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BenchLM
Model A
Muse Glimmer 30B

Meta

52.4/100

Estimated · Public rank #112

90% interval 42.5–62.3

Muse Glimmer 30B vs Qwen3.8-27B

Updated August 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Qwen3.8-27B

Alibaba

—

Evidence status unavailable

90% interval unavailable

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.

6 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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.

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Qwen3.8-27B

    Qwen3.8-27B leads on the same 1 weighted benchmark row.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    Qwen3.8-27B

    Qwen3.8-27B 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

    The category averages use different weighted benchmark sets, so they are 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. Qwen3.8-27B 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

What is actually comparable

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

Shared results
6
Muse Glimmer 30B only
8
Qwen3.8-27B only
21
Like-for-like categories
2 / 8

2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Like-for-like
Muse Glimmer 30B
65.9
Qwen3.8-27B
84.3
Weighted basis
1 vs 1 rows
Reading
Qwen3.8-27B leads

Instruction following

Like-for-like
Muse Glimmer 30B
77.0
Qwen3.8-27B
79.5
Weighted basis
1 vs 1 rows
Reading
Qwen3.8-27B leads

Coding

Directional only
Muse Glimmer 30B
57.8
Qwen3.8-27B
61.7
Weighted basis
3 vs 1 rows
Reading
Directional only

Multimodal

Directional only
Muse Glimmer 30B
75.7
Qwen3.8-27B
90.2
Weighted basis
2 vs 1 rows
Reading
Directional only

Reasoning

Not comparable
Muse Glimmer 30B
Not measured
Qwen3.8-27B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Muse Glimmer 30B
Not measured
Qwen3.8-27B
38.7
Weighted basis
0 vs 2 rows
Reading
Not comparable

Math

Not comparable
Muse Glimmer 30B
94.7
Qwen3.8-27B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Muse Glimmer 30B
Not measured
Qwen3.8-27B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

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.

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
Qwen3.8-27B
Self-hosted; infrastructure cost varies
Fits in one request

Muse Glimmer 30B 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 Glimmer 30B
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.8-27B
Self-hosted; infrastructure cost varies
Fits in one request

Muse Glimmer 30B 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 Glimmer 30B
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable
Qwen3.8-27B
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. Qwen3.8-27B 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.

Context window

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

Muse Glimmer 30B

131K

Qwen3.8-27B

262K

Qwen3.8-27B model card

API model ID

Muse Glimmer 30B

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 Glimmer 30B

No comparable hosted API rate

Qwen3.8-27B

No comparable hosted API rate

Qwen3.8-27B model card

Documented inputs

Muse Glimmer 30B

Not sourced

Qwen3.8-27B

Not sourced

Documented outputs

Muse Glimmer 30B

Not sourced

Qwen3.8-27B

Not sourced

Provider availability

Muse Glimmer 30B

Not sourced

Qwen3.8-27B

Not sourced

Reasoning profile

Muse Glimmer 30B

Reasoning

Qwen3.8-27B

Reasoning

Weight access

Muse Glimmer 30B

Open Weight

Qwen3.8-27B

Open Weight

License

Muse Glimmer 30B

Open Weight

Qwen3.8-27B

Open Weight

Release date

Muse Glimmer 30B

2026-08-10

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
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
Qwen3.8-27B has the larger documented window (262K).

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 evidence35 rows

Agentic

  • MCP Atlas

    Muse Glimmer 30B75.5%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • DeepSearchQA

    Muse Glimmer 30B74.6%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • skillsBench

    Muse Glimmer 30B44.3%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • OSWorld-Verified

    Muse Glimmer 30B65.9%
    Source
    Qwen3.8-27B84.3%
    Source

    Qwen3.8-27B leads this result

  • Terminal-Bench 2.1

    Muse Glimmer 30B—
    Qwen3.8-27B73.0%
    Source

    Not directly comparable

  • CoWorkBench

    Muse Glimmer 30B—
    Qwen3.8-27B70.7%
    Source

    Not directly comparable

  • JobBench

    Muse Glimmer 30B—
    Qwen3.8-27B33.4%
    Source

    Not directly comparable

  • Agents' Last Exam

    Muse Glimmer 30B—
    Qwen3.8-27B42.9%
    Source

    Not directly comparable

  • WebArena-Verified

    Muse Glimmer 30B—
    Qwen3.8-27B64.8%
    Source

    Not directly comparable

  • AndroidWorld

    Muse Glimmer 30B—
    Qwen3.8-27B81.9%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    Muse Glimmer 30B51.2%
    Source
    Qwen3.8-27B61.7%
    Source

    Qwen3.8-27B leads this result

  • SWE-bench Verified

    Muse Glimmer 30B76%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • Terminal-Bench 2.1

    Muse Glimmer 30B51.7%
    Source
    Qwen3.8-27B73.0%
    Source

    Qwen3.8-27B leads this result

  • SciCode

    Muse Glimmer 30B43.6%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • NL2Repo

    Muse Glimmer 30B—
    Qwen3.8-27B42.3%
    Source

    Not directly comparable

  • deepSwe

    Muse Glimmer 30B—
    Qwen3.8-27B42.2%
    Source

    Not directly comparable

  • LiveCodeBench v6

    Muse Glimmer 30B—
    Qwen3.8-27B90.3%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Muse Glimmer 30B—
    Qwen3.8-27B89.2%
    Source

    Not directly comparable

  • GPQA-D

    Muse Glimmer 30B—
    Qwen3.8-27B89.2%
    Source

    Not directly comparable

  • HLE

    Muse Glimmer 30B—
    Qwen3.8-27B30.8%
    Source

    Not directly comparable

  • HLE w/o tools

    Muse Glimmer 30B—
    Qwen3.8-27B30.8%
    Source

    Not directly comparable

Math

  • AIME26

    Muse Glimmer 30B94.7%
    Source
    Qwen3.8-27B—

    Not directly comparable

Multimodal

  • CharXiv

    Muse Glimmer 30B78.8%
    Source
    Qwen3.8-27B90.2%
    Source

    Qwen3.8-27B leads this result

  • ScreenSpot Pro

    Muse Glimmer 30B75.4%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • OmniDocBench 1.5

    Muse Glimmer 30B75.8%
    Source
    Qwen3.8-27B91.1%
    Source

    Qwen3.8-27B leads this result

  • MMMU-Pro

    Muse Glimmer 30B74%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • MathVision

    Muse Glimmer 30B—
    Qwen3.8-27B90.0%
    Source

    Not directly comparable

  • MathVision w/ Python

    Muse Glimmer 30B—
    Qwen3.8-27B94.6%
    Source

    Not directly comparable

  • BabyVision

    Muse Glimmer 30B—
    Qwen3.8-27B65.7%
    Source

    Not directly comparable

  • BabyVision w/ Python

    Muse Glimmer 30B—
    Qwen3.8-27B85.6%
    Source

    Not directly comparable

  • Vision2Web

    Muse Glimmer 30B—
    Qwen3.8-27B62.9%
    Source

    Not directly comparable

  • CharXiv w/o tools

    Muse Glimmer 30B—
    Qwen3.8-27B83.7%
    Source

    Not directly comparable

  • RealWorldQA

    Muse Glimmer 30B—
    Qwen3.8-27B85.9%
    Source

    Not directly comparable

  • ERQA

    Muse Glimmer 30B—
    Qwen3.8-27B65.5%
    Source

    Not directly comparable

Instruction following

  • IFBench

    Muse Glimmer 30B77%
    Source
    Qwen3.8-27B79.5%
    Source

    Qwen3.8-27B leads this result

Frequently asked questions

Which is better, Muse Glimmer 30B or Qwen3.8-27B?

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 Glimmer 30B or Qwen3.8-27B?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. 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 Qwen3.8-27B?

Qwen3.8-27B leads the like-for-like agentic tasks comparison across 1 shared weighted benchmark row.

Which costs less, Muse Glimmer 30B 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 Glimmer 30B or Qwen3.8-27B?

Qwen3.8-27B has the larger documented context window: 262K, compared with 131K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 14, 2026

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