Muse Glimmer 30B vs Qwen3.6-27B

Updated September 24, 2026. Rank says Qwen3.6-27B is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Qwen3.6-27B has the higher public score estimate, 46.49 versus 41.73, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 5 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 Glimmer 30B

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.

Long documents
Prompts that approach the documented context limit
Qwen3.6-27B
Qwen3.6-27B has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Muse Glimmer 30B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Muse Glimmer 30B is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Muse Glimmer 30B does not fit this workload in one request. Muse Glimmer 30B has no comparable published API token rate. Qwen3.6-27B has no comparable published API token rate.
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.

36.3Muse Glimmer 30B37.7Qwen3.6-27B

Directional only · BenchAlign v5.7

Qwen3.6-27B scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

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.

SWE-bench ProCoding
Normalized gap 2.3
Muse Glimmer 30B:51.2%
Qwen3.6-27B:53.5%
Muse Glimmer 30B source Qwen3.6-27B source
MMMU-ProMultimodal
Normalized gap 1.8
Muse Glimmer 30B:74%
Qwen3.6-27B:75.8%
Muse Glimmer 30B source Qwen3.6-27B source
SWE-bench VerifiedCoding
Normalized gap 1.2
Muse Glimmer 30B:76%
Qwen3.6-27B:77.2%
Muse Glimmer 30B source Qwen3.6-27B source
AIME26Math
Normalized gap 0.6
Muse Glimmer 30B:94.7%
Qwen3.6-27B:94.1%
Muse Glimmer 30B source Qwen3.6-27B source
CharXivMultimodal
Normalized gap 0.4
Muse Glimmer 30B:78.8%
Qwen3.6-27B:78.4%
Muse Glimmer 30B source Qwen3.6-27B 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.

Multimodal

Like-for-like

Muse Glimmer 30B: 46.3; #42/50
Qwen3.6-27B: 51.9; #37/50
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Qwen3.6-27B leads

Agentic

Directional only

Muse Glimmer 30B: 27.6; Estimated · #72/105
Qwen3.6-27B: 28.1; Supported · #71/105
Basis: BenchAlign v5.7 lane · 4 vs 6 public rows
Reading: Directional only

Coding

Directional only

Muse Glimmer 30B: 36.3; Estimated · #69/135
Qwen3.6-27B: 37.7; Supported · #63/135
Basis: BenchAlign v5.7 lane · 4 vs 7 public rows
Reading: Directional only

Knowledge

Directional only

Muse Glimmer 30B: 43.9; Estimated · #75/158
Qwen3.6-27B: 45.7; Estimated · #72/158
Basis: BenchAlign v5.7 lane · 0 vs 6 public rows
Reading: Directional only

Instruction following

Directional only

Muse Glimmer 30B: 77.7; #55/124
Qwen3.6-27B: 81.0; #52/124
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

Muse Glimmer 30B: 79.3; Unranked · 2 rankable rows
Qwen3.6-27B: 75.2; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Muse Glimmer 30B: Not ranked
Qwen3.6-27B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Muse Glimmer 30B: 75.4; Unranked · 1 rankable row
Qwen3.6-27B: 72.1; Unranked · 5 rankable rows
Basis: Provisional lane · 1 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Muse Glimmer 30B and Qwen3.6-27B
Category	Muse Glimmer 30B	Qwen3.6-27B	Basis	Reading
Multimodal	46.3#42/50	51.9#37/50	Like-for-likeProvisional lane · 2 vs 2 weighted rows	Qwen3.6-27B leads
Agentic	27.6Estimated · #72/105	28.1Supported · #71/105	Directional onlyBenchAlign v5.7 lane · 4 vs 6 public rows	Directional only
Coding	36.3Estimated · #69/135	37.7Supported · #63/135	Directional onlyBenchAlign v5.7 lane · 4 vs 7 public rows	Directional only
Knowledge	43.9Estimated · #75/158	45.7Estimated · #72/158	Directional onlyBenchAlign v5.7 lane · 0 vs 6 public rows	Directional only
Instruction following	77.7#55/124	81.0#52/124	Directional onlyProvisional lane · 1 vs 0 weighted rows	Directional only
Reasoning	79.3Unranked · 2 rankable rows	75.2Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	75.4Unranked · 1 rankable row	72.1Unranked · 5 rankable rows	Not comparableProvisional lane · 1 vs 2 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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 Glimmer 30B: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request

Muse Glimmer 30B has no comparable published API token rate. Qwen3.6-27B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Muse Glimmer 30B: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request

Muse Glimmer 30B has no comparable published API token rate. Qwen3.6-27B has no comparable published API token rate.

Cache-heavy agent loop

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

Muse Glimmer 30B: Self-hosted; infrastructure cost varies; Does not fit in one request; Cached-input rate unavailable
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Muse Glimmer 30B does not fit this workload in one request. Muse Glimmer 30B has no comparable published API token rate. Qwen3.6-27B 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 Glimmer 30BQwen3.6-27B

Context window

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

Muse Glimmer 30B

131K

Qwen3.6-27B

262K

API model ID

Muse Glimmer 30B

Not sourced

Qwen3.6-27B

Not sourced

Cached-input rate

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

Muse Glimmer 30B

No comparable hosted API rate

Qwen3.6-27B

No comparable hosted API rate

Documented inputs

Muse Glimmer 30B

Not sourced

Qwen3.6-27B

Not sourced

Documented outputs

Muse Glimmer 30B

Not sourced

Qwen3.6-27B

Not sourced

Provider availability

Muse Glimmer 30B

Not sourced

Qwen3.6-27B

Not sourced

Reasoning profile

Muse Glimmer 30B

Reasoning

Qwen3.6-27B

Reasoning

Weight access

Muse Glimmer 30B

Open Weight

Qwen3.6-27B

Open Weight

License

Muse Glimmer 30B

Open Weight

Qwen3.6-27B

Open Weight

Release date

Muse Glimmer 30B

2026-08-10

Qwen3.6-27B

2026-04-21

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: Qwen3.6-27B has the higher public score estimate, 46.49 versus 41.73, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Qwen3.6-27B has the larger documented window (262K).

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

Questions

Which is better, Muse Glimmer 30B or Qwen3.6-27B?

Qwen3.6-27B has the higher public score estimate, 46.49 versus 41.73, 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 Glimmer 30B or Qwen3.6-27B?

Qwen3.6-27B scores higher for coding on the public lane, 37.7 to 36.3. Muse Glimmer 30B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Muse Glimmer 30B or Qwen3.6-27B?

Qwen3.6-27B scores higher for agentic tasks on the public lane, 28.1 to 27.6. Muse Glimmer 30B 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 Glimmer 30B or Qwen3.6-27B?

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 Glimmer 30B or Qwen3.6-27B?

Qwen3.6-27B has the larger documented context window: 262K, compared with 131K.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Muse Glimmer 30B

API / mo$0

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Qwen3.6-27B

API / mo$0

Self-host / mo$429

Break-even—

Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence48 rows

Agentic

MCP Atlas
Muse Glimmer 30B75.5%
Source
Qwen3.6-27B—
Not directly comparable
DeepSearchQA
Muse Glimmer 30B74.6%
Source
Qwen3.6-27B—
Not directly comparable
skillsBench
Muse Glimmer 30B44.3%
Source
Qwen3.6-27B—
Not directly comparable
OSWorld-Verified
Muse Glimmer 30B65.9%
Source
Qwen3.6-27B—
Not directly comparable
Terminal-Bench 2.0
Muse Glimmer 30B—
Qwen3.6-27B59.3%
Source
Not directly comparable
Claw-Eval
Muse Glimmer 30B—
Qwen3.6-27B72.4%
Source
Not directly comparable
QwenClawBench
Muse Glimmer 30B—
Qwen3.6-27B53.4%
Source
Not directly comparable
QwenWebBench
Muse Glimmer 30B—
Qwen3.6-27B1487
Source
Not directly comparable
AndroidWorld
Muse Glimmer 30B—
Qwen3.6-27B70.3%
Source
Not directly comparable
Gert Labs
Muse Glimmer 30B—
Qwen3.6-27B54.84%
Source
Not directly comparable

Coding

SWE-bench Pro
Muse Glimmer 30B51.2%
Source
Qwen3.6-27B53.5%
Source
Qwen3.6-27B leads this result
SWE-bench Verified
Muse Glimmer 30B76%
Source
Qwen3.6-27B77.2%
Source
Qwen3.6-27B leads this result
Terminal-Bench 2.1
Muse Glimmer 30B51.7%
Source
Qwen3.6-27B—
Not directly comparable
SciCode
Muse Glimmer 30B43.6%
Source
Qwen3.6-27B—
Not directly comparable
SWE Multilingual
Muse Glimmer 30B—
Qwen3.6-27B71.3%
Source
Not directly comparable
Terminal-Bench 2.0
Muse Glimmer 30B—
Qwen3.6-27B59.3%
Source
Not directly comparable
LiveCodeBench
Muse Glimmer 30B—
Qwen3.6-27B83.9%
Source
Not directly comparable
NL2Repo
Muse Glimmer 30B—
Qwen3.6-27B36.2%
Source
Not directly comparable
SWE-bench (Vals)
Muse Glimmer 30B—
Qwen3.6-27B70.0%
Source
Not directly comparable

Multimodal

CharXiv
Muse Glimmer 30B78.8%
Source
Qwen3.6-27B78.4%
Source
Muse Glimmer 30B leads this result
ScreenSpot Pro
Muse Glimmer 30B75.4%
Source
Qwen3.6-27B—
Not directly comparable
OmniDocBench 1.5
Muse Glimmer 30B75.8%
Source
Qwen3.6-27B—
Not directly comparable
MMMU-Pro
Muse Glimmer 30B74%
Source
Qwen3.6-27B75.8%
Source
Qwen3.6-27B leads this result
MMMU
Muse Glimmer 30B—
Qwen3.6-27B82.9%
Source
Not directly comparable
RealWorldQA
Muse Glimmer 30B—
Qwen3.6-27B84.1%
Source
Not directly comparable
DynaMath
Muse Glimmer 30B—
Qwen3.6-27B85.6%
Source
Not directly comparable
MStar
Muse Glimmer 30B—
Qwen3.6-27B81.4%
Source
Not directly comparable
SimpleVQA
Muse Glimmer 30B—
Qwen3.6-27B56.1%
Source
Not directly comparable
CC-OCR
Muse Glimmer 30B—
Qwen3.6-27B81.2%
Source
Not directly comparable
CountBench
Muse Glimmer 30B—
Qwen3.6-27B97.8%
Source
Not directly comparable
RefCOCO (avg)
Muse Glimmer 30B—
Qwen3.6-27B92.5%
Source
Not directly comparable
ERQA
Muse Glimmer 30B—
Qwen3.6-27B62.5%
Source
Not directly comparable
Video-MME (with subtitle)
Muse Glimmer 30B—
Qwen3.6-27B87.7%
Source
Not directly comparable
VideoMMMU
Muse Glimmer 30B—
Qwen3.6-27B84.4%
Source
Not directly comparable
MLVU (M-Avg)
Muse Glimmer 30B—
Qwen3.6-27B86.6%
Source
Not directly comparable
V*
Muse Glimmer 30B—
Qwen3.6-27B94.7%
Source
Not directly comparable

Knowledge

MMLU-Pro
Muse Glimmer 30B—
Qwen3.6-27B86.2%
Source
Not directly comparable
MMLU-Redux
Muse Glimmer 30B—
Qwen3.6-27B93.5%
Source
Not directly comparable
SuperGPQA
Muse Glimmer 30B—
Qwen3.6-27B66%
Source
Not directly comparable
C-Eval
Muse Glimmer 30B—
Qwen3.6-27B91.4%
Source
Not directly comparable
GPQA
Muse Glimmer 30B—
Qwen3.6-27B87.8%
Source
Not directly comparable
HLE
Muse Glimmer 30B—
Qwen3.6-27B24%
Source
Not directly comparable

Instruction following

IFBench
Muse Glimmer 30B77%
Source
Qwen3.6-27B—
Not directly comparable

Math

AIME26
Muse Glimmer 30B94.7%
Source
Qwen3.6-27B94.1%
Source
Muse Glimmer 30B leads this result
HMMT Feb 2025
Muse Glimmer 30B—
Qwen3.6-27B93.8%
Source
Not directly comparable
HMMT Nov 2025
Muse Glimmer 30B—
Qwen3.6-27B90.7%
Source
Not directly comparable
HMMT Feb 2026
Muse Glimmer 30B—
Qwen3.6-27B84.3%
Source
Not directly comparable
MMAnswerBench
Muse Glimmer 30B—
Qwen3.6-27B80.8%
Source
Not directly comparable

48 public results · 5 shared

Related comparisons

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Last updated September 24, 2026

Muse Glimmer 30B vs Qwen3.6-27B

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

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

Multimodal

Agentic

Coding

Knowledge

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 Glimmer 30B or Qwen3.6-27B?

Which is better for coding, Muse Glimmer 30B or Qwen3.6-27B?

Which is better for agentic tasks, Muse Glimmer 30B or Qwen3.6-27B?

Which costs less, Muse Glimmer 30B or Qwen3.6-27B?

Which has the larger context window, Muse Glimmer 30B or Qwen3.6-27B?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Multimodal

Knowledge

Instruction following

Math

Related comparisons

Watch Muse Glimmer 30B vs Qwen3.6-27B