Model comparison
GLM-5 vs Muse Spark 1.1
Head-to-head evidence from 16 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5 #28 (Supported); Muse Spark 1.1 #6 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and Muse Spark 1.1 share 16 comparable benchmark results. 3 of 8 categories are comparable. 33 results are unique to GLM-5; 23 to Muse Spark 1.1.
Updated July 18, 2026- Shared results
- 16
- GLM-5 only
- 33
- Muse Spark 1.1 only
- 23
- Comparable categories
- 3 / 8
Pick Muse Spark 1.1 if you want the stronger benchmark profile. GLM-5 only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 16 shared benchmark results across 5 evidence categories; 3 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Muse Spark 1.1 is clearly ahead on the BenchAlign aggregate, 77.44 to 66.06. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Muse Spark 1.1's sharpest advantage is in agentic, where it averages 80.4 against 56.2. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 56.2% to 80%. GLM-5 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Muse Spark 1.1 is the reasoning model in the pair, while GLM-5 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Muse Spark 1.1 gives you the larger context window at 1M, compared with 200K for GLM-5.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | GLM-5 | Δ | Muse Spark 1.1 |
|---|---|---|---|
| Agentic | GLM-556.2 | Margin→ 24.2 | Muse Spark 1.180.4 |
| Coding | GLM-566.3 | Margin← 4.8 | Muse Spark 1.161.5 |
| Knowledge | GLM-566.4 | Margin← 4.3 | Muse Spark 1.162.1 |
| Reasoning | GLM-560.8 | MarginNo overlap | Muse Spark 1.1Not measured |
| Math | GLM-556.3 | MarginNo overlap | Muse Spark 1.1Not measured |
| Multilingual | GLM-583.1 | MarginNo overlap | Muse Spark 1.1Not measured |
| Multimodal | GLM-5Not measured | MarginNo overlap | Muse Spark 1.188.4 |
| Inst. Following | GLM-592.6 | MarginNo overlap | Muse Spark 1.1Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 56.2%B 80%Winner: Muse Spark 1.1Δ 23.8Terminal-Bench 2.0: GLM-5 scored 56.2%; Muse Spark 1.1 scored 80%. Muse Spark 1.1 wins this benchmark. - Source ↗
HLE
KnowledgeA 50.4%B 62.1%Winner: Muse Spark 1.1Δ 11.7HLE: GLM-5 scored 50.4%; Muse Spark 1.1 scored 62.1%. Muse Spark 1.1 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 55.1%B 61.5%Winner: Muse Spark 1.1Δ 6.4SWE-bench Pro: GLM-5 scored 55.1%; Muse Spark 1.1 scored 61.5%. Muse Spark 1.1 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | Muse Spark 1.1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | Muse Spark 1.1Not available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-574 tok/s | Muse Spark 1.1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | Muse Spark 1.1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | Muse Spark 1.11M | Muse Spark 1.1 lists the larger context window. |
Benchmark Deep Dive
AgenticMuse Spark 1.1 wins29 benchmarks
| Benchmark | GLM-5 | Muse Spark 1.1 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.2% | 80% | Muse Spark 1.1 leads |
| Claw-EvalSource | 57.7% | — | Not comparable |
| QwenClawBenchSource | 54.1% | — | Not comparable |
| τ³-bench resultsSource | 65.6% | — | Not comparable |
| DeepPlanningSource | 14.6% | — | Not comparable |
| ToolathlonSource | 38% | 75.6% | Muse Spark 1.1 leads |
| MCP AtlasSource | 31.1% | 88.1% | Muse Spark 1.1 leads |
| MCP-TasksSource | 60.8% | — | Not comparable |
| WideResearchSource | 69.8% | — | Not comparable |
| τ²-bench resultsSource | 98.2% | — | Not comparable |
| CyberGymSource | 43.2% | 59.0% | Muse Spark 1.1 leads |
| APEX-Agents-AASource | 14.5% | — | Not comparable |
| Gert LabsSource | 50.99% | — | Not comparable |
| OSWorld-VerifiedSource | — | 80.8% | Not comparable |
| DeepSearchQASource | — | 84.9% | Not comparable |
| Finance Agent v2Source | — | 57.2% | Not comparable |
| deepSweSource | — | 53.3% | Not comparable |
| OSWorld 2.0Source | — | 14.2% | Not comparable |
| JobBenchSource | — | 54.7% | Not comparable |
| CybenchSource | — | 92.9% | Not comparable |
| ExploitGymSource | — | 0.8% | Not comparable |
| AA Agentic IndexSource | — | 37.5% | Not comparable |
| GDPval-AASource | — | 43.7% | Not comparable |
| GDPval-AASource | — | 1374 | Not comparable |
| AA BriefcaseSource | — | 863 | Not comparable |
| AA AutomationBenchSource | — | 42.8% | Not comparable |
| AA Harvey LABSource | — | 8.3% | Not comparable |
| AA Tau3 BankingSource | — | 25.2% | Not comparable |
| aaTerminalBench21Source | — | 77.9% | Not comparable |
CodingGLM-5 wins9 benchmarks
| Benchmark | GLM-5 | Muse Spark 1.1 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 77.8% | — | Not comparable |
| SWE-bench Verified*Source | 72.8% | — | Not comparable |
| SWE-bench ProSource | 55.1% | 61.5% | Muse Spark 1.1 leads |
| SWE MultilingualSource | 73.3% | — | Not comparable |
| SWE-RebenchSource | 62.8% | — | Not comparable |
| React Native EvalsSource | 74.8% | — | Not comparable |
| AA-SciCodeSource | 46.2% | 58.2% | Muse Spark 1.1 leads |
| Terminal-Bench 2.0Source | — | 80.0% | Not comparable |
| AA Coding IndexSource | — | 71.3% | Not comparable |
Reasoning5 benchmarks
KnowledgeGLM-5 wins14 benchmarks
| Benchmark | GLM-5 | Muse Spark 1.1 | Result |
|---|---|---|---|
| GPQASource | 86% | — | Not comparable |
| GPQA-DSource | 86.0% | — | Not comparable |
| SuperGPQASource | 66.8% | — | Not comparable |
| MMLU-ProSource | 85.7% | — | Not comparable |
| MMLU-Pro (Arcee)Source | 85.8% | — | Not comparable |
| HLESource | 50.4% | 62.1% | Muse Spark 1.1 leads |
| Artificial Analysis Intelligence IndexSource | 39.5% | 50.6% | Muse Spark 1.1 leads |
| AA-GPQA DiamondSource | 82.0% | 89.8% | Muse Spark 1.1 leads |
| AA-HLESource | 27.2% | 45.1% | Muse Spark 1.1 leads |
| AA-Omniscience IndexSource | 2.0% | 18.0% | Muse Spark 1.1 leads |
| AA-Omniscience AccuracySource | 26.9% | 40.6% | Muse Spark 1.1 leads |
| AA-Omniscience Hallucination RateSource | 34.0% | 38.1% | GLM-5 leads |
| HLE w/o toolsSource | — | 52.2% | Not comparable |
| HealthBench ProfessionalSource | — | 59.3% | Not comparable |
Math8 benchmarks
| Benchmark | GLM-5 | Muse Spark 1.1 | Result |
|---|---|---|---|
| AIME26Source | 95.8% | — | Not comparable |
| AIME25 (Arcee)Source | 93.3% | — | Not comparable |
| HMMT Feb 2025Source | 97.5% | — | Not comparable |
| HMMT Nov 2025Source | 96.9% | — | Not comparable |
| HMMT Feb 2026Source | 86.4% | — | Not comparable |
| MMAnswerBenchSource | 82.5% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 16.434% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 2.100% | — | Not comparable |
Multilingual2 benchmarks
Multimodal3 benchmarks
Frequently Asked Questions (4)
Which is better, GLM-5 or Muse Spark 1.1?
Muse Spark 1.1 is ahead on BenchLM's BenchAlign leaderboard, 77.44 to 66.06. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 56.2% and 80%.
Which is better for knowledge tasks, GLM-5 or Muse Spark 1.1?
GLM-5 has the edge for knowledge tasks in this comparison, averaging 66.4 versus 62.1. Inside this category, AA-HLE is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-5 or Muse Spark 1.1?
GLM-5 has the edge for coding in this comparison, averaging 66.3 versus 61.5. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-5 or Muse Spark 1.1?
Muse Spark 1.1 has the edge for agentic tasks in this comparison, averaging 80.4 versus 56.2. Inside this category, MCP Atlas is the benchmark that creates the most daylight between them.
Related Comparisons
Explore More
The AI models change fast. We track them for you.
A weekly brief for engineers and researchers covering new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.