Muse Spark vs Qwen3.8-27B

Updated September 28, 2026. Rank says Muse Spark is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Muse Spark has the higher public score estimate, 60.78 versus 55.26, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 9 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
Muse Spark
Muse Spark leads on the public coding lane, 53.1 to 48.7, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited

Show secondary and unsupported calls

Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Muse Spark is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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.

53.1Muse Spark48.7Qwen3.8-27B

Like-for-like · BenchAlign v5.7

Muse Spark leads the like-for-like coding row, although the 90% intervals overlap.

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.

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 19.6
Muse Spark:50.4%
Qwen3.8-27B:30.8%
Muse Spark source Qwen3.8-27B source
HLE w/o toolsKnowledge
Normalized gap 12.0
Muse Spark:42.8%
Qwen3.8-27B:30.8%
Muse Spark source Qwen3.8-27B source
SWE-bench ProCoding
Normalized gap 9.3
Muse Spark:52.4%
Qwen3.8-27B:61.7%
Muse Spark source Qwen3.8-27B source
CharXivMultimodal
Normalized gap 3.8
Muse Spark:86.4%
Qwen3.8-27B:90.2%
Muse Spark source Qwen3.8-27B source
MMLU-Pro (Vals)Knowledge
Normalized gap 3.0
Muse Spark:87.3%
Qwen3.8-27B:84.3%
Muse Spark source Qwen3.8-27B source

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 v5.7 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 Spark: 53.1; Supported · #32/136
Qwen3.8-27B: 48.7; Supported · #41/136
Basis: BenchAlign v5.7 lane · 5 vs 8 public rows
Reading: Muse Spark leads · intervals overlap

Knowledge

Like-for-like

Muse Spark: 60.8; Supported · #34/160
Qwen3.8-27B: 49.2; Supported · #59/160
Basis: BenchAlign v5.7 lane · 7 vs 6 public rows
Reading: Muse Spark leads · intervals overlap

Agentic

Directional only

Muse Spark: 49.3; Estimated · #36/111
Qwen3.8-27B: 61.2; Supported · #16/111
Basis: BenchAlign v5.7 lane · 5 vs 8 public rows
Reading: Directional only

Multimodal

Directional only

Muse Spark: 78.5; #14/50
Qwen3.8-27B: 80.9; #11/50
Basis: Provisional lane · 2 vs 1 weighted rows
Reading: Directional only

Instruction following

Directional only

Muse Spark: 91.9; #8/124
Qwen3.8-27B: 83.2; #45/124
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Directional only

Reasoning

Not comparable

Muse Spark: 53.4; Unranked · 3 rankable rows
Qwen3.8-27B: 78.7; #8/27
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Muse Spark: Not ranked
Qwen3.8-27B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Muse Spark: 55.1; Unranked · 2 rankable rows
Qwen3.8-27B: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Muse Spark and Qwen3.8-27B
Category	Muse Spark	Qwen3.8-27B	Basis	Reading
Coding	53.1Supported · #32/136	48.7Supported · #41/136	Like-for-likeBenchAlign v5.7 lane · 5 vs 8 public rows	Muse Spark leads · intervals overlap
Knowledge	60.8Supported · #34/160	49.2Supported · #59/160	Like-for-likeBenchAlign v5.7 lane · 7 vs 6 public rows	Muse Spark leads · intervals overlap
Agentic	49.3Estimated · #36/111	61.2Supported · #16/111	Directional onlyBenchAlign v5.7 lane · 5 vs 8 public rows	Directional only
Multimodal	78.5#14/50	80.9#11/50	Directional onlyProvisional lane · 2 vs 1 weighted rows	Directional only
Instruction following	91.9#8/124	83.2#45/124	Directional onlyProvisional lane · 0 vs 1 weighted rows	Directional only
Reasoning	53.4Unranked · 3 rankable rows	78.7#8/27	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	55.1Unranked · 2 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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 Spark: API rate not published; Fits in one request
Qwen3.8-27B: Self-hosted; infrastructure cost varies; Fits in one request

Muse Spark has no comparable published API token rate. Qwen3.8-27B 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
Qwen3.8-27B: Self-hosted; infrastructure cost varies; Fits in one request

Muse Spark has no comparable published API token rate. Qwen3.8-27B 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
Qwen3.8-27B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Muse Spark has no comparable published API token rate. Qwen3.8-27B 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 SparkQwen3.8-27B

Context window

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

Muse Spark

262K

Qwen3.8-27B

262K

Qwen3.8-27B model card

API model ID

Muse Spark

Not sourced

Qwen3.8-27B

Not sourced

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

Qwen3.8-27B

No comparable hosted API rate

Qwen3.8-27B model card

Documented inputs

Muse Spark

Not sourced

Qwen3.8-27B

Not sourced

Documented outputs

Muse Spark

Not sourced

Qwen3.8-27B

Not sourced

Provider availability

Muse Spark

Not sourced

Qwen3.8-27B

Not sourced

Reasoning profile

Muse Spark

Reasoning

Qwen3.8-27B

Reasoning

Weight access

Muse Spark

Proprietary

Qwen3.8-27B

Open Weight

License

Muse Spark

Proprietary

Qwen3.8-27B

Open Weight

Release date

Muse Spark

2026-04-08

Qwen3.8-27B

2026-08-05

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 Spark has the higher public score estimate, 60.78 versus 55.26, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Both models list 262K.

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Muse Spark or Qwen3.8-27B?

Muse Spark has the higher public score estimate, 60.78 versus 55.26, 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 Qwen3.8-27B?

Muse Spark leads the public coding lane, 53.1 to 48.7, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, Muse Spark or Qwen3.8-27B?

Qwen3.8-27B scores higher for agentic tasks on the public lane, 61.2 to 49.3. Muse Spark is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Muse Spark or Qwen3.8-27B?

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 Qwen3.8-27B?

Both models list the same context window, 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 evidence51 rows

Agentic

Terminal-Bench 2.0
Muse Spark59%
Source
Qwen3.8-27B—
Not directly comparable
τ²-bench results
Muse Spark91.5%
Source
Qwen3.8-27B—
Not directly comparable
DeepSearchQA
Muse Spark74.8%
Source
Qwen3.8-27B—
Not directly comparable
CyberGym
Muse Spark43.5%
Source
Qwen3.8-27B—
Not directly comparable
Claw-Eval
Muse Spark63.8%
Source
Qwen3.8-27B—
Not directly comparable
Terminal-Bench 2.1
Muse Spark—
Qwen3.8-27B73.0%
Source
Not directly comparable
CoWorkBench
Muse Spark—
Qwen3.8-27B70.7%
Source
Not directly comparable
JobBench
Muse Spark—
Qwen3.8-27B33.4%
Source
Not directly comparable
Agents' Last Exam
Muse Spark—
Qwen3.8-27B42.9%
Source
Not directly comparable
OSWorld-Verified
Muse Spark—
Qwen3.8-27B84.3%
Source
Not directly comparable
WebArena-Verified
Muse Spark—
Qwen3.8-27B64.8%
Source
Not directly comparable
AndroidWorld
Muse Spark—
Qwen3.8-27B81.9%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Muse Spark—
Qwen3.8-27B58.4%
Source
Not directly comparable

Coding

SWE-bench Verified
Muse Spark77.4%
Source
Qwen3.8-27B—
Not directly comparable
SWE-bench Pro
Muse Spark52.4%
Source
Qwen3.8-27B61.7%
Source
Qwen3.8-27B leads this result
LiveCodeBench Pro
Muse Spark80.0%
Source
Qwen3.8-27B—
Not directly comparable
Vibe Code Bench
Muse Spark19.67%
Source
Qwen3.8-27B—
Not directly comparable
SWE-bench (Vals)
Muse Spark74.4%
Source
Qwen3.8-27B86.0%
Source
Qwen3.8-27B leads this result
Terminal-Bench 2.1
Muse Spark—
Qwen3.8-27B73.0%
Source
Not directly comparable
NL2Repo
Muse Spark—
Qwen3.8-27B42.3%
Source
Not directly comparable
DeepSWE
Muse Spark—
Qwen3.8-27B42.2%
Source
Not directly comparable
LiveCodeBench v6
Muse Spark—
Qwen3.8-27B90.3%
Source
Not directly comparable
VulcanBench v3
Muse Spark—
Qwen3.8-27B82.6%
Source
Not directly comparable
LiveCodeBench (Vals)
Muse Spark—
Qwen3.8-27B84.0%
Source
Not directly comparable

Reasoning

ARC-AGI-2
Muse Spark42.5%
Source
Qwen3.8-27B—
Not directly comparable

Multimodal

CharXiv
Muse Spark86.4%
Source
Qwen3.8-27B90.2%
Source
Qwen3.8-27B leads this result
MMMU-Pro
Muse Spark80.4%
Source
Qwen3.8-27B—
Not directly comparable
ERQA
Muse Spark64.7%
Source
Qwen3.8-27B65.5%
Source
Qwen3.8-27B leads this result
SimpleVQA
Muse Spark71.3%
Source
Qwen3.8-27B—
Not directly comparable
ScreenSpot Pro
Muse Spark84.1%
Source
Qwen3.8-27B—
Not directly comparable
ZeroBench
Muse Spark33.0%
Source
Qwen3.8-27B—
Not directly comparable
MedXpertQA (MM)
Muse Spark78.4%
Source
Qwen3.8-27B—
Not directly comparable
MathVision
Muse Spark—
Qwen3.8-27B90.0%
Source
Not directly comparable
MathVision w/ Python
Muse Spark—
Qwen3.8-27B94.6%
Source
Not directly comparable
BabyVision
Muse Spark—
Qwen3.8-27B65.7%
Source
Not directly comparable
BabyVision w/ Python
Muse Spark—
Qwen3.8-27B85.6%
Source
Not directly comparable
Vision2Web
Muse Spark—
Qwen3.8-27B62.9%
Source
Not directly comparable
CharXiv w/o tools
Muse Spark—
Qwen3.8-27B83.7%
Source
Not directly comparable
OmniDocBench 1.5
Muse Spark—
Qwen3.8-27B91.1%
Source
Not directly comparable
RealWorldQA
Muse Spark—
Qwen3.8-27B85.9%
Source
Not directly comparable

Knowledge

GPQA-D
Muse Spark89.5%
Source
Qwen3.8-27B89.2%
Source
Muse Spark leads this result
HLE
Muse Spark50.4%
Source
Qwen3.8-27B30.8%
Source
Muse Spark leads this result
HLE w/o tools
Muse Spark42.8%
Source
Qwen3.8-27B30.8%
Source
Muse Spark leads this result
HealthBench Hard
Muse Spark42.8%
Source
Qwen3.8-27B—
Not directly comparable
MedXpertQA (Text)
Muse Spark52.6%
Source
Qwen3.8-27B—
Not directly comparable
GPQA Diamond (Vals)
Muse Spark89.6%
Source
Qwen3.8-27B88.9%
Source
Muse Spark leads this result
MMLU-Pro (Vals)
Muse Spark87.3%
Source
Qwen3.8-27B84.3%
Source
Muse Spark leads this result
GPQA
Muse Spark—
Qwen3.8-27B89.2%
Source
Not directly comparable

Instruction following

IFBench
Muse Spark—
Qwen3.8-27B79.5%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Muse Spark39.000%
Source
Qwen3.8-27B—
Not directly comparable
FrontierMath v2 (Tier 4)
Muse Spark14.600%
Source
Qwen3.8-27B—
Not directly comparable

51 public results · 9 shared

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Last updated September 28, 2026

Muse Spark vs Qwen3.8-27B

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

Multimodal

Instruction following

Reasoning

Multilingual

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 Spark or Qwen3.8-27B?

Which is better for coding, Muse Spark or Qwen3.8-27B?

Which is better for agentic tasks, Muse Spark or Qwen3.8-27B?

Which costs less, Muse Spark or Qwen3.8-27B?

Which has the larger context window, Muse Spark or Qwen3.8-27B?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Math

Related comparisons

Watch Muse Spark vs Qwen3.8-27B