Model comparison
GLM-4.7 vs Muse Spark 1.1
Head-to-head evidence from 15 shared benchmark results across 4 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Verified leaderboard positions: GLM-4.7 #32; Muse Spark 1.1 unranked
BenchAlign evidence: GLM-4.7 supported; Muse Spark 1.1 supported. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.7 and Muse Spark 1.1 share 15 comparable benchmark results. 3 of 8 categories are comparable. 16 results are unique to GLM-4.7; 24 to Muse Spark 1.1.
Updated July 15, 2026- Shared results
- 15
- GLM-4.7 only
- 16
- Muse Spark 1.1 only
- 24
- Comparable categories
- 3 / 8
Pick Muse Spark 1.1 if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if coding is the priority.
Confidence note. This is a partial-evidence comparison with 15 shared benchmark results across 4 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 provisional aggregate, 83 to 63. 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 45.7. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 41% to 80%. GLM-4.7 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 gives you the larger context window at 1M, compared with 200K for GLM-4.7.
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-4.7 | Δ | Muse Spark 1.1 |
|---|---|---|---|
| Agentic | GLM-4.745.7 | Margin→ 34.7 | Muse Spark 1.180.4 |
| Coding | GLM-4.775.4 | Margin← 13.9 | Muse Spark 1.161.5 |
| Knowledge | GLM-4.752.1 | Margin→ 10.0 | Muse Spark 1.162.1 |
| Math | GLM-4.71.8 | MarginNo overlap | Muse Spark 1.1Not measured |
| Multimodal | GLM-4.7Not measured | MarginNo overlap | Muse Spark 1.188.4 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 41%B 80%Winner: Muse Spark 1.1Δ 39Terminal-Bench 2.0: GLM-4.7 scored 41%; Muse Spark 1.1 scored 80%. Muse Spark 1.1 wins this benchmark. - Source ↗
HLE
KnowledgeA 24.8%B 62.1%Winner: Muse Spark 1.1Δ 37.3HLE: GLM-4.7 scored 24.8%; Muse Spark 1.1 scored 62.1%. 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-4.7 | Muse Spark 1.1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | Muse Spark 1.1Not available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-4.782 tok/s | Muse Spark 1.1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | Muse Spark 1.1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-4.7200K | Muse Spark 1.11M | Muse Spark 1.1 lists the larger context window. |
Benchmark Deep Dive
AgenticMuse Spark 1.1 wins24 benchmarks
| Benchmark | GLM-4.7 | Muse Spark 1.1 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 41% | 80% | Muse Spark 1.1 leads |
| BrowseCompSource | 52% | — | Not comparable |
| VITA-BenchSource | 15.5% | — | Not comparable |
| AA Agentic IndexSource | 25.4% | 37.5% | Muse Spark 1.1 leads |
| τ²-bench resultsSource | 95.9% | — | Not comparable |
| Gert LabsSource | 39.95% | — | Not comparable |
| GDPval-AASource | 33.3% | 43.7% | Muse Spark 1.1 leads |
| GDPval-AASource | 1165 | 1374 | Muse Spark 1.1 leads |
| MCP AtlasSource | — | 88.1% | Not comparable |
| ToolathlonSource | — | 75.6% | Not comparable |
| OSWorld-VerifiedSource | — | 80.8% | Not comparable |
| WebArena-VerifiedSource | — | 69% | Not comparable |
| DeepSearchQASource | — | 84.9% | Not comparable |
| CyberGymSource | — | 59.0% | 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 BriefcaseSource | — | 863 | Not comparable |
| AA AutomationBenchSource | — | 42.8% | Not comparable |
| AA Harvey LABSource | — | 8.3% | Not comparable |
| AA Tau3 BankingSource | — | 25.2% | Not comparable |
CodingGLM-4.7 wins10 benchmarks
| Benchmark | GLM-4.7 | Muse Spark 1.1 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.8% | — | Not comparable |
| LiveCodeBenchSource | 84.9% | — | Not comparable |
| SWE-RebenchSource | 58.7% | — | Not comparable |
| AA Coding IndexSource | 45.3% | 71.3% | Muse Spark 1.1 leads |
| Terminal-Bench HardSource | 31.8% | — | Not comparable |
| AA-SciCodeSource | 45.1% | 58.2% | Muse Spark 1.1 leads |
| AA LiveCodeBenchSource | 89.4% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 80.0% | Not comparable |
| SWE-bench ProSource | — | 61.5% | Not comparable |
| AA Terminal-Bench 2.1Source | — | 77.9% | Not comparable |
Reasoning3 benchmarks
KnowledgeMuse Spark 1.1 wins11 benchmarks
| Benchmark | GLM-4.7 | Muse Spark 1.1 | Result |
|---|---|---|---|
| GPQASource | 85.7% | — | Not comparable |
| MMLU-ProSource | 84.3% | — | Not comparable |
| HLESource | 24.8% | 62.1% | Muse Spark 1.1 leads |
| Artificial Analysis Intelligence IndexSource | 33.7% | 50.6% | Muse Spark 1.1 leads |
| AA-GPQA DiamondSource | 85.9% | 89.8% | Muse Spark 1.1 leads |
| AA-HLESource | 25.1% | 45.1% | Muse Spark 1.1 leads |
| AA-Omniscience IndexSource | -34.6% | 18.0% | Muse Spark 1.1 leads |
| AA-Omniscience AccuracySource | 29.3% | 40.6% | Muse Spark 1.1 leads |
| AA-Omniscience Hallucination RateSource | 90.3% | 38.1% | Muse Spark 1.1 leads |
| HLE w/o toolsSource | — | 52.2% | Not comparable |
| HealthBench ProfessionalSource | — | 59.3% | Not comparable |
Math3 benchmarks
Multimodal3 benchmarks
Inst. Following1 benchmarks
| Benchmark | GLM-4.7 | Muse Spark 1.1 | Result |
|---|---|---|---|
| AA-IFBenchSource | 67.9% | — | Not comparable |
Frequently Asked Questions (4)
Which is better, GLM-4.7 or Muse Spark 1.1?
Muse Spark 1.1 is ahead on BenchLM's provisional leaderboard, 83 to 63. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 41% and 80%.
Which is better for knowledge tasks, GLM-4.7 or Muse Spark 1.1?
Muse Spark 1.1 has the edge for knowledge tasks in this comparison, averaging 62.1 versus 52.1. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-4.7 or Muse Spark 1.1?
GLM-4.7 has the edge for coding in this comparison, averaging 75.4 versus 61.5. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-4.7 or Muse Spark 1.1?
Muse Spark 1.1 has the edge for agentic tasks in this comparison, averaging 80.4 versus 45.7. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
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