Agentic
Not comparable- Celeris-1
- Not ranked
- Muse Spark
- 58.8
- Supported · #31/151
- Basis
- BenchAlign lane · 0 vs 5 public rows
- Reading
- Not comparable
Celeris-1 vs Muse Spark
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 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
Muse Spark
Muse Spark 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
Celeris-1 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
Celeris-1 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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Celeris-1 does not fit this workload in one request. Muse Spark has no comparable published API token rate.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Celeris-1 does not fit this workload in one request. Celeris-1 has no published cached-input rate, so cached tokens use its listed input rate. Muse Spark has no comparable published API token rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Celeris-1 does not fit this workload in one request. Muse Spark has no comparable published API token rate.
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.
4 Celeris-1 only
24 Muse Spark only
- Shared results
- 0
- Celeris-1 only
- 4
- Muse Spark only
- 24
- 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- Celeris-1
- Not ranked
- Muse Spark
- 59.2
- Supported · #28/183
- Basis
- BenchAlign lane · 0 vs 4 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Celeris-1
- 47.8
- Unranked · 3 rankable rows
- Muse Spark
- 45.9
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Celeris-1
- Not ranked
- Muse Spark
- 65.7
- Supported · #23/181
- Basis
- BenchAlign lane · 1 vs 5 public rows
- Reading
- Not comparable
Math
Not comparable- Celeris-1
- Not ranked
- Muse Spark
- 55.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Celeris-1
- Not ranked
- Muse Spark
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Celeris-1
- Not ranked
- Muse Spark
- 77.5
- #14/48
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Celeris-1
- Not ranked
- Muse Spark
- 92.9
- #8/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Celeris-1 | Muse Spark | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | 58.8Supported · #31/151 | Not comparableBenchAlign lane · 0 vs 5 public rows | Not comparable |
| Coding | Not ranked | 59.2Supported · #28/183 | Not comparableBenchAlign lane · 0 vs 4 public rows | Not comparable |
| Reasoning | 47.8Unranked · 3 rankable rows | 45.9Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Knowledge | Not ranked | 65.7Supported · #23/181 | Not comparableBenchAlign lane · 1 vs 5 public rows | Not comparable |
| Math | Not ranked | 55.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 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 |
| Instruction following | Not ranked | 92.9#8/120 | 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
- Celeris-1
- $0.00055
- Does not fit in one request
- Muse Spark
- API rate not published
- Fits in one request
Celeris-1 does not fit this workload in one request. Muse Spark has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Celeris-1
- $0.0121
- Does not fit in one request
- Muse Spark
- API rate not published
- Fits in one request
Celeris-1 does not fit this workload in one request. Muse Spark has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Celeris-1
- $0.051
- Does not fit in one request
- Cached input priced at the published list-input rate
- Muse Spark
- API rate not published
- Fits in one request
- Cached-input rate unavailable
Celeris-1 does not fit this workload in one request. Celeris-1 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.
SpecificationCeleris-1Muse Spark
Context window
Maximum documented context; output-token limits may be lower.
Celeris-1
131,072 tokens
Celeris-1 model guideMuse Spark
262K
API model ID
Celeris-1
celeris-1
Celeris-1 model guideMuse Spark
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Celeris-1
Not published
Celeris API pricingMuse Spark
No comparable hosted API rate
Documented inputs
Celeris-1
text, image
Celeris-1 model guideMuse Spark
Not sourced
Documented outputs
Celeris-1
Muse Spark
Not sourced
Provider availability
Celeris-1
Generally Available · Celeris OpenAI-compatible API (United States)
Celeris availabilityMuse Spark
Not sourced
Reasoning profile
Celeris-1
Non-Reasoning
Muse Spark
Reasoning
Weight access
Celeris-1
Proprietary
Muse Spark
Proprietary
License
Celeris-1
Proprietary
Muse Spark
Proprietary
Release date
Celeris-1
2026-07-22
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- 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 evidence28 rows
Agentic
Terminal-Bench 2.0
Celeris-1—Muse Spark59%SourceNot directly comparable
τ²-bench results
Celeris-1—Muse Spark91.5%SourceNot directly comparable
DeepSearchQA
Celeris-1—Muse Spark74.8%SourceNot directly comparable
CyberGym
Celeris-1—Muse Spark43.5%SourceNot directly comparable
Claw-Eval
Celeris-1—Muse Spark63.8%SourceNot directly comparable
Coding
Reasoning
Knowledge
MMLU-Pro
Celeris-175.9%SourceMuse Spark—Not directly comparable
GPQA-D
Celeris-1—Muse Spark89.5%SourceNot directly comparable
HLE
Celeris-1—Muse Spark50.4%SourceNot directly comparable
HLE w/o tools
Celeris-1—Muse Spark42.8%SourceNot directly comparable
HealthBench Hard
Celeris-1—Muse Spark42.8%SourceNot directly comparable
MedXpertQA (Text)
Celeris-1—Muse Spark52.6%SourceNot directly comparable
Math
Multimodal
CharXiv
Celeris-1—Muse Spark86.4%SourceNot directly comparable
MMMU-Pro
Celeris-1—Muse Spark80.4%SourceNot directly comparable
ERQA
Celeris-1—Muse Spark64.7%SourceNot directly comparable
SimpleVQA
Celeris-1—Muse Spark71.3%SourceNot directly comparable
ScreenSpot Pro
Celeris-1—Muse Spark84.1%SourceNot directly comparable
ZeroBench
Celeris-1—Muse Spark33.0%SourceNot directly comparable
MedXpertQA (MM)
Celeris-1—Muse Spark78.4%SourceNot directly comparable
Instruction following
IFEval
Celeris-180.8%SourceMuse Spark—Not directly comparable
Frequently asked questions
Which is better, Celeris-1 or Muse Spark?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Celeris-1 or Muse Spark?
Celeris-1 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Celeris-1 or Muse Spark?
Celeris-1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Celeris-1 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, Celeris-1 or Muse Spark?
Muse Spark has the larger documented context window: 262K, compared with 131,072 tokens.
Related comparisons
Last updated September 4, 2026
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