Updated October 7, 2026. Rank says Muse Spark 1.1 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 Spark 1.1 has the higher public score estimate, 65.95 versus 56.13, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 9 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.
Code generation, repair, and software-engineering tasks
Muse Spark 1.1
Muse Spark 1.1 has the higher public coding point estimate, 54.2 to 49.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Muse Spark 1.1
Muse Spark 1.1 has the larger documented context window.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-5.1 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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. GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate. Muse Spark 1.1 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.
Like-for-like · BenchAlign v5.8
Muse Spark 1.1 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.
1 category rests 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.
HLEKnowledge
Normalized gap 9.8LiveCodeBench (Vals)Coding
Normalized gap 4.5SWE-bench ProCoding
Normalized gap 3.1MMLU-Pro (Vals)Knowledge
Normalized gap 1.8Each 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 | GLM-5.1 | Muse Spark 1.1 | Basis | Reading |
|---|---|---|---|---|
| Coding | 49.2Supported · #44/146 | 54.2Supported · #35/146 | Like-for-likeBenchAlign v5.8 lane · 7 vs 4 public rows | Muse Spark 1.1 leads · intervals overlap |
| Knowledge | 52.6Supported · #61/174 | 66.7Supported · #21/174 | Like-for-likeBenchAlign v5.8 lane · 4 vs 5 public rows | Muse Spark 1.1 leads · intervals overlap |
| Agentic | 43.2Estimated · #58/122 | 56.9Supported · #32/122 | Directional onlyBenchAlign v5.8 lane · 9 vs 14 public rows | Directional only |
| Reasoning | 73.0Unranked · 2 rankable rows | 75.7Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 78.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 92.4#4/125 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 63.8#3/7 | Not ranked | Not comparableProvisional lane · 4 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.
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 Spark 1.1 has no comparable published API token rate.
50K fresh input + 3K output tokens
Muse Spark 1.1 has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate. Muse Spark 1.1 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.
GLM-5.1
203K
Muse Spark 1.1
1M
GLM-5.1
Not sourced
Muse Spark 1.1
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5.1
Not published
Muse Spark 1.1
No comparable hosted API rate
GLM-5.1
Not sourced
Muse Spark 1.1
Not sourced
GLM-5.1
Not sourced
Muse Spark 1.1
Not sourced
GLM-5.1
Not sourced
Muse Spark 1.1
Not sourced
GLM-5.1
Reasoning
Muse Spark 1.1
Reasoning
GLM-5.1
Open Weight
Muse Spark 1.1
Proprietary
GLM-5.1
Open Weight
Muse Spark 1.1
Proprietary
GLM-5.1
2026-04-07
Muse Spark 1.1
2026-07-09
Muse Spark 1.1 has the higher public score estimate, 65.95 versus 56.13, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Muse Spark 1.1 has the higher public coding point estimate, 54.2 to 49.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Muse Spark 1.1 scores higher for agentic tasks on the public lane, 56.9 to 43.2. GLM-5.1 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.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Muse Spark 1.1 has the larger documented context window: 1M, compared with 203K.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Not directly comparable
BrowseComp
Not directly comparable
τ³-bench results
Not directly comparable
MCP Atlas
Muse Spark 1.1 leads this result
CyberGym
GLM-5.1 leads this result
Claw-Eval
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
Muse Spark 1.1 leads this result
Terminal-Bench 2.1
Not directly comparable
Toolathlon
Not directly comparable
OSWorld-Verified
Not directly comparable
WebArena-Verified
Not directly comparable
DeepSearchQA
Not directly comparable
Finance Agent v2
Not directly comparable
deepSwe
Not directly comparable
OSWorld 2.0
Not directly comparable
JobBench
Not directly comparable
Cybench
Not directly comparable
ExploitGym
Not directly comparable
SWE-bench Pro
Muse Spark 1.1 leads this result
NL2Repo
Not directly comparable
SWE-Rebench
Not directly comparable
Vibe Code Bench
Not directly comparable
OpenHarmony Bench
Not directly comparable
LiveCodeBench (Vals)
Muse Spark 1.1 leads this result
SWE-bench (Vals)
Muse Spark 1.1 leads this result
Terminal-Bench 2.1
Not directly comparable
MRCR 1M
Not directly comparable
GPQA-D
Not directly comparable
HLE
Muse Spark 1.1 leads this result
GPQA Diamond (Vals)
Muse Spark 1.1 leads this result
MMLU-Pro (Vals)
Muse Spark 1.1 leads this result
HLE w/o tools
Not directly comparable
HealthBench Professional
Not directly comparable
AIME26
Not directly comparable
HMMT Nov 2025
Not directly comparable
HMMT Feb 2026
Not directly comparable
MMAnswerBench
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
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Last updated October 7, 2026
