Muse Wonder Full vs Qwen3.8 Max

Updated October 13, 2026. Rank says Muse Wonder Full is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Muse Wonder Full has the higher public point estimate, 74.13 versus 68.99. Their conditional score ranges overlap. These ranges do not establish rank confidence. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Muse Wonder Full

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 Wonder Full
Muse Wonder Full has the higher public coding point estimate, 73.8 to 53.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited

Show secondary and unsupported calls

Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Muse Wonder Full is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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

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.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

73.8Muse Wonder Full53.7Qwen3.8 Max

Like-for-like · BenchAlign v5.8

Muse Wonder Full has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

Coding

Like-for-like

Muse Wonder Full: 73.8; Supported · #5/148
Qwen3.8 Max: 53.7; Supported · #34/148
Basis: BenchAlign v5.8 lane · 3 vs 13 public rows
Reading: Muse Wonder Full leads · intervals overlap

Knowledge

Like-for-like

Muse Wonder Full: 77.7; Supported · #10/180
Qwen3.8 Max: 67.4; Supported · #25/180
Basis: BenchAlign v5.8 lane · 1 vs 6 public rows
Reading: Muse Wonder Full leads · intervals overlap

Agentic

Not comparable

Muse Wonder Full: Not ranked
Qwen3.8 Max: 65.9; Supported · #14/123
Basis: BenchAlign v5.8 lane · 2 vs 15 public rows
Reading: Not comparable

Reasoning

Not comparable

Muse Wonder Full: 83.4; Unranked · 2 rankable rows
Qwen3.8 Max: 87.8; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Muse Wonder Full: Not ranked
Qwen3.8 Max: 91.2; #4/54
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Muse Wonder Full: Not ranked
Qwen3.8 Max: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Muse Wonder Full: Not ranked
Qwen3.8 Max: 94.7; #2/127
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

Muse Wonder Full: Not ranked
Qwen3.8 Max: 78.9; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Muse Wonder Full and Qwen3.8 Max
Category	Muse Wonder Full	Qwen3.8 Max	Basis	Reading
Coding	73.8Supported · #5/148	53.7Supported · #34/148	Like-for-likeBenchAlign v5.8 lane · 3 vs 13 public rows	Muse Wonder Full leads · intervals overlap
Knowledge	77.7Supported · #10/180	67.4Supported · #25/180	Like-for-likeBenchAlign v5.8 lane · 1 vs 6 public rows	Muse Wonder Full leads · intervals overlap
Agentic	Not ranked	65.9Supported · #14/123	Not comparableBenchAlign v5.8 lane · 2 vs 15 public rows	Not comparable
Reasoning	83.4Unranked · 2 rankable rows	87.8Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multimodal	Not ranked	91.2#4/54	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
Instruction following	Not ranked	94.7#2/127	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Math	Not ranked	78.9Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 2 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 Full: API rate not published; Fits in one request
Qwen3.8 Max: API rate not published; Fits in one request

Muse Wonder Full has no comparable published API token rate. Qwen3.8 Max has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Muse Wonder Full: API rate not published; Fits in one request
Qwen3.8 Max: API rate not published; Fits in one request

Muse Wonder Full has no comparable published API token rate. Qwen3.8 Max has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Muse Wonder Full: API rate not published; Fits in one request; Cached-input rate unavailable
Qwen3.8 Max: API rate not published; Fits in one request; Cached-input rate unavailable

Muse Wonder Full has no comparable published API token rate. Qwen3.8 Max 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 Wonder FullQwen3.8 Max

Context window

Maximum documented context; output-token limits may be lower.

Muse Wonder Full

Meta: Muse Wonder models and pricing

Qwen3.8 Max

Alibaba Cloud Model Studio pricing

API model ID

Muse Wonder Full

Not sourced

Qwen3.8 Max

qwen3.8-max

Alibaba Cloud Model Studio pricing

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Muse Wonder Full

No comparable hosted API rate

Meta: Muse Wonder models and pricing

Qwen3.8 Max

No comparable hosted API rate

Alibaba Cloud Model Studio pricing

Documented inputs

Muse Wonder Full

Not sourced

Qwen3.8 Max

Not sourced

Documented outputs

Muse Wonder Full

Not sourced

Qwen3.8 Max

Not sourced

Provider availability

Muse Wonder Full

Not sourced

Qwen3.8 Max

Not sourced

Reasoning profile

Muse Wonder Full

Reasoning

Qwen3.8 Max

Reasoning

Weight access

Muse Wonder Full

Proprietary

Qwen3.8 Max

Open Weight

License

Muse Wonder Full

Proprietary

Qwen3.8 Max

Open Weight

Release date

Muse Wonder Full

2026-10-12

Qwen3.8 Max

2026-08-03

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: Muse Wonder Full has the higher public point estimate, 74.13 versus 68.99. Their conditional score ranges overlap. These ranges do not establish rank confidence.
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 Full or Qwen3.8 Max?

Muse Wonder Full has the higher public point estimate, 74.13 versus 68.99. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Muse Wonder Full or Qwen3.8 Max?

Muse Wonder Full has the higher public coding point estimate, 73.8 to 53.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, Muse Wonder Full or Qwen3.8 Max?

Muse Wonder Full is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Muse Wonder Full or Qwen3.8 Max?

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 Full or Qwen3.8 Max?

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 evidence67 rows

Agentic

JobBench
Muse Wonder Full75.3%
Source
Qwen3.8 Max53.4%
Source
Muse Wonder Full leads this result
Finance Agent v2
Muse Wonder Full59.1%
Source
Qwen3.8 Max—
Not directly comparable
Terminal-Bench 2.1
Muse Wonder Full—
Qwen3.8 Max86.6%
Source
Not directly comparable
CoWorkBench
Muse Wonder Full—
Qwen3.8 Max74.8%
Source
Not directly comparable
skillsBench
Muse Wonder Full—
Qwen3.8 Max70.2%
Source
Not directly comparable
Agents' Last Exam
Muse Wonder Full—
Qwen3.8 Max52.4%
Source
Not directly comparable
AutomationBench
Muse Wonder Full—
Qwen3.8 Max27.3%
Source
Not directly comparable
Toolathlon-Verified
Muse Wonder Full—
Qwen3.8 Max72.5%
Source
Not directly comparable
WideResearch
Muse Wonder Full—
Qwen3.8 Max81.9%
Source
Not directly comparable
HLE w/ tools
Muse Wonder Full—
Qwen3.8 Max56.2%
Source
Not directly comparable
OSWorld-Verified
Muse Wonder Full—
Qwen3.8 Max86.1%
Source
Not directly comparable
OSWorld 2.0
Muse Wonder Full—
Qwen3.8 Max19.4%
Source
Not directly comparable
WebArena-Verified
Muse Wonder Full—
Qwen3.8 Max66.8%
Source
Not directly comparable
AndroidWorld
Muse Wonder Full—
Qwen3.8 Max85.3%
Source
Not directly comparable
MobileWorld
Muse Wonder Full—
Qwen3.8 Max77.8%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Muse Wonder Full—
Qwen3.8 Max67.4%
Source
Not directly comparable

Coding

DeepSWE
Muse Wonder Full79.0%
Source
Qwen3.8 Max56.6%
Source
Muse Wonder Full leads this result
ProgramBench
Muse Wonder Full59.0%
Source
Qwen3.8 Max—
Not directly comparable
sweMarathon
Muse Wonder Full57.5%
Source
Qwen3.8 Max—
Not directly comparable
Terminal-Bench 2.1
Muse Wonder Full—
Qwen3.8 Max86.6%
Source
Not directly comparable
SWE-bench Pro
Muse Wonder Full—
Qwen3.8 Max67.7%
Source
Not directly comparable
NL2Repo
Muse Wonder Full—
Qwen3.8 Max55.9%
Source
Not directly comparable
FrontierSWE
Muse Wonder Full—
Qwen3.8 Max73.5%
Source
Not directly comparable
MLS-Bench Lite
Muse Wonder Full—
Qwen3.8 Max41.0%
Source
Not directly comparable
PaperBench
Muse Wonder Full—
Qwen3.8 Max93.0%
Source
Not directly comparable
VulcanBench v3
Muse Wonder Full—
Qwen3.8 Max81.2%
Source
Not directly comparable
OpenHarmony Bench
Muse Wonder Full—
Qwen3.8 Max60.8%
Source
Not directly comparable
FrontierSWE v2
Muse Wonder Full—
Qwen3.8 Max15.8%
Source
Not directly comparable
LiveCodeBench (Vals)
Muse Wonder Full—
Qwen3.8 Max87.9%
Source
Not directly comparable
SWE-bench (Vals)
Muse Wonder Full—
Qwen3.8 Max85.6%
Source
Not directly comparable
Bug Hunt Bench
Muse Wonder Full—
Qwen3.8 Max25.7 fixes
Source
Not directly comparable

Reasoning

MRCRv2
Muse Wonder Full—
Qwen3.8 Max92.9%
Source
Not directly comparable
LongBench v2
Muse Wonder Full—
Qwen3.8 Max66.3%
Source
Not directly comparable

Multimodal

MMMU-Pro
Muse Wonder Full—
Qwen3.8 Max82.3%
Source
Not directly comparable
MathVision
Muse Wonder Full—
Qwen3.8 Max95.2%
Source
Not directly comparable
MathVision w/ Python
Muse Wonder Full—
Qwen3.8 Max97.7%
Source
Not directly comparable
BabyVision
Muse Wonder Full—
Qwen3.8 Max82.0%
Source
Not directly comparable
BabyVision w/ Python
Muse Wonder Full—
Qwen3.8 Max91.3%
Source
Not directly comparable
ZeroBench
Muse Wonder Full—
Qwen3.8 Max24.0%
Source
Not directly comparable
ZeroBench w/ Python
Muse Wonder Full—
Qwen3.8 Max49.0%
Source
Not directly comparable
MedXpertQA (MM)
Muse Wonder Full—
Qwen3.8 Max80.4%
Source
Not directly comparable
ScreenSpot Pro
Muse Wonder Full—
Qwen3.8 Max84.5%
Source
Not directly comparable
Vision2Web
Muse Wonder Full—
Qwen3.8 Max69.0%
Source
Not directly comparable
CharXiv w/o tools
Muse Wonder Full—
Qwen3.8 Max88.4%
Source
Not directly comparable
CharXiv
Muse Wonder Full—
Qwen3.8 Max93.5%
Source
Not directly comparable
OmniDocBench 1.5
Muse Wonder Full—
Qwen3.8 Max92.1%
Source
Not directly comparable
OCRBench V2
Muse Wonder Full—
Qwen3.8 Max74.2%
Source
Not directly comparable
CC-OCR
Muse Wonder Full—
Qwen3.8 Max79.6%
Source
Not directly comparable
RealWorldQA
Muse Wonder Full—
Qwen3.8 Max88.0%
Source
Not directly comparable
ERQA
Muse Wonder Full—
Qwen3.8 Max77.8%
Source
Not directly comparable
SimpleVQA
Muse Wonder Full—
Qwen3.8 Max75.0%
Source
Not directly comparable
PerceptionBench
Muse Wonder Full—
Qwen3.8 Max63.5%
Source
Not directly comparable
Video-MME (with subtitle)
Muse Wonder Full—
Qwen3.8 Max90.4%
Source
Not directly comparable
VideoMMMU
Muse Wonder Full—
Qwen3.8 Max88.7%
Source
Not directly comparable
MMVU
Muse Wonder Full—
Qwen3.8 Max82.4%
Source
Not directly comparable
MLVU (M-Avg)
Muse Wonder Full—
Qwen3.8 Max90.8%
Source
Not directly comparable
LVBench
Muse Wonder Full—
Qwen3.8 Max81.8%
Source
Not directly comparable

Knowledge

HealthBench Professional
Muse Wonder Full62.3%
Source
Qwen3.8 Max—
Not directly comparable
GPQA
Muse Wonder Full—
Qwen3.8 Max92.6%
Source
Not directly comparable
GPQA-D
Muse Wonder Full—
Qwen3.8 Max92.6%
Source
Not directly comparable
HLE
Muse Wonder Full—
Qwen3.8 Max43.6%
Source
Not directly comparable
HLE w/o tools
Muse Wonder Full—
Qwen3.8 Max43.6%
Source
Not directly comparable
GPQA Diamond (Vals)
Muse Wonder Full—
Qwen3.8 Max93.7%
Source
Not directly comparable
MMLU-Pro (Vals)
Muse Wonder Full—
Qwen3.8 Max88.6%
Source
Not directly comparable

Instruction following

IFBench
Muse Wonder Full—
Qwen3.8 Max82.8%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Muse Wonder Full—
Qwen3.8 Max74.737%
Source
Not directly comparable
FrontierMath v2 (Tier 4)
Muse Wonder Full—
Qwen3.8 Max46.341%
Source
Not directly comparable

67 public results · 2 shared

Related comparisons

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Last updated October 13, 2026

Muse Wonder Full vs Qwen3.8 Max

Decision reading

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Coding

Knowledge

Agentic

Reasoning

Multimodal

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, Muse Wonder Full or Qwen3.8 Max?

Which is better for coding, Muse Wonder Full or Qwen3.8 Max?

Which is better for agentic tasks, Muse Wonder Full or Qwen3.8 Max?

Which costs less, Muse Wonder Full or Qwen3.8 Max?

Which has the larger context window, Muse Wonder Full or Qwen3.8 Max?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Math

Related comparisons

Watch Muse Wonder Full vs Qwen3.8 Max