Long documents
Prompts that approach the documented context limit
Muse Wonder
Muse Wonder has the larger documented context window.
Confidence: documented
Muse Glimmer 30B vs Muse Wonder
Updated October 12, 2026. Rank says Muse Wonder is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 0
- Muse Glimmer 30B only
- 14
- Muse Wonder only
- 6
- Like-for-like categories
- 1 / 8
Estimated: Muse Glimmer 30B and Muse Wonder. Conditional ranges do not establish rank confidence.How 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.
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: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Muse Wonder is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedChat 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-ratesCache-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-ratesRepository 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.
31.2Muse Glimmer 30B69.8Muse Wonder
Directional only · BenchAlign v5.8
Muse Wonder has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
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.
14 Muse Glimmer 30B only
6 Muse Wonder only
1 category rests 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.
Knowledge
Like-for-like- Muse Glimmer 30B
- 44.1
- Supported · #100/180
- Muse Wonder
- 74.0
- Supported · #12/180
- Basis
- BenchAlign v5.8 lane · 0 vs 1 public rows
- Reading
- Muse Wonder leads
Coding
Directional only- Muse Glimmer 30B
- 31.2
- Estimated · #91/148
- Muse Wonder
- 69.8
- Supported · #10/148
- Basis
- BenchAlign v5.8 lane · 4 vs 3 public rows
- Reading
- Directional only
Agentic
Not comparable- Muse Glimmer 30B
- 24.5
- Estimated · #98/123
- Muse Wonder
- Not ranked
- Basis
- BenchAlign v5.8 lane · 4 vs 2 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Muse Glimmer 30B
- 83.1
- Unranked · 2 rankable rows
- Muse Wonder
- 81.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Muse Glimmer 30B
- 65.1
- #41/54
- Muse Wonder
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Muse Glimmer 30B
- Not ranked
- Muse Wonder
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Muse Glimmer 30B
- 81.1
- #54/127
- Muse Wonder
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Muse Glimmer 30B
- 77.9
- Unranked · 1 rankable row
- Muse Wonder
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
| Category | Muse Glimmer 30B | Muse Wonder | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 44.1Supported · #100/180 | 74.0Supported · #12/180 | Like-for-likeBenchAlign v5.8 lane · 0 vs 1 public rows | Muse Wonder leads |
| Coding | 31.2Estimated · #91/148 | 69.8Supported · #10/148 | Directional onlyBenchAlign v5.8 lane · 4 vs 3 public rows | Directional only |
| Agentic | 24.5Estimated · #98/123 | Not ranked | Not comparableBenchAlign v5.8 lane · 4 vs 2 public rows | Not comparable |
| Reasoning | 83.1Unranked · 2 rankable rows | 81.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 65.1#41/54 | 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 |
| Instruction following | 81.1#54/127 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Math | 77.9Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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
- Muse Wonder
- $0.014
- Fits in one request
Muse Glimmer 30B 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
- Muse Wonder
- $0.26
- 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
- Muse Glimmer 30B
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
- Muse Wonder
- $0.32
- Fits in one request
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.
SpecificationMuse Glimmer 30BMuse Wonder
Context window
Maximum documented context; output-token limits may be lower.
Muse Glimmer 30B
131K
Muse Wonder
API model ID
Muse Glimmer 30B
Not sourced
Muse Wonder
muse-wonder-1.0
Meta: Muse Wonder models and pricingCached-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
Muse Wonder
$0.2 per 1M cached input tokens
Meta: Muse Wonder models and pricingDocumented inputs
Muse Glimmer 30B
Not sourced
Muse Wonder
Not sourced
Documented outputs
Muse Glimmer 30B
Not sourced
Muse Wonder
Not sourced
Provider availability
Muse Glimmer 30B
Not sourced
Muse Wonder
Not sourced
Reasoning profile
Muse Glimmer 30B
Reasoning
Muse Wonder
Reasoning
Weight access
Muse Glimmer 30B
Open Weight
Muse Wonder
Proprietary
License
Muse Glimmer 30B
Open Weight
Muse Wonder
Proprietary
Release date
Muse Glimmer 30B
2026-08-10
Muse Wonder
2026-10-12
If you already use one of these models
- Deployment change
- Both entries list Meta as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Muse Wonder has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Muse Glimmer 30B or Muse Wonder?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Muse Glimmer 30B or Muse Wonder?
Muse Wonder scores higher for coding on the public lane, 69.8 to 31.2. 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 Muse Wonder?
Muse Wonder is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Muse Glimmer 30B or Muse Wonder?
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 Muse Wonder?
Muse Wonder 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 evidence20 rows
Agentic
MCP Atlas
Muse Glimmer 30B75.5%SourceMuse Wonder—Not directly comparable
DeepSearchQA
Muse Glimmer 30B74.6%SourceMuse Wonder—Not directly comparable
skillsBench
Muse Glimmer 30B44.3%SourceMuse Wonder—Not directly comparable
OSWorld-Verified
Muse Glimmer 30B65.9%SourceMuse Wonder—Not directly comparable
JobBench
Muse Glimmer 30B—Muse Wonder70.5%SourceNot directly comparable
Finance Agent v2
Muse Glimmer 30B—Muse Wonder56.6%SourceNot directly comparable
Coding
SWE-bench Pro
Muse Glimmer 30B51.2%SourceMuse Wonder—Not directly comparable
SWE-bench Verified
Muse Glimmer 30B76%SourceMuse Wonder—Not directly comparable
Terminal-Bench 2.1
Muse Glimmer 30B51.7%SourceMuse Wonder—Not directly comparable
SciCode
Muse Glimmer 30B43.6%SourceMuse Wonder—Not directly comparable
DeepSWE
Muse Glimmer 30B—Muse Wonder78.8%SourceNot directly comparable
ProgramBench
Muse Glimmer 30B—Muse Wonder50.0%SourceNot directly comparable
sweMarathon
Muse Glimmer 30B—Muse Wonder53.1%SourceNot directly comparable
Multimodal
Knowledge
HealthBench Professional
Muse Glimmer 30B—Muse Wonder60.5%SourceNot directly comparable
Instruction following
IFBench
Muse Glimmer 30B77%SourceMuse Wonder—Not directly comparable
Math
AIME26
Muse Glimmer 30B94.7%SourceMuse Wonder—Not directly comparable
20 public results · 0 shared
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Last updated October 12, 2026