Long documents
Prompts that approach the documented context limit
Hy4
Hy4 has the larger documented context window.
Confidence: documented
Hy4 vs Muse Spark
Updated November 4, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
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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. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 2
- Hy4 only
- 13
- Muse Spark only
- 23
- Like-for-like categories
- 0 / 8
Supported: Muse SparkHow the comparison works
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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Hy4 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Hy4 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedChat 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-ratesCache-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-ratesRepository 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
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
—Hy449.8Muse Spark
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
13 Hy4 only
2 Shared
23 Muse Spark only
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.
HLEKnowledge
Normalized gap 8.7Hy4:59.1%Muse Spark:50.4%MMMU-ProMultimodal
Normalized gap 1.8Hy4:78.6%Muse Spark:80.4%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.
Agentic
Not comparable- Hy4
- Not ranked
- Muse Spark
- 53.4
- Supported · #47/135
- Basis
- BenchAlign v5.8 lane · 7 vs 5 public rows
- Reading
- Not comparable
Coding
Not comparable- Hy4
- Not ranked
- Muse Spark
- 49.8
- Supported · #46/156
- Basis
- BenchAlign v5.8 lane · 5 vs 5 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Hy4
- Not ranked
- Muse Spark
- 57.1
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Hy4
- Not ranked
- Muse Spark
- 83.6
- #15/58
- Basis
- Provisional lane · 1 vs 2 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Hy4
- Not ranked
- Muse Spark
- 62.1
- Supported · #43/179
- Basis
- BenchAlign v5.8 lane · 1 vs 7 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Hy4
- Not ranked
- Muse Spark
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Hy4
- Not ranked
- Muse Spark
- 93.3
- #11/157
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Hy4
- Not ranked
- Muse Spark
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Hy4 | Muse Spark | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | 53.4Supported · #47/135 | Not comparableBenchAlign v5.8 lane · 7 vs 5 public rows | Not comparable |
| Coding | Not ranked | 49.8Supported · #46/156 | Not comparableBenchAlign v5.8 lane · 5 vs 5 public rows | Not comparable |
| Reasoning | Not ranked | 57.1Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | 83.6#15/58 | Not comparableProvisional lane · 1 vs 2 weighted rows | Not comparable |
| Knowledge | Not ranked | 62.1Supported · #43/179 | Not comparableBenchAlign v5.8 lane · 1 vs 7 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 93.3#11/157 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 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 v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
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
- Hy4
- $0.00208
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
Muse Spark has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Hy4
- $0.0492
- Fits in one request
- Muse Spark
- API rate not published
- 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
- Hy4
- $0.05009
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
- Cached-input rate unavailable
Muse Spark has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationHy4Muse Spark
Context window
Maximum documented context; output-token limits may be lower.
Muse Spark
262K
API model ID
Hy4
tencent/hy4
Tencent Cloud TokenHub model pricingMuse Spark
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Hy4
$0.042 per 1M cached input tokens
Tencent Cloud TokenHub model pricingMuse Spark
No comparable hosted API rate
Documented inputs
Hy4
Not sourced
Muse Spark
Not sourced
Documented outputs
Hy4
Not sourced
Muse Spark
Not sourced
Provider availability
Hy4
Not sourced
Muse Spark
Not sourced
Reasoning profile
Hy4
Reasoning
Muse Spark
Reasoning
Weight access
Hy4
Pending
Muse Spark
Proprietary
License
Hy4
Pending
Muse Spark
Proprietary
Release date
Hy4
2026-11-04
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
- Hy4 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Hy4 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, Hy4 or Muse Spark?
Hy4 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Hy4 or Muse Spark?
Hy4 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Hy4 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, Hy4 or Muse Spark?
Hy4 has the larger documented context window: 1M, compared with 262K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence38 rows
Agentic
Terminal-Bench 4.0
Hy444.40%SourceMuse Spark—Not directly comparable
Terminal-Bench-Science 0.1
Hy419.8%SourceMuse Spark—Not directly comparable
HLE w/ tools
Hy459.1%SourceMuse Spark—Not directly comparable
Agents' Last Exam
Hy441.0%SourceMuse Spark—Not directly comparable
APEX-Agents
Hy475.2%SourceMuse Spark—Not directly comparable
AutomationBench
Hy456.2%SourceMuse Spark—Not directly comparable
OSWorld 2.1
Hy478.9%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.0
Hy4—Muse Spark59%SourceNot directly comparable
τ²-bench results
Hy4—Muse Spark91.5%SourceNot directly comparable
DeepSearchQA
Hy4—Muse Spark74.8%SourceNot directly comparable
CyberGym
Hy4—Muse Spark43.5%SourceNot directly comparable
Claw-Eval
Hy4—Muse Spark63.8%SourceNot directly comparable
Coding
DeepSWE
Hy474.9%SourceMuse Spark—Not directly comparable
sweMarathon
Hy443.8%SourceMuse Spark—Not directly comparable
ProgramBench
Hy430.0%SourceMuse Spark—Not directly comparable
FrontierCode 2.0
Hy431.0%SourceMuse Spark—Not directly comparable
PostTrainBench v1.2
Hy435.2%SourceMuse Spark—Not directly comparable
SWE-bench Verified
Hy4—Muse Spark77.4%SourceNot directly comparable
SWE-bench Pro
Hy4—Muse Spark52.4%SourceNot directly comparable
LiveCodeBench Pro
Hy4—Muse Spark80.0%SourceNot directly comparable
Vibe Code Bench
Hy4—Muse Spark19.67%SourceNot directly comparable
SWE-bench (Vals)
Hy4—Muse Spark74.4%SourceNot directly comparable
Reasoning
ARC-AGI-2
Hy4—Muse Spark42.5%SourceNot directly comparable
Multimodal
MMMU-Pro
Hy478.6%SourceMuse Spark80.4%SourceMuse Spark leads this result
ZeroBench w/ Python
Hy455.4%SourceMuse Spark—Not directly comparable
CharXiv
Hy4—Muse Spark86.4%SourceNot directly comparable
ERQA
Hy4—Muse Spark64.7%SourceNot directly comparable
SimpleVQA
Hy4—Muse Spark71.3%SourceNot directly comparable
ScreenSpot Pro
Hy4—Muse Spark84.1%SourceNot directly comparable
ZeroBench
Hy4—Muse Spark33.0%SourceNot directly comparable
MedXpertQA (MM)
Hy4—Muse Spark78.4%SourceNot directly comparable
Knowledge
HLE
Hy459.1%SourceMuse Spark50.4%SourceHy4 leads this result
GPQA-D
Hy4—Muse Spark89.5%SourceNot directly comparable
HLE w/o tools
Hy4—Muse Spark42.8%SourceNot directly comparable
HealthBench Hard
Hy4—Muse Spark42.8%SourceNot directly comparable
MedXpertQA (Text)
Hy4—Muse Spark52.6%SourceNot directly comparable
GPQA Diamond (Vals)
Hy4—Muse Spark89.6%SourceNot directly comparable
MMLU-Pro (Vals)
Hy4—Muse Spark87.3%SourceNot directly comparable
38 public results · 2 shared
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Last updated November 4, 2026