Updated October 13, 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
Muse Glimmer 30B has the higher public point estimate, 41.3 versus 37.8. Their conditional score ranges overlap. These ranges do not establish rank confidence. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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.
Code generation, repair, and software-engineering tasks
Not enough matched evidence
LFM2.5-24B-A2B and Muse Glimmer 30B are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
LFM2.5-24B-A2B and Muse Glimmer 30B are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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. LFM2.5-24B-A2B does not fit this 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.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.
Directional only · BenchAlign v5.8
Muse Glimmer 30B 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.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
SWE-bench VerifiedCoding
Normalized gap 25.0SciCodeCoding
Normalized gap 10.9IFBenchInstruction following
Normalized gap 2.3Each 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.
| Category | LFM2.5-24B-A2B | Muse Glimmer 30B | Basis | Reading |
|---|---|---|---|---|
| Instruction following | 75.6#61/128 | 81.1#54/128 | Like-for-likeProvisional lane · 1 vs 1 weighted rows | Muse Glimmer 30B leads |
| Agentic | 30.2Estimated · #88/124 | 24.5Estimated · #99/124 | Directional onlyBenchAlign v5.8 lane · 5 vs 4 public rows | Directional only |
| Coding | 28.5Estimated · #98/149 | 30.9Estimated · #92/149 | Directional onlyBenchAlign v5.8 lane · 2 vs 4 public rows | Directional only |
| Knowledge | 40.8Estimated · #112/181 | 44.1Supported · #101/181 | Directional onlyBenchAlign v5.8 lane · 3 vs 0 public rows | Directional only |
| Reasoning | Not ranked | 83.1Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 65.1#41/54 | 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 | 84.6Unranked · 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 v5.8) 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.
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.
1K fresh input + 500 output tokens
Muse Glimmer 30B has no comparable published API token rate.
50K fresh input + 3K output tokens
Muse Glimmer 30B has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
LFM2.5-24B-A2B does not fit this 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.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
LFM2.5-24B-A2B
Muse Glimmer 30B
131K
LFM2.5-24B-A2B
Not sourced
Muse Glimmer 30B
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
LFM2.5-24B-A2B
$0.05 per 1M cached input tokens
Liquid AI launch postMuse Glimmer 30B
No comparable hosted API rate
LFM2.5-24B-A2B
Not sourced
Muse Glimmer 30B
Not sourced
LFM2.5-24B-A2B
Not sourced
Muse Glimmer 30B
Not sourced
LFM2.5-24B-A2B
Not sourced
Muse Glimmer 30B
Not sourced
LFM2.5-24B-A2B
Reasoning
Muse Glimmer 30B
Reasoning
LFM2.5-24B-A2B
Open Weight
Muse Glimmer 30B
Open Weight
LFM2.5-24B-A2B
Open Weight
Muse Glimmer 30B
Open Weight
LFM2.5-24B-A2B
2026-10-13
Muse Glimmer 30B
2026-08-10
Muse Glimmer 30B has the higher public point estimate, 41.3 versus 37.8. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Muse Glimmer 30B scores higher for coding on the public lane, 30.9 to 28.5. LFM2.5-24B-A2B 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.
LFM2.5-24B-A2B scores higher for agentic tasks on the public lane, 30.2 to 24.5. LFM2.5-24B-A2B and Muse Glimmer 30B are 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.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Both models list the same context window, 131K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
AutomationBench
Not directly comparable
BFCL v4
Not directly comparable
τ³-bench results
Not directly comparable
PinchBench
Not directly comparable
WebArena-Verified
Not directly comparable
MCP Atlas
Not directly comparable
DeepSearchQA
Not directly comparable
skillsBench
Not directly comparable
OSWorld-Verified
Not directly comparable
SciCode
Muse Glimmer 30B leads this result
SWE-bench Verified
Muse Glimmer 30B leads this result
SWE-bench Pro
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
CharXiv
Not directly comparable
ScreenSpot Pro
Not directly comparable
OmniDocBench 1.5
Not directly comparable
MMMU-Pro
Not directly comparable
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Last updated October 13, 2026
