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BenchLM

Claude Sonnet 5 vs Muse Glimmer 30B

Updated September 24, 2026. Rank says Claude Sonnet 5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Claude Sonnet 5 has the higher public score, 67.01 versus 41.73, and the 90% score intervals do not overlap. 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
Anthropic logo

Anthropic

67.01/100

Supported · Public rank #20

90% interval 62.8–71.2

Model B
Meta logo

Meta

41.73/100

Estimated · Public rank #108

90% interval 30.2–53.3

Shared results
5
Claude Sonnet 5 only
21
Muse Glimmer 30B only
9
Like-for-like categories
0 / 8
Supported: Claude Sonnet 5 · Estimated: Muse Glimmer 30BHow the comparison works

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

    Claude Sonnet 5

    Claude Sonnet 5 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.

    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.

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

60.8Claude Sonnet 536.3Muse Glimmer 30B

Directional only · BenchAlign v5.7

Claude Sonnet 5 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.

Same basis rules as the category table below

What is actually comparable

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

4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

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.

Agentic

Directional only
Claude Sonnet 5
64.2
Supported · #12/105
Muse Glimmer 30B
27.6
Estimated · #72/105
Basis
BenchAlign v5.7 lane · 7 vs 4 public rows
Reading
Directional only

Coding

Directional only
Claude Sonnet 5
60.8
Supported · #15/135
Muse Glimmer 30B
36.3
Estimated · #69/135
Basis
BenchAlign v5.7 lane · 11 vs 4 public rows
Reading
Directional only

Multimodal

Directional only
Claude Sonnet 5
77.4
#15/50
Muse Glimmer 30B
46.3
#42/50
Basis
Provisional lane · 1 vs 2 weighted rows
Reading
Directional only

Knowledge

Directional only
Claude Sonnet 5
64.5
Supported · #23/158
Muse Glimmer 30B
43.9
Estimated · #75/158
Basis
BenchAlign v5.7 lane · 6 vs 0 public rows
Reading
Directional only

Reasoning

Not comparable
Claude Sonnet 5
78.4
Unranked · 2 rankable rows
Muse Glimmer 30B
79.3
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Sonnet 5
Not ranked
Muse Glimmer 30B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Sonnet 5
Not ranked
Muse Glimmer 30B
77.7
#55/124
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Sonnet 5
Not ranked
Muse Glimmer 30B
75.4
Unranked · 1 rankable row
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
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

Claude Sonnet 5
$0.007
Fits in one request
Muse Glimmer 30B
Self-hosted; infrastructure cost varies
Fits in one request

Muse Glimmer 30B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Claude Sonnet 5
$0.13
Fits in one request
Muse Glimmer 30B
Self-hosted; infrastructure cost varies
Fits in one request

Muse Glimmer 30B has no comparable published API token rate.

Cache-heavy agent loop

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

Claude Sonnet 5
$0.18
Fits in one request
Muse Glimmer 30B
Self-hosted; infrastructure cost varies
Does not fit 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.

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.

Context window

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

Claude Sonnet 5

Muse Glimmer 30B

131K

Cached-input rate

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

Claude Sonnet 5

$0.2 per 1M cached input tokens

Claude API pricing

Muse Glimmer 30B

No comparable hosted API rate

Provider availability

Claude Sonnet 5

Generally Available · Claude API

Anthropic model overview

Muse Glimmer 30B

Not sourced

Reasoning profile

Claude Sonnet 5

Reasoning

Muse Glimmer 30B

Reasoning

Weight access

Claude Sonnet 5

Proprietary

Muse Glimmer 30B

Open Weight

License

Claude Sonnet 5

Proprietary

Muse Glimmer 30B

Open Weight

Release date

Claude Sonnet 5

2026-06-30

Muse Glimmer 30B

2026-08-10

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
Claude Sonnet 5 has the higher public score, 67.01 versus 41.73, and the 90% score intervals do not overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Claude Sonnet 5 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Claude Sonnet 5 or Muse Glimmer 30B?

Claude Sonnet 5 has the higher public score, 67.01 versus 41.73, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Claude Sonnet 5 or Muse Glimmer 30B?

Claude Sonnet 5 scores higher for coding on the public lane, 60.8 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, Claude Sonnet 5 or Muse Glimmer 30B?

Claude Sonnet 5 scores higher for agentic tasks on the public lane, 64.2 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, Claude Sonnet 5 or Muse Glimmer 30B?

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, Claude Sonnet 5 or Muse Glimmer 30B?

Claude Sonnet 5 has the larger documented context window: 1M, compared with 131K.

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

  • Terminal-Bench 3.0

    Claude Sonnet 514.6%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • Terminal-Bench 2.1

    Claude Sonnet 580.4%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • BrowseComp

    Claude Sonnet 584.7%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • HLE w/ tools

    Claude Sonnet 557.4%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • OSWorld-Verified

    Claude Sonnet 581.2%
    Source
    Muse Glimmer 30B65.9%
    Source

    Claude Sonnet 5 leads this result

  • Terminal-Bench 2.1 (Vals)

    Claude Sonnet 574.5%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • ApprenticeBench

    Claude Sonnet 516%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • MCP Atlas

    Claude Sonnet 5—
    Muse Glimmer 30B75.5%
    Source

    Not directly comparable

  • DeepSearchQA

    Claude Sonnet 5—
    Muse Glimmer 30B74.6%
    Source

    Not directly comparable

  • skillsBench

    Claude Sonnet 5—
    Muse Glimmer 30B44.3%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Sonnet 585.2%
    Source
    Muse Glimmer 30B76%
    Source

    Claude Sonnet 5 leads this result

  • SWE-bench Pro

    Claude Sonnet 563.2%
    Source
    Muse Glimmer 30B51.2%
    Source

    Claude Sonnet 5 leads this result

  • SWE Multilingual

    Claude Sonnet 578.3%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • SWE Multimodal

    Claude Sonnet 528.1%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • Terminal-Bench 2.1

    Claude Sonnet 580.4%
    Source
    Muse Glimmer 30B51.7%
    Source

    Claude Sonnet 5 leads this result

  • FrontierCode 1.1 Main

    Claude Sonnet 542.7%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • cursorBench32

    Claude Sonnet 561.5%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • VulcanBench CII v1

    Claude Sonnet 589.2%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • LiveCodeBench (Vals)

    Claude Sonnet 582.4%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • SWE-bench (Vals)

    Claude Sonnet 579.6%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • cursorBench40

    Claude Sonnet 534.1%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • SciCode

    Claude Sonnet 5—
    Muse Glimmer 30B43.6%
    Source

    Not directly comparable

Multimodal

  • CharXiv

    Claude Sonnet 588.3%
    Source
    Muse Glimmer 30B78.8%
    Source

    Claude Sonnet 5 leads this result

  • CharXiv w/o tools

    Claude Sonnet 577%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • ScreenSpot Pro

    Claude Sonnet 5—
    Muse Glimmer 30B75.4%
    Source

    Not directly comparable

  • OmniDocBench 1.5

    Claude Sonnet 5—
    Muse Glimmer 30B75.8%
    Source

    Not directly comparable

  • MMMU-Pro

    Claude Sonnet 5—
    Muse Glimmer 30B74%
    Source

    Not directly comparable

Knowledge

  • HLE

    Claude Sonnet 557.4%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • HLE w/o tools

    Claude Sonnet 543.2%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • HLE-Verified

    Claude Sonnet 531.0%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • LABBench2

    Claude Sonnet 580.1%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • GPQA Diamond (Vals)

    Claude Sonnet 588.9%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • MMLU-Pro (Vals)

    Claude Sonnet 587.5%
    Source
    Muse Glimmer 30B—

    Not directly comparable

Instruction following

  • IFBench

    Claude Sonnet 5—
    Muse Glimmer 30B77%
    Source

    Not directly comparable

Math

  • AIME26

    Claude Sonnet 5—
    Muse Glimmer 30B94.7%
    Source

    Not directly comparable

35 public results · 5 shared

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Last updated September 24, 2026