2 / 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
Like-for-like- Muse Spark
- 67.8
- Ornith-1.0-9B
- 59.2
- Weighted basis
- 2 vs 2 rows
- Reading
- Muse Spark leads
Reasoning
Not comparable- Muse Spark
- 42.5
- Ornith-1.0-9B
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Muse Spark
- 50.4
- Ornith-1.0-9B
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- Muse Spark
- 32.9
- Ornith-1.0-9B
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Muse Spark
- Not measured
- Ornith-1.0-9B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Muse Spark
- 82.5
- Ornith-1.0-9B
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Muse Spark
- Not measured
- Ornith-1.0-9B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Muse Spark | Ornith-1.0-9B | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 59.0 | 43.1 | Like-for-like1 vs 1 rows | Muse Spark leads |
| Coding | 67.8 | 59.2 | Like-for-like2 vs 2 rows | Muse Spark leads |
| Reasoning | 42.5 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Knowledge | 50.4 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Math | 32.9 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 82.5 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 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.
Terminal-Bench 2.0
Agentic
SWE-bench Pro
Coding
SWE-bench Verified
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
- Muse Spark
- API rate not published
- Fits in one request
- Ornith-1.0-9B
- Self-hosted; infrastructure cost varies
- Fits in one request
Muse Spark has no comparable published API token rate. Ornith-1.0-9B 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
- Ornith-1.0-9B
- Self-hosted; infrastructure cost varies
- Fits in one request
Muse Spark has no comparable published API token rate. Ornith-1.0-9B 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
- Ornith-1.0-9B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
Muse Spark has no comparable published API token rate. Ornith-1.0-9B 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 SparkOrnith-1.0-9B
Context window
Maximum documented context; output-token limits may be lower.
Muse Spark
262K
Ornith-1.0-9B
256K
API model ID
Muse Spark
Not sourced
Ornith-1.0-9B
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
Ornith-1.0-9B
No comparable hosted API rate
Documented inputs
Muse Spark
Not sourced
Ornith-1.0-9B
Not sourced
Documented outputs
Muse Spark
Not sourced
Ornith-1.0-9B
Not sourced
Provider availability
Muse Spark
Not sourced
Ornith-1.0-9B
Not sourced
Reasoning profile
Muse Spark
Reasoning
Ornith-1.0-9B
Reasoning
Weight access
Muse Spark
Proprietary
Ornith-1.0-9B
Open Weight
License
Muse Spark
Proprietary
Ornith-1.0-9B
Open Weight
Release date
Muse Spark
2026-04-08
Ornith-1.0-9B
2026-06-01
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
- Muse Spark has the larger documented window (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
Muse Spark59%SourceOrnith-1.0-9B43.1%SourceMuse Spark leads this result
τ²-bench results
Muse Spark91.5%SourceOrnith-1.0-9B—Not directly comparable
DeepSearchQA
Muse Spark74.8%SourceOrnith-1.0-9B—Not directly comparable
CyberGym
Muse Spark43.5%SourceOrnith-1.0-9B—Not directly comparable
Claw-Eval
Muse Spark63.8%SourceOrnith-1.0-9B63.1%SourceMuse Spark leads this result
Coding
SWE-bench Verified
Muse Spark77.4%SourceOrnith-1.0-9B69.4%SourceMuse Spark leads this result
SWE-bench Pro
Muse Spark52.4%SourceOrnith-1.0-9B42.9%SourceMuse Spark leads this result
LiveCodeBench Pro
Muse Spark80.0%SourceOrnith-1.0-9B—Not directly comparable
Vibe Code Bench
Muse Spark19.67%SourceOrnith-1.0-9B—Not directly comparable
SWE Multilingual
Muse Spark—Ornith-1.0-9B52%SourceNot directly comparable
NL2Repo
Muse Spark—Ornith-1.0-9B27.2%SourceNot directly comparable
Terminal-Bench 2.0
Muse Spark—Ornith-1.0-9B43.1%SourceNot directly comparable
Reasoning
ARC-AGI-2
Muse Spark42.5%SourceOrnith-1.0-9B—Not directly comparable
Knowledge
GPQA-D
Muse Spark89.5%SourceOrnith-1.0-9B—Not directly comparable
HLE
Muse Spark50.4%SourceOrnith-1.0-9B—Not directly comparable
HLE w/o tools
Muse Spark42.8%SourceOrnith-1.0-9B—Not directly comparable
HealthBench Hard
Muse Spark42.8%SourceOrnith-1.0-9B—Not directly comparable
MedXpertQA (Text)
Muse Spark52.6%SourceOrnith-1.0-9B—Not directly comparable
Math
Multimodal
CharXiv
Muse Spark86.4%SourceOrnith-1.0-9B—Not directly comparable
MMMU-Pro
Muse Spark80.4%SourceOrnith-1.0-9B—Not directly comparable
ERQA
Muse Spark64.7%SourceOrnith-1.0-9B—Not directly comparable
SimpleVQA
Muse Spark71.3%SourceOrnith-1.0-9B—Not directly comparable
ScreenSpot Pro
Muse Spark84.1%SourceOrnith-1.0-9B—Not directly comparable
ZeroBench
Muse Spark33.0%SourceOrnith-1.0-9B—Not directly comparable
MedXpertQA (MM)
Muse Spark78.4%SourceOrnith-1.0-9B—Not directly comparable
Frequently asked questions
Which is better, Muse Spark or Ornith-1.0-9B?
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, Muse Spark or Ornith-1.0-9B?
Muse Spark leads the like-for-like coding comparison across 2 shared weighted benchmark rows.
Which is better for agentic tasks, Muse Spark or Ornith-1.0-9B?
Muse Spark leads the like-for-like agentic tasks comparison across 1 shared weighted benchmark row.
Which costs less, Muse Spark or Ornith-1.0-9B?
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 Ornith-1.0-9B?
Muse Spark has the larger documented context window: 262K, compared with 256K.
Related comparisons
Last updated August 13, 2026
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