Long documents
Prompts that approach the documented context limit
GLM-5V-Turbo
GLM-5V-Turbo has the larger documented context window.
Confidence: documented
GLM-5V-Turbo vs Muse Glimmer 30B
Updated September 24, 2026. Rank says GLM-5V-Turbo 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
- GLM-5V-Turbo only
- 2
- Muse Glimmer 30B only
- 14
- Like-for-like categories
- 0 / 8
Estimated: GLM-5V-Turbo and 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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GLM-5V-Turbo and Muse Glimmer 30B are 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
GLM-5V-Turbo 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. GLM-5V-Turbo does not fit this workload in one request. Muse Glimmer 30B does not fit this workload in one request. GLM-5V-Turbo has no published cached-input rate, so cached tokens use its listed input rate. Muse Glimmer 30B has no comparable published API token rate.
Confidence: rate-fallbackRepository 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.
34.4GLM-5V-Turbo36.3Muse Glimmer 30B
Directional only · BenchAlign v5.7
Muse Glimmer 30B 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.
2 GLM-5V-Turbo only
14 Muse Glimmer 30B 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.
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 v5.7 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.
Coding
Directional only- GLM-5V-Turbo
- 34.4
- Estimated · #76/135
- Muse Glimmer 30B
- 36.3
- Estimated · #69/135
- Basis
- BenchAlign v5.7 lane · 0 vs 4 public rows
- Reading
- Directional only
Knowledge
Directional only- GLM-5V-Turbo
- 43.5
- Estimated · #76/158
- Muse Glimmer 30B
- 43.9
- Estimated · #75/158
- Basis
- BenchAlign v5.7 lane · 0 vs 0 public rows
- Reading
- Directional only
Instruction following
Directional only- GLM-5V-Turbo
- 72.5
- #61/124
- Muse Glimmer 30B
- 77.7
- #55/124
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Directional only
Agentic
Not comparable- GLM-5V-Turbo
- Not ranked
- Muse Glimmer 30B
- 27.6
- Estimated · #72/105
- Basis
- BenchAlign v5.7 lane · 2 vs 4 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GLM-5V-Turbo
- 70.4
- Unranked · 2 rankable rows
- Muse Glimmer 30B
- 79.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5V-Turbo
- 67.3
- Unranked · 1 rankable row
- Muse Glimmer 30B
- 46.3
- #42/50
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5V-Turbo
- Not ranked
- Muse Glimmer 30B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GLM-5V-Turbo
- Not ranked
- Muse Glimmer 30B
- 75.4
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
| Category | GLM-5V-Turbo | Muse Glimmer 30B | Basis | Reading |
|---|---|---|---|---|
| Coding | 34.4Estimated · #76/135 | 36.3Estimated · #69/135 | Directional onlyBenchAlign v5.7 lane · 0 vs 4 public rows | Directional only |
| Knowledge | 43.5Estimated · #76/158 | 43.9Estimated · #75/158 | Directional onlyBenchAlign v5.7 lane · 0 vs 0 public rows | Directional only |
| Instruction following | 72.5#61/124 | 77.7#55/124 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Agentic | Not ranked | 27.6Estimated · #72/105 | Not comparableBenchAlign v5.7 lane · 2 vs 4 public rows | Not comparable |
| Reasoning | 70.4Unranked · 2 rankable rows | 79.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 67.3Unranked · 1 rankable row | 46.3#42/50 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 75.4Unranked · 1 rankable row | Not comparableProvisional lane · 0 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.
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
- GLM-5V-Turbo
- $0.0032
- 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
- GLM-5V-Turbo
- $0.072
- 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
- GLM-5V-Turbo
- $0.304
- Does not fit in one request
- Cached input priced at the published list-input rate
- Muse Glimmer 30B
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
GLM-5V-Turbo does not fit this workload in one request. Muse Glimmer 30B does not fit this workload in one request. GLM-5V-Turbo has no published cached-input rate, so cached tokens use its listed input rate. 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.
SpecificationGLM-5V-TurboMuse Glimmer 30B
Context window
Maximum documented context; output-token limits may be lower.
GLM-5V-Turbo
200K
Muse Glimmer 30B
131K
API model ID
GLM-5V-Turbo
Not sourced
Muse Glimmer 30B
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5V-Turbo
Not published
Muse Glimmer 30B
No comparable hosted API rate
Documented inputs
GLM-5V-Turbo
Not sourced
Muse Glimmer 30B
Not sourced
Documented outputs
GLM-5V-Turbo
Not sourced
Muse Glimmer 30B
Not sourced
Provider availability
GLM-5V-Turbo
Not sourced
Muse Glimmer 30B
Not sourced
Reasoning profile
GLM-5V-Turbo
Non-Reasoning
Muse Glimmer 30B
Reasoning
Weight access
GLM-5V-Turbo
Proprietary
Muse Glimmer 30B
Open Weight
License
GLM-5V-Turbo
Proprietary
Muse Glimmer 30B
Open Weight
Release date
GLM-5V-Turbo
2026-03-01
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
- 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
- GLM-5V-Turbo has the larger documented window (200K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GLM-5V-Turbo or Muse Glimmer 30B?
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, GLM-5V-Turbo or Muse Glimmer 30B?
Muse Glimmer 30B scores higher for coding on the public lane, 36.3 to 34.4. GLM-5V-Turbo and Muse Glimmer 30B are 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, GLM-5V-Turbo or Muse Glimmer 30B?
GLM-5V-Turbo is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GLM-5V-Turbo 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, GLM-5V-Turbo or Muse Glimmer 30B?
GLM-5V-Turbo has the larger documented context window: 200K, 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 evidence16 rows
Agentic
Claw-Eval
GLM-5V-Turbo53.8%SourceMuse Glimmer 30B—Not directly comparable
Gert Labs
GLM-5V-Turbo30.76%SourceMuse Glimmer 30B—Not directly comparable
MCP Atlas
GLM-5V-Turbo—Muse Glimmer 30B75.5%SourceNot directly comparable
DeepSearchQA
GLM-5V-Turbo—Muse Glimmer 30B74.6%SourceNot directly comparable
skillsBench
GLM-5V-Turbo—Muse Glimmer 30B44.3%SourceNot directly comparable
OSWorld-Verified
GLM-5V-Turbo—Muse Glimmer 30B65.9%SourceNot directly comparable
Coding
SWE-bench Pro
GLM-5V-Turbo—Muse Glimmer 30B51.2%SourceNot directly comparable
SWE-bench Verified
GLM-5V-Turbo—Muse Glimmer 30B76%SourceNot directly comparable
Terminal-Bench 2.1
GLM-5V-Turbo—Muse Glimmer 30B51.7%SourceNot directly comparable
SciCode
GLM-5V-Turbo—Muse Glimmer 30B43.6%SourceNot directly comparable
Multimodal
Instruction following
IFBench
GLM-5V-Turbo—Muse Glimmer 30B77%SourceNot directly comparable
Math
AIME26
GLM-5V-Turbo—Muse Glimmer 30B94.7%SourceNot directly comparable
16 public results · 0 shared
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Last updated September 24, 2026