Chat turn cost
1K fresh input + 500 output tokens
Hy4
Hy4 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Hy4 vs Muse Wonder
Updated November 4, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Share or export
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. 5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 5
- Hy4 only
- 10
- Muse Wonder only
- 6
- Like-for-like categories
- 0 / 8
Estimated: Muse Wonder. Conditional ranges do not establish rank confidence.How 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.
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Hy4
Hy4 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Hy4
Hy4 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding 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 and Muse Wonder are not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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.
—Hy465.7Muse Wonder
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.
10 Hy4 only
5 Shared
6 Muse Wonder 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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 Wonder
- Not ranked
- Basis
- BenchAlign v5.8 lane · 7 vs 4 public rows
- Reading
- Not comparable
Coding
Not comparable- Hy4
- Not ranked
- Muse Wonder
- 65.7
- Supported · #12/156
- Basis
- BenchAlign v5.8 lane · 5 vs 5 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Hy4
- Not ranked
- Muse Wonder
- 80.4
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Hy4
- Not ranked
- Muse Wonder
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Hy4
- Not ranked
- Muse Wonder
- 68.7
- Supported · #20/179
- Basis
- BenchAlign v5.8 lane · 1 vs 1 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Hy4
- Not ranked
- Muse Wonder
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Hy4
- Not ranked
- Muse Wonder
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Hy4
- Not ranked
- Muse Wonder
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Hy4 | Muse Wonder | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 7 vs 4 public rows | Not comparable |
| Coding | Not ranked | 65.7Supported · #12/156 | Not comparableBenchAlign v5.8 lane · 5 vs 5 public rows | Not comparable |
| Reasoning | Not ranked | 80.4Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | 68.7Supported · #20/179 | Not comparableBenchAlign v5.8 lane · 1 vs 1 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | 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 Wonder
- $0.014
- Fits in one request
Hy4 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Hy4
- $0.0492
- Fits in one request
- Muse Wonder
- $0.26
- Fits in one request
Hy4 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Hy4
- $0.05009
- Fits in one request
- Muse Wonder
- $0.32
- Fits in one request
Hy4 has the lower modeled cost
Costs use the listed standard API rates.
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 Wonder
Context window
Maximum documented context; output-token limits may be lower.
Muse Wonder
API model ID
Hy4
tencent/hy4
Tencent Cloud TokenHub model pricingMuse Wonder
muse-wonder-1.0
Meta: Muse Wonder models and pricingCached-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 Wonder
$0.2 per 1M cached input tokens
Meta: Muse Wonder models and pricingDocumented inputs
Hy4
Not sourced
Muse Wonder
Not sourced
Documented outputs
Hy4
Not sourced
Muse Wonder
Not sourced
Provider availability
Hy4
Not sourced
Muse Wonder
Not sourced
Reasoning profile
Hy4
Reasoning
Muse Wonder
Reasoning
Weight access
Hy4
Pending
Muse Wonder
Proprietary
License
Hy4
Pending
Muse Wonder
Proprietary
Release date
Hy4
2026-11-04
Muse Wonder
2026-10-12
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
- Repository review: $0.0492 vs $0.26. Cache-heavy agent loop: $0.05009 vs $0.32.
- Context tradeoff
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Hy4 or Muse Wonder?
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 Wonder?
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 Wonder?
Hy4 and Muse Wonder are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Hy4 or Muse Wonder?
For the stated presets, chat costs $0.00209 on Hy4 and $0.014 on Muse Wonder; repository review costs $0.0492 and $0.26; the cache-heavy agent loop costs $0.05009 and $0.32. Costs use the listed standard API rates.
Which has the larger context window, Hy4 or Muse Wonder?
Both models list the same context window, 1M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence21 rows
Agentic
Terminal-Bench 4.0
Hy444.40%SourceMuse Wonder—Not directly comparable
Terminal-Bench-Science 0.1
Hy419.8%SourceMuse Wonder—Not directly comparable
HLE w/ tools
Hy459.1%SourceMuse Wonder—Not directly comparable
Agents' Last Exam
Hy441.0%SourceMuse Wonder—Not directly comparable
APEX-Agents
Hy475.2%SourceMuse Wonder—Not directly comparable
AutomationBench
Hy456.2%SourceMuse Wonder—Not directly comparable
OSWorld 2.1
Hy478.9%SourceMuse Wonder72.6%SourceHy4 leads this result
JobBench
Hy4—Muse Wonder70.5%SourceNot directly comparable
Finance Agent v2
Hy4—Muse Wonder56.6%SourceNot directly comparable
MazeBench
Hy4—Muse Wonder0%SourceNot directly comparable
Coding
DeepSWE
Hy474.9%SourceMuse Wonder78.8%SourceMuse Wonder leads this result
sweMarathon
Hy443.8%SourceMuse Wonder53.1%SourceMuse Wonder leads this result
ProgramBench
Hy430.0%SourceMuse Wonder50.0%SourceMuse Wonder leads this result
FrontierCode 2.0
Hy431.0%SourceMuse Wonder24.3%SourceHy4 leads this result
PostTrainBench v1.2
Hy435.2%SourceMuse Wonder—Not directly comparable
Bug Hunt Bench
Hy4—Muse Wonder31.7 fixesSourceNot directly comparable
Reasoning
STEMBench
Hy4—Muse Wonder2.68SourceNot directly comparable
Multimodal
Knowledge
21 public results · 5 shared
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Last updated November 4, 2026