Muse Spark vs Qwen3.8 Max

Decision reading

Qwen3.8 Max has the higher public score estimate, 71.76 versus 67.65, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

11 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 Spark

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
Qwen3.8 Max
Qwen3.8 Max leads on the public coding lane, 60.8 to 58.9, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Qwen3.8 Max
Qwen3.8 Max leads on the public agentic lane, 67.3 to 56.1, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Qwen3.8 Max
Qwen3.8 Max has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

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

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

13 Muse Spark only

11 Shared

49 Qwen3.8 Max only

Shared results: 11
Muse Spark only: 13
Qwen3.8 Max only: 49
Like-for-like categories: 4 / 8

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Agentic

Like-for-like

Muse Spark: 56.1; Supported · #35/153
Qwen3.8 Max: 67.3; Supported · #9/153
Basis: BenchAlign lane · 5 vs 15 public rows
Reading: Qwen3.8 Max leads · intervals overlap

Coding

Like-for-like

Muse Spark: 58.9; Supported · #25/152
Qwen3.8 Max: 60.8; Supported · #20/152
Basis: BenchAlign lane · 4 vs 12 public rows
Reading: Qwen3.8 Max leads · intervals overlap

Knowledge

Like-for-like

Muse Spark: 65.4; Supported · #24/183
Qwen3.8 Max: 68.8; Supported · #17/183
Basis: BenchAlign lane · 5 vs 6 public rows
Reading: Qwen3.8 Max leads · intervals overlap

Multimodal

Like-for-like

Muse Spark: 77.5; #14/48
Qwen3.8 Max: 87.4; #5/48
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Qwen3.8 Max leads

Instruction following

Directional only

Muse Spark: 93.2; #8/123
Qwen3.8 Max: 90.7; #18/123
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Directional only

Reasoning

Not comparable

Muse Spark: 45.1; Unranked · 3 rankable rows
Qwen3.8 Max: 86.5; Unranked · 2 rankable rows
Basis: Provisional lane · 1 vs 2 weighted rows
Reading: Not comparable

Math

Not comparable

Muse Spark: 55.3; Unranked · 2 rankable rows
Qwen3.8 Max: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

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

Category scores with the server-provided evidence basis for Muse Spark and Qwen3.8 Max
Category	Muse Spark	Qwen3.8 Max	Basis	Reading
Agentic	56.1Supported · #35/153	67.3Supported · #9/153	Like-for-likeBenchAlign lane · 5 vs 15 public rows	Qwen3.8 Max leads · intervals overlap
Coding	58.9Supported · #25/152	60.8Supported · #20/152	Like-for-likeBenchAlign lane · 4 vs 12 public rows	Qwen3.8 Max leads · intervals overlap
Knowledge	65.4Supported · #24/183	68.8Supported · #17/183	Like-for-likeBenchAlign lane · 5 vs 6 public rows	Qwen3.8 Max leads · intervals overlap
Multimodal	77.5#14/48	87.4#5/48	Like-for-likeProvisional lane · 2 vs 2 weighted rows	Qwen3.8 Max leads
Instruction following	93.2#8/123	90.7#18/123	Directional onlyProvisional lane · 0 vs 1 weighted rows	Directional only
Reasoning	45.1Unranked · 3 rankable rows	86.5Unranked · 2 rankable rows	Not comparableProvisional lane · 1 vs 2 weighted rows	Not comparable
Math	55.3Unranked · 2 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Multilingual	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) 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.

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.

SWE-bench Pro
Coding
Muse Spark: 52.4%Qwen3.8 Max: 67.7%Normalized gap 15.3Muse Spark source Qwen3.8 Max source
CharXiv
Multimodal
Muse Spark: 86.4%Qwen3.8 Max: 93.5%Normalized gap 7.1Muse Spark source Qwen3.8 Max source
HLE
Knowledge
Muse Spark: 50.4%Qwen3.8 Max: 43.6%Normalized gap 6.8Muse Spark source Qwen3.8 Max source
MMMU-Pro
Multimodal
Muse Spark: 80.4%Qwen3.8 Max: 82.3%Normalized gap 1.9Muse Spark source Qwen3.8 Max source
HLE w/o tools
Knowledge
Muse Spark: 42.8%Qwen3.8 Max: 43.6%Normalized gap 0.8Muse Spark source Qwen3.8 Max source

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 Spark: API rate not published; Fits in one request
Qwen3.8 Max: API rate not published; Fits in one request

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

Muse Spark 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 Spark: 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 Spark has no comparable published API token rate. Qwen3.8 Max 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.

SpecificationMuse SparkQwen3.8 Max

Context window

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

Muse Spark

262K

Qwen3.8 Max

Alibaba Cloud Model Studio pricing

API model ID

Muse Spark

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 Spark

No comparable hosted API rate

Qwen3.8 Max

No comparable hosted API rate

Alibaba Cloud Model Studio pricing

Documented inputs

Muse Spark

Not sourced

Qwen3.8 Max

Not sourced

Documented outputs

Muse Spark

Not sourced

Qwen3.8 Max

Not sourced

Provider availability

Muse Spark

Not sourced

Qwen3.8 Max

Not sourced

Reasoning profile

Muse Spark

Reasoning

Qwen3.8 Max

Reasoning

Weight access

Muse Spark

Proprietary

Qwen3.8 Max

Open Weight

License

Muse Spark

Proprietary

Qwen3.8 Max

Open Weight

Release date

Muse Spark

2026-04-08

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: Qwen3.8 Max has the higher public score estimate, 71.76 versus 67.65, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Qwen3.8 Max has the larger documented window (1M).

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 evidence73 rows

Agentic

Terminal-Bench 2.0
Muse Spark59%
Source
Qwen3.8 Max—
Not directly comparable
τ²-bench results
Muse Spark91.5%
Source
Qwen3.8 Max—
Not directly comparable
DeepSearchQA
Muse Spark74.8%
Source
Qwen3.8 Max—
Not directly comparable
CyberGym
Muse Spark43.5%
Source
Qwen3.8 Max—
Not directly comparable
Claw-Eval
Muse Spark63.8%
Source
Qwen3.8 Max—
Not directly comparable
Terminal-Bench 2.1
Muse Spark—
Qwen3.8 Max86.6%
Source
Not directly comparable
CoWorkBench
Muse Spark—
Qwen3.8 Max74.8%
Source
Not directly comparable
JobBench
Muse Spark—
Qwen3.8 Max53.4%
Source
Not directly comparable
skillsBench
Muse Spark—
Qwen3.8 Max70.2%
Source
Not directly comparable
Agents' Last Exam
Muse Spark—
Qwen3.8 Max52.4%
Source
Not directly comparable
AutomationBench
Muse Spark—
Qwen3.8 Max27.3%
Source
Not directly comparable
Toolathlon-Verified
Muse Spark—
Qwen3.8 Max72.5%
Source
Not directly comparable
WideResearch
Muse Spark—
Qwen3.8 Max81.9%
Source
Not directly comparable
HLE w/ tools
Muse Spark—
Qwen3.8 Max56.2%
Source
Not directly comparable
OSWorld-Verified
Muse Spark—
Qwen3.8 Max86.1%
Source
Not directly comparable
OSWorld 2.0
Muse Spark—
Qwen3.8 Max19.4%
Source
Not directly comparable
WebArena-Verified
Muse Spark—
Qwen3.8 Max66.8%
Source
Not directly comparable
AndroidWorld
Muse Spark—
Qwen3.8 Max85.3%
Source
Not directly comparable
MobileWorld
Muse Spark—
Qwen3.8 Max77.8%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Muse Spark—
Qwen3.8 Max67.4%
Source
Not directly comparable

Coding

SWE-bench Verified
Muse Spark77.4%
Source
Qwen3.8 Max—
Not directly comparable
SWE-bench Pro
Muse Spark52.4%
Source
Qwen3.8 Max67.7%
Source
Qwen3.8 Max leads this result
LiveCodeBench Pro
Muse Spark80.0%
Source
Qwen3.8 Max—
Not directly comparable
Vibe Code Bench
Muse Spark19.67%
Source
Qwen3.8 Max—
Not directly comparable
Terminal-Bench 2.1
Muse Spark—
Qwen3.8 Max86.6%
Source
Not directly comparable
DeepSWE
Muse Spark—
Qwen3.8 Max56.6%
Source
Not directly comparable
NL2Repo
Muse Spark—
Qwen3.8 Max55.9%
Source
Not directly comparable
FrontierSWE
Muse Spark—
Qwen3.8 Max73.5%
Source
Not directly comparable
MLS-Bench Lite
Muse Spark—
Qwen3.8 Max41.0%
Source
Not directly comparable
PaperBench
Muse Spark—
Qwen3.8 Max93.0%
Source
Not directly comparable
VulcanBench v3
Muse Spark—
Qwen3.8 Max81.2%
Source
Not directly comparable
OpenHarmony Bench
Muse Spark—
Qwen3.8 Max60.8%
Source
Not directly comparable
FrontierSWE v2
Muse Spark—
Qwen3.8 Max15.8%
Source
Not directly comparable
LiveCodeBench (Vals)
Muse Spark—
Qwen3.8 Max87.9%
Source
Not directly comparable
SWE-bench (Vals)
Muse Spark—
Qwen3.8 Max85.6%
Source
Not directly comparable

Reasoning

ARC-AGI-2
Muse Spark42.5%
Source
Qwen3.8 Max—
Not directly comparable
MRCRv2
Muse Spark—
Qwen3.8 Max92.9%
Source
Not directly comparable
LongBench v2
Muse Spark—
Qwen3.8 Max66.3%
Source
Not directly comparable

Knowledge

GPQA-D
Muse Spark89.5%
Source
Qwen3.8 Max92.6%
Source
Qwen3.8 Max leads this result
HLE
Muse Spark50.4%
Source
Qwen3.8 Max43.6%
Source
Muse Spark leads this result
HLE w/o tools
Muse Spark42.8%
Source
Qwen3.8 Max43.6%
Source
Qwen3.8 Max leads this result
HealthBench Hard
Muse Spark42.8%
Source
Qwen3.8 Max—
Not directly comparable
MedXpertQA (Text)
Muse Spark52.6%
Source
Qwen3.8 Max—
Not directly comparable
GPQA
Muse Spark—
Qwen3.8 Max92.6%
Source
Not directly comparable
GPQA Diamond (Vals)
Muse Spark—
Qwen3.8 Max93.7%
Source
Not directly comparable
MMLU-Pro (Vals)
Muse Spark—
Qwen3.8 Max88.6%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Muse Spark39.000%
Source
Qwen3.8 Max—
Not directly comparable
FrontierMath v2 (Tier 4)
Muse Spark14.600%
Source
Qwen3.8 Max—
Not directly comparable

Multimodal

CharXiv
Muse Spark86.4%
Source
Qwen3.8 Max93.5%
Source
Qwen3.8 Max leads this result
MMMU-Pro
Muse Spark80.4%
Source
Qwen3.8 Max82.3%
Source
Qwen3.8 Max leads this result
ERQA
Muse Spark64.7%
Source
Qwen3.8 Max77.8%
Source
Qwen3.8 Max leads this result
SimpleVQA
Muse Spark71.3%
Source
Qwen3.8 Max75.0%
Source
Qwen3.8 Max leads this result
ScreenSpot Pro
Muse Spark84.1%
Source
Qwen3.8 Max84.5%
Source
Qwen3.8 Max leads this result
ZeroBench
Muse Spark33.0%
Source
Qwen3.8 Max24.0%
Source
Muse Spark leads this result
MedXpertQA (MM)
Muse Spark78.4%
Source
Qwen3.8 Max80.4%
Source
Qwen3.8 Max leads this result
MathVision
Muse Spark—
Qwen3.8 Max95.2%
Source
Not directly comparable
MathVision w/ Python
Muse Spark—
Qwen3.8 Max97.7%
Source
Not directly comparable
BabyVision
Muse Spark—
Qwen3.8 Max82.0%
Source
Not directly comparable
BabyVision w/ Python
Muse Spark—
Qwen3.8 Max91.3%
Source
Not directly comparable
ZeroBench w/ Python
Muse Spark—
Qwen3.8 Max49.0%
Source
Not directly comparable
Vision2Web
Muse Spark—
Qwen3.8 Max69.0%
Source
Not directly comparable
CharXiv w/o tools
Muse Spark—
Qwen3.8 Max88.4%
Source
Not directly comparable
OmniDocBench 1.5
Muse Spark—
Qwen3.8 Max92.1%
Source
Not directly comparable
OCRBench V2
Muse Spark—
Qwen3.8 Max74.2%
Source
Not directly comparable
CC-OCR
Muse Spark—
Qwen3.8 Max79.6%
Source
Not directly comparable
RealWorldQA
Muse Spark—
Qwen3.8 Max88.0%
Source
Not directly comparable
PerceptionBench
Muse Spark—
Qwen3.8 Max63.5%
Source
Not directly comparable
Video-MME (with subtitle)
Muse Spark—
Qwen3.8 Max90.4%
Source
Not directly comparable
VideoMMMU
Muse Spark—
Qwen3.8 Max88.7%
Source
Not directly comparable
MMVU
Muse Spark—
Qwen3.8 Max82.4%
Source
Not directly comparable
MLVU (M-Avg)
Muse Spark—
Qwen3.8 Max90.8%
Source
Not directly comparable
LVBench
Muse Spark—
Qwen3.8 Max81.8%
Source
Not directly comparable

Instruction following

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

Questions

Which is better, Muse Spark or Qwen3.8 Max?

Qwen3.8 Max has the higher public score estimate, 71.76 versus 67.65, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

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

Qwen3.8 Max leads the public coding lane, 60.8 to 58.9, with Supported evidence for both models, although the 90% intervals overlap.

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

Qwen3.8 Max leads the public agentic tasks lane, 67.3 to 56.1, with Supported evidence for both models, although the 90% intervals overlap.

Which costs less, Muse Spark 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 Spark or Qwen3.8 Max?

Qwen3.8 Max has the larger documented context window: 1M, compared with 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 15, 2026

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Muse Spark vs Qwen3.8 Max

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Knowledge

Multimodal

Instruction following

Reasoning

Math

Multilingual

Shape of the matched evidence

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

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multimodal

Instruction following

Questions

Which is better, Muse Spark or Qwen3.8 Max?

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

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

Which costs less, Muse Spark or Qwen3.8 Max?

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

Related comparisons

Watch Muse Spark vs Qwen3.8 Max