Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Qwen3.8-Omni-Flash is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Muse Wonder vs Qwen3.8-Omni-Flash
Updated October 12, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 1
- Muse Wonder only
- 5
- Qwen3.8-Omni-Flash only
- 18
- 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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Show secondary and unsupported calls
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Muse Wonder and Qwen3.8-Omni-Flash 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: documentedChat 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.
69.8Muse Wonder—Qwen3.8-Omni-Flash
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.
5 Muse Wonder only
1 Shared
18 Qwen3.8-Omni-Flash 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- Muse Wonder
- Not ranked
- Qwen3.8-Omni-Flash
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 2 public rows
- Reading
- Not comparable
Coding
Not comparable- Muse Wonder
- 69.8
- Supported · #10/148
- Qwen3.8-Omni-Flash
- Not ranked
- Basis
- BenchAlign v5.8 lane · 3 vs 5 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Muse Wonder
- 81.3
- Unranked · 2 rankable rows
- Qwen3.8-Omni-Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Muse Wonder
- Not ranked
- Qwen3.8-Omni-Flash
- 89.7
- Unranked · 7 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Muse Wonder
- 74.0
- Supported · #12/180
- Qwen3.8-Omni-Flash
- Not ranked
- Basis
- BenchAlign v5.8 lane · 1 vs 3 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Muse Wonder
- Not ranked
- Qwen3.8-Omni-Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Muse Wonder
- Not ranked
- Qwen3.8-Omni-Flash
- 91.6
- #21/127
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- Muse Wonder
- Not ranked
- Qwen3.8-Omni-Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Muse Wonder | Qwen3.8-Omni-Flash | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 2 public rows | Not comparable |
| Coding | 69.8Supported · #10/148 | Not ranked | Not comparableBenchAlign v5.8 lane · 3 vs 5 public rows | Not comparable |
| Reasoning | 81.3Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 89.7Unranked · 7 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Knowledge | 74.0Supported · #12/180 | Not ranked | Not comparableBenchAlign v5.8 lane · 1 vs 3 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 91.6#21/127 | Not comparableProvisional lane · 0 vs 1 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
- Muse Wonder
- $0.014
- Fits in one request
- Qwen3.8-Omni-Flash
- API rate not published
- Fits in one request
Qwen3.8-Omni-Flash has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Muse Wonder
- $0.26
- Fits in one request
- Qwen3.8-Omni-Flash
- API rate not published
- Fits in one request
Qwen3.8-Omni-Flash has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Muse Wonder
- $0.32
- Fits in one request
- Qwen3.8-Omni-Flash
- API rate not published
- Fits in one request
- Cached-input rate unavailable
Qwen3.8-Omni-Flash 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.
SpecificationMuse WonderQwen3.8-Omni-Flash
Context window
Maximum documented context; output-token limits may be lower.
Muse Wonder
Qwen3.8-Omni-Flash
API model ID
Muse Wonder
muse-wonder-1.0
Meta: Muse Wonder models and pricingQwen3.8-Omni-Flash
qwen3.8-omni-flash
Alibaba Cloud Model Studio pricingCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Muse Wonder
$0.2 per 1M cached input tokens
Meta: Muse Wonder models and pricingQwen3.8-Omni-Flash
No comparable hosted API rate
Alibaba Cloud Model Studio pricingDocumented inputs
Muse Wonder
Not sourced
Qwen3.8-Omni-Flash
Not sourced
Documented outputs
Muse Wonder
Not sourced
Qwen3.8-Omni-Flash
Not sourced
Provider availability
Muse Wonder
Not sourced
Qwen3.8-Omni-Flash
Not sourced
Reasoning profile
Muse Wonder
Reasoning
Qwen3.8-Omni-Flash
Reasoning
Weight access
Muse Wonder
Proprietary
Qwen3.8-Omni-Flash
Proprietary
License
Muse Wonder
Proprietary
Qwen3.8-Omni-Flash
Proprietary
Release date
Muse Wonder
2026-10-12
Qwen3.8-Omni-Flash
2026-09-18
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
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Muse Wonder or Qwen3.8-Omni-Flash?
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 Wonder or Qwen3.8-Omni-Flash?
Qwen3.8-Omni-Flash is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Muse Wonder or Qwen3.8-Omni-Flash?
Muse Wonder and Qwen3.8-Omni-Flash are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Muse Wonder or Qwen3.8-Omni-Flash?
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 Wonder or Qwen3.8-Omni-Flash?
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 evidence24 rows
Agentic
JobBench
Muse Wonder70.5%SourceQwen3.8-Omni-Flash—Not directly comparable
Finance Agent v2
Muse Wonder56.6%SourceQwen3.8-Omni-Flash—Not directly comparable
CoWorkBench
Muse Wonder—Qwen3.8-Omni-Flash75.3%SourceNot directly comparable
AndroidWorld
Muse Wonder—Qwen3.8-Omni-Flash87.1%SourceNot directly comparable
Coding
DeepSWE
Muse Wonder78.8%SourceQwen3.8-Omni-Flash57.8%SourceMuse Wonder leads this result
ProgramBench
Muse Wonder50.0%SourceQwen3.8-Omni-Flash—Not directly comparable
sweMarathon
Muse Wonder53.1%SourceQwen3.8-Omni-Flash—Not directly comparable
SWE-bench Pro
Muse Wonder—Qwen3.8-Omni-Flash63.3%SourceNot directly comparable
SWE Multilingual
Muse Wonder—Qwen3.8-Omni-Flash80.5%SourceNot directly comparable
NL2Repo
Muse Wonder—Qwen3.8-Omni-Flash48.9%SourceNot directly comparable
LiveCodeBench v6
Muse Wonder—Qwen3.8-Omni-Flash92.6%SourceNot directly comparable
Multimodal
Vision2Web
Muse Wonder—Qwen3.8-Omni-Flash62.9%SourceNot directly comparable
ERQA
Muse Wonder—Qwen3.8-Omni-Flash71.0%SourceNot directly comparable
LVBench
Muse Wonder—Qwen3.8-Omni-Flash76.9%SourceNot directly comparable
RealWorldQA
Muse Wonder—Qwen3.8-Omni-Flash87.7%SourceNot directly comparable
MathVision
Muse Wonder—Qwen3.8-Omni-Flash91.8%SourceNot directly comparable
MathVision w/ Python
Muse Wonder—Qwen3.8-Omni-Flash96.2%SourceNot directly comparable
CharXiv w/o tools
Muse Wonder—Qwen3.8-Omni-Flash83.5%SourceNot directly comparable
CharXiv
Muse Wonder—Qwen3.8-Omni-Flash91.4%SourceNot directly comparable
Knowledge
Instruction following
IFBench
Muse Wonder—Qwen3.8-Omni-Flash81.5%SourceNot directly comparable
24 public results · 1 shared
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Last updated October 12, 2026