Long documents
Prompts that approach the documented context limit
Qwen3.7 Max
Qwen3.7 Max has the larger documented context window.
Confidence: documented
Muse Glimmer 30B vs Qwen3.7 Max
Updated September 24, 2026. Rank says Qwen3.7 Max 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
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Decision reading
Qwen3.7 Max has the higher public score, 63.18 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.
- Shared results
- 5
- Muse Glimmer 30B only
- 9
- Qwen3.7 Max only
- 36
- Like-for-like categories
- 1 / 8
Estimated: Muse Glimmer 30B · Supported: Qwen3.7 MaxHow 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 Glimmer 30B is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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. Qwen3.7 Max 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 30B46.2Qwen3.7 Max
Directional only · BenchAlign v5.7
Qwen3.7 Max 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.
9 Muse Glimmer 30B only
5 Shared
36 Qwen3.7 Max only
3 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.
SciCodeCoding
Normalized gap 9.9Muse Glimmer 30B:43.6%Qwen3.7 Max:53.5%SWE-bench ProCoding
Normalized gap 9.4Muse Glimmer 30B:51.2%Qwen3.7 Max:60.6%SWE-bench VerifiedCoding
Normalized gap 4.4Muse Glimmer 30B:76%Qwen3.7 Max:80.4%IFBenchInstruction following
Normalized gap 2.1Muse Glimmer 30B:77%Qwen3.7 Max:79.1%
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
- Qwen3.7 Max
- 89.2
- #17/124
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Qwen3.7 Max leads
Agentic
Directional only- Muse Glimmer 30B
- 27.6
- Estimated · #72/105
- Qwen3.7 Max
- 39.8
- Supported · #46/105
- Basis
- BenchAlign v5.7 lane · 4 vs 10 public rows
- Reading
- Directional only
Coding
Directional only- Muse Glimmer 30B
- 36.3
- Estimated · #69/135
- Qwen3.7 Max
- 46.2
- Supported · #45/135
- Basis
- BenchAlign v5.7 lane · 4 vs 10 public rows
- Reading
- Directional only
Knowledge
Directional only- Muse Glimmer 30B
- 43.9
- Estimated · #75/158
- Qwen3.7 Max
- 60.8
- Supported · #34/158
- Basis
- BenchAlign v5.7 lane · 0 vs 9 public rows
- Reading
- Directional only
Reasoning
Not comparable- Muse Glimmer 30B
- 79.3
- Unranked · 2 rankable rows
- Qwen3.7 Max
- 76.1
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Muse Glimmer 30B
- 46.3
- #42/50
- Qwen3.7 Max
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Muse Glimmer 30B
- Not ranked
- Qwen3.7 Max
- 100.0
- #1/12
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- Muse Glimmer 30B
- 75.4
- Unranked · 1 rankable row
- Qwen3.7 Max
- 81.9
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Not comparable
| Category | Muse Glimmer 30B | Qwen3.7 Max | Basis | Reading |
|---|---|---|---|---|
| Instruction following | 77.7#55/124 | 89.2#17/124 | Like-for-likeProvisional lane · 1 vs 1 weighted rows | Qwen3.7 Max leads |
| Agentic | 27.6Estimated · #72/105 | 39.8Supported · #46/105 | Directional onlyBenchAlign v5.7 lane · 4 vs 10 public rows | Directional only |
| Coding | 36.3Estimated · #69/135 | 46.2Supported · #45/135 | Directional onlyBenchAlign v5.7 lane · 4 vs 10 public rows | Directional only |
| Knowledge | 43.9Estimated · #75/158 | 60.8Supported · #34/158 | Directional onlyBenchAlign v5.7 lane · 0 vs 9 public rows | Directional only |
| Reasoning | 79.3Unranked · 2 rankable rows | 76.1Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | 46.3#42/50 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | 100.0#1/12 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | 75.4Unranked · 1 rankable row | 81.9Unranked · 3 rankable rows | Not comparableProvisional lane · 1 vs 1 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
- Qwen3.7 Max
- API rate not published
- Fits in one request
Muse Glimmer 30B has no comparable published API token rate. Qwen3.7 Max 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
- Qwen3.7 Max
- API rate not published
- Fits in one request
Muse Glimmer 30B has no comparable published API token rate. Qwen3.7 Max 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
- Qwen3.7 Max
- API rate not published
- 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. Qwen3.7 Max 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 30BQwen3.7 Max
Context window
Maximum documented context; output-token limits may be lower.
Muse Glimmer 30B
131K
Qwen3.7 Max
1M
API model ID
Muse Glimmer 30B
Not sourced
Qwen3.7 Max
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
Qwen3.7 Max
No comparable hosted API rate
Documented inputs
Muse Glimmer 30B
Not sourced
Qwen3.7 Max
Not sourced
Documented outputs
Muse Glimmer 30B
Not sourced
Qwen3.7 Max
Not sourced
Provider availability
Muse Glimmer 30B
Not sourced
Qwen3.7 Max
Not sourced
Reasoning profile
Muse Glimmer 30B
Reasoning
Qwen3.7 Max
Reasoning
Weight access
Muse Glimmer 30B
Open Weight
Qwen3.7 Max
Proprietary
License
Muse Glimmer 30B
Open Weight
Qwen3.7 Max
Proprietary
Release date
Muse Glimmer 30B
2026-08-10
Qwen3.7 Max
2026-05-16
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
- Qwen3.7 Max has the higher public score, 63.18 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
- Qwen3.7 Max 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 Qwen3.7 Max?
Qwen3.7 Max has the higher public score, 63.18 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, Muse Glimmer 30B or Qwen3.7 Max?
Qwen3.7 Max scores higher for coding on the public lane, 46.2 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 Qwen3.7 Max?
Qwen3.7 Max scores higher for agentic tasks on the public lane, 39.8 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 Qwen3.7 Max?
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 Qwen3.7 Max?
Qwen3.7 Max 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 evidence50 rows
Agentic
MCP Atlas
Muse Glimmer 30B75.5%SourceQwen3.7 Max76.4%SourceQwen3.7 Max leads this result
DeepSearchQA
Muse Glimmer 30B74.6%SourceQwen3.7 Max—Not directly comparable
skillsBench
Muse Glimmer 30B44.3%SourceQwen3.7 Max—Not directly comparable
OSWorld-Verified
Muse Glimmer 30B65.9%SourceQwen3.7 Max—Not directly comparable
Terminal-Bench 2.0
Muse Glimmer 30B—Qwen3.7 Max69.7%SourceNot directly comparable
QwenClawBench
Muse Glimmer 30B—Qwen3.7 Max64.3%SourceNot directly comparable
Claw-Eval
Muse Glimmer 30B—Qwen3.7 Max65.2%SourceNot directly comparable
BFCL v4
Muse Glimmer 30B—Qwen3.7 Max75.0%SourceNot directly comparable
VITA-Bench
Muse Glimmer 30B—Qwen3.7 Max47.9%SourceNot directly comparable
HLE w/ tools
Muse Glimmer 30B—Qwen3.7 Max53.5%SourceNot directly comparable
Gert Labs
Muse Glimmer 30B—Qwen3.7 Max64.27%SourceNot directly comparable
ResearchClawBench
Muse Glimmer 30B—Qwen3.7 Max18.7%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Muse Glimmer 30B—Qwen3.7 Max61.0%SourceNot directly comparable
Coding
SWE-bench Pro
Muse Glimmer 30B51.2%SourceQwen3.7 Max60.6%SourceQwen3.7 Max leads this result
SWE-bench Verified
Muse Glimmer 30B76%SourceQwen3.7 Max80.4%SourceQwen3.7 Max leads this result
Terminal-Bench 2.1
Muse Glimmer 30B51.7%SourceQwen3.7 Max—Not directly comparable
SciCode
Muse Glimmer 30B43.6%SourceQwen3.7 Max53.5%SourceQwen3.7 Max leads this result
SWE Multilingual
Muse Glimmer 30B—Qwen3.7 Max78.3%SourceNot directly comparable
NL2Repo
Muse Glimmer 30B—Qwen3.7 Max47.2%SourceNot directly comparable
LiveCodeBench
Muse Glimmer 30B—Qwen3.7 Max91.6%SourceNot directly comparable
Terminal-Bench 2.0
Muse Glimmer 30B—Qwen3.7 Max69.7%SourceNot directly comparable
OpenHarmony Bench
Muse Glimmer 30B—Qwen3.7 Max53.4%SourceNot directly comparable
LiveCodeBench (Vals)
Muse Glimmer 30B—Qwen3.7 Max87.1%SourceNot directly comparable
SWE-bench (Vals)
Muse Glimmer 30B—Qwen3.7 Max68.8%SourceNot directly comparable
Reasoning
Multimodal
Knowledge
GPQA
Muse Glimmer 30B—Qwen3.7 Max92.4%SourceNot directly comparable
GPQA-D
Muse Glimmer 30B—Qwen3.7 Max92.4%SourceNot directly comparable
HLE
Muse Glimmer 30B—Qwen3.7 Max41.4%SourceNot directly comparable
MMLU-Pro
Muse Glimmer 30B—Qwen3.7 Max89.6%SourceNot directly comparable
MMLU-Redux
Muse Glimmer 30B—Qwen3.7 Max95%SourceNot directly comparable
SuperGPQA
Muse Glimmer 30B—Qwen3.7 Max73.6%SourceNot directly comparable
MMMLU
Muse Glimmer 30B—Qwen3.7 Max90.3%SourceNot directly comparable
GPQA Diamond (Vals)
Muse Glimmer 30B—Qwen3.7 Max90.2%SourceNot directly comparable
MMLU-Pro (Vals)
Muse Glimmer 30B—Qwen3.7 Max89.3%SourceNot directly comparable
Multilingual
MMLU-ProX
Muse Glimmer 30B—Qwen3.7 Max87%SourceNot directly comparable
NOVA-63
Muse Glimmer 30B—Qwen3.7 Max59.0%SourceNot directly comparable
INCLUDE
Muse Glimmer 30B—Qwen3.7 Max86.2%SourceNot directly comparable
MAXIFE
Muse Glimmer 30B—Qwen3.7 Max89.2%SourceNot directly comparable
PolyMath
Muse Glimmer 30B—Qwen3.7 Max86.5%SourceNot directly comparable
Instruction following
Math
50 public results · 5 shared
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Last updated September 24, 2026