Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
ZAYA1-8B is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Muse Glimmer 30B vs ZAYA1-8B
Updated September 24, 2026. Rank says Muse Glimmer 30B 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
Muse Glimmer 30B has the higher public score estimate, 41.73 versus 31.1, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 2
- Muse Glimmer 30B only
- 12
- ZAYA1-8B only
- 9
- Like-for-like categories
- 1 / 8
Estimated: Muse Glimmer 30B and ZAYA1-8BHow 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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Show secondary and unsupported calls
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
ZAYA1-8B is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documentedChat 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. ZAYA1-8B does not fit this workload in one request. Muse Glimmer 30B has no comparable published API token rate. ZAYA1-8B 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.
36.3Muse Glimmer 30B—ZAYA1-8B
Not comparable · BenchAlign v5.7
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
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.
12 Muse Glimmer 30B only
2 Shared
9 ZAYA1-8B 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.
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.
IFBenchInstruction following
Normalized gap 24.4Muse Glimmer 30B:77%ZAYA1-8B:52.6%AIME26Math
Normalized gap 5.6Muse Glimmer 30B:94.7%ZAYA1-8B:89.1%
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.
Instruction following
Like-for-like- Muse Glimmer 30B
- 77.7
- #55/124
- ZAYA1-8B
- 23.5
- #121/124
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Muse Glimmer 30B leads
Knowledge
Directional only- Muse Glimmer 30B
- 43.9
- Estimated · #75/158
- ZAYA1-8B
- 32.3
- Estimated · #118/158
- Basis
- BenchAlign v5.7 lane · 0 vs 3 public rows
- Reading
- Directional only
Agentic
Not comparable- Muse Glimmer 30B
- 27.6
- Estimated · #72/105
- ZAYA1-8B
- Not ranked
- Basis
- BenchAlign v5.7 lane · 4 vs 1 public rows
- Reading
- Not comparable
Coding
Not comparable- Muse Glimmer 30B
- 36.3
- Estimated · #69/135
- ZAYA1-8B
- Not ranked
- Basis
- BenchAlign v5.7 lane · 4 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Muse Glimmer 30B
- 79.3
- Unranked · 2 rankable rows
- ZAYA1-8B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Muse Glimmer 30B
- 46.3
- #42/50
- ZAYA1-8B
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Muse Glimmer 30B
- Not ranked
- ZAYA1-8B
- 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
- ZAYA1-8B
- 61.3
- Unranked · 4 rankable rows
- Basis
- Provisional lane · 1 vs 2 weighted rows
- Reading
- Not comparable
| Category | Muse Glimmer 30B | ZAYA1-8B | Basis | Reading |
|---|---|---|---|---|
| Instruction following | 77.7#55/124 | 23.5#121/124 | Like-for-likeProvisional lane · 1 vs 1 weighted rows | Muse Glimmer 30B leads |
| Knowledge | 43.9Estimated · #75/158 | 32.3Estimated · #118/158 | Directional onlyBenchAlign v5.7 lane · 0 vs 3 public rows | Directional only |
| Agentic | 27.6Estimated · #72/105 | Not ranked | Not comparableBenchAlign v5.7 lane · 4 vs 1 public rows | Not comparable |
| Coding | 36.3Estimated · #69/135 | Not ranked | Not comparableBenchAlign v5.7 lane · 4 vs 1 public rows | Not comparable |
| Reasoning | 79.3Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 46.3#42/50 | 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 |
| Math | 75.4Unranked · 1 rankable row | 61.3Unranked · 4 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.
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
- ZAYA1-8B
- Self-hosted; infrastructure cost varies
- Fits in one request
Muse Glimmer 30B has no comparable published API token rate. ZAYA1-8B 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
- ZAYA1-8B
- Self-hosted; infrastructure cost varies
- Fits in one request
Muse Glimmer 30B has no comparable published API token rate. ZAYA1-8B 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
- ZAYA1-8B
- 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. ZAYA1-8B does not fit this workload in one request. Muse Glimmer 30B has no comparable published API token rate. ZAYA1-8B 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 30BZAYA1-8B
Context window
Maximum documented context; output-token limits may be lower.
Muse Glimmer 30B
131K
ZAYA1-8B
131K
API model ID
Muse Glimmer 30B
Not sourced
ZAYA1-8B
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
ZAYA1-8B
No comparable hosted API rate
Documented inputs
Muse Glimmer 30B
Not sourced
ZAYA1-8B
Not sourced
Documented outputs
Muse Glimmer 30B
Not sourced
ZAYA1-8B
Not sourced
Provider availability
Muse Glimmer 30B
Not sourced
ZAYA1-8B
Not sourced
Reasoning profile
Muse Glimmer 30B
Reasoning
ZAYA1-8B
Reasoning
Weight access
Muse Glimmer 30B
Open Weight
ZAYA1-8B
Open Weight
License
Muse Glimmer 30B
Open Weight
ZAYA1-8B
Open Weight
Release date
Muse Glimmer 30B
2026-08-10
ZAYA1-8B
2026-05-05
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 Glimmer 30B has the higher public score estimate, 41.73 versus 31.1, 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 131K.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Muse Glimmer 30B or ZAYA1-8B?
Muse Glimmer 30B has the higher public score estimate, 41.73 versus 31.1, 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 ZAYA1-8B?
ZAYA1-8B is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Muse Glimmer 30B or ZAYA1-8B?
ZAYA1-8B 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 ZAYA1-8B?
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 ZAYA1-8B?
Both models list the same context window, 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 evidence23 rows
Agentic
MCP Atlas
Muse Glimmer 30B75.5%SourceZAYA1-8B—Not directly comparable
DeepSearchQA
Muse Glimmer 30B74.6%SourceZAYA1-8B—Not directly comparable
skillsBench
Muse Glimmer 30B44.3%SourceZAYA1-8B—Not directly comparable
OSWorld-Verified
Muse Glimmer 30B65.9%SourceZAYA1-8B—Not directly comparable
BFCL v4
Muse Glimmer 30B—ZAYA1-8B39.2%SourceNot directly comparable
Coding
SWE-bench Pro
Muse Glimmer 30B51.2%SourceZAYA1-8B—Not directly comparable
SWE-bench Verified
Muse Glimmer 30B76%SourceZAYA1-8B—Not directly comparable
Terminal-Bench 2.1
Muse Glimmer 30B51.7%SourceZAYA1-8B—Not directly comparable
SciCode
Muse Glimmer 30B43.6%SourceZAYA1-8B—Not directly comparable
LiveCodeBench v6
Muse Glimmer 30B—ZAYA1-8B65.8%SourceNot directly comparable
Multimodal
Knowledge
Instruction following
Math
23 public results · 2 shared
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Last updated September 24, 2026