Model comparison
GLM-5.1 vs Muse Spark
Head-to-head evidence from 24 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5.1 #18 (Supported); Muse Spark #13 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5.1 and Muse Spark share 24 comparable benchmark results. 4 of 8 categories are comparable. 12 results are unique to GLM-5.1; 15 to Muse Spark.
Updated July 23, 2026- Shared results
- 24
- GLM-5.1 only
- 12
- Muse Spark only
- 15
- Comparable categories
- 4 / 8
Pick Muse Spark if you want the stronger benchmark profile. GLM-5.1 only becomes the better choice if mathematics is the priority.
Confidence note. This is a partial-evidence comparison with 24 shared benchmark results across 6 evidence categories; 4 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 is clearly ahead on the BenchAlign aggregate, 71.04 to 67.74. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Muse Spark's sharpest advantage is in coding, where it averages 67.8 against 61.3. The single biggest benchmark swing on the page is SWE-bench Pro, 58.4% to 52.4%. GLM-5.1 does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
Muse Spark gives you the larger context window at 262K, compared with 203K for GLM-5.1.
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-5.1 | Δ | Muse Spark |
|---|---|---|---|
| Math | GLM-5.162.0 | Margin← 29.1 | Muse Spark32.9 |
| Coding | GLM-5.161.3 | Margin→ 6.5 | Muse Spark67.8 |
| Agentic | GLM-5.165.4 | Margin← 6.4 | Muse Spark59.0 |
| Knowledge | GLM-5.152.3 | Margin← 1.9 | Muse Spark50.4 |
| Reasoning | GLM-5.1Not measured | MarginNo overlap | Muse Spark42.5 |
| Multimodal | GLM-5.1Not measured | MarginNo overlap | Muse Spark82.5 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Pro
CodingA 58.4%B 52.4%Winner: GLM-5.1Δ 6SWE-bench Pro: GLM-5.1 scored 58.4%; Muse Spark scored 52.4%. GLM-5.1 wins this benchmark. - Source ↗
FrontierMath v2 (Tiers 1-3)
MathA 33.448%B 39.000%Winner: Muse SparkΔ 5.6FrontierMath v2 (Tiers 1-3): GLM-5.1 scored 33.448%; Muse Spark scored 39.000%. Muse Spark wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 63.5%B 59%Winner: GLM-5.1Δ 4.5Terminal-Bench 2.0: GLM-5.1 scored 63.5%; Muse Spark scored 59%. GLM-5.1 wins this benchmark. - Source ↗
FrontierMath v2 (Tier 4)
MathA 12.500%B 14.600%Winner: Muse SparkΔ 2.1FrontierMath v2 (Tier 4): GLM-5.1 scored 12.500%; Muse Spark scored 14.600%. Muse Spark wins this benchmark. - Source ↗
HLE
KnowledgeA 52.3%B 50.4%Winner: GLM-5.1Δ 1.9HLE: GLM-5.1 scored 52.3%; Muse Spark scored 50.4%. GLM-5.1 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5.1 | Muse Spark | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5.1$1.4 input / $4.4 output | Muse SparkNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-5.1Not available | Muse SparkNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-5.1Not available | Muse SparkNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5.1203K | Muse Spark262K | Muse Spark lists the larger context window. |
Benchmark Deep Dive
AgenticGLM-5.1 wins13 benchmarks
| Benchmark | GLM-5.1 | Muse Spark | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 63.5% | 59% | GLM-5.1 leads |
| BrowseCompSource | 68% | — | Not comparable |
| τ³-bench resultsSource | 70.6% | — | Not comparable |
| MCP AtlasSource | 71.8% | — | Not comparable |
| CyberGymSource | 68.7% | 43.5% | GLM-5.1 leads |
| Claw-EvalSource | 62.3% | 63.8% | Muse Spark leads |
| AA Agentic IndexSource | 29.9% | 28.7% | GLM-5.1 leads |
| τ²-bench resultsSource | 97.7% | 91.5% | GLM-5.1 leads |
| GDPval-AASource | 37.8% | 32.2% | GLM-5.1 leads |
| Gert LabsSource | 60.11% | — | Not comparable |
| GDPval-AASource | 1257 | 1144 | GLM-5.1 leads |
| ResearchClawBenchSource | 18.2% | — | Not comparable |
| DeepSearchQASource | — | 74.8% | Not comparable |
CodingMuse Spark wins8 benchmarks
| Benchmark | GLM-5.1 | Muse Spark | Result |
|---|---|---|---|
| SWE-bench ProSource | 58.4% | 52.4% | GLM-5.1 leads |
| NL2RepoSource | 42.7% | — | Not comparable |
| SWE-RebenchSource | 62.7% | — | Not comparable |
| Vibe Code BenchSource | 31.46% | 19.67% | GLM-5.1 leads |
| AA Coding IndexSource | 55.8% | 58.6% | Muse Spark leads |
| AA-SciCodeSource | 43.8% | 51.5% | Muse Spark leads |
| SWE-bench VerifiedSource | — | 77.4% | Not comparable |
| LiveCodeBench ProSource | — | 80.0% | Not comparable |
Reasoning3 benchmarks
KnowledgeGLM-5.1 wins11 benchmarks
| Benchmark | GLM-5.1 | Muse Spark | Result |
|---|---|---|---|
| GPQA-DSource | 86.2% | 89.5% | Muse Spark leads |
| HLESource | 52.3% | 50.4% | GLM-5.1 leads |
| Artificial Analysis Intelligence IndexSource | 40.2% | 43.1% | Muse Spark leads |
| AA-GPQA DiamondSource | 86.8% | 88.4% | Muse Spark leads |
| AA-HLESource | 28.0% | 39.9% | Muse Spark leads |
| AA-Omniscience IndexSource | 1.9% | 4.1% | Muse Spark leads |
| AA-Omniscience AccuracySource | 24.2% | 44.6% | Muse Spark leads |
| AA-Omniscience Hallucination RateSource | 29.4% | 73.2% | GLM-5.1 leads |
| HLE w/o toolsSource | — | 42.8% | Not comparable |
| HealthBench HardSource | — | 42.8% | Not comparable |
| MedXpertQA (Text)Source | — | 52.6% | Not comparable |
MathGLM-5.1 wins6 benchmarks
| Benchmark | GLM-5.1 | Muse Spark | Result |
|---|---|---|---|
| AIME26Source | 95.3% | — | Not comparable |
| HMMT Nov 2025Source | 94.0% | — | Not comparable |
| HMMT Feb 2026Source | 82.6% | — | Not comparable |
| MMAnswerBenchSource | 83.8% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 33.448% | 39.000% | Muse Spark leads |
| FrontierMath v2 (Tier 4)Source | 12.500% | 14.600% | Muse Spark leads |
Multimodal9 benchmarks
| Benchmark | GLM-5.1 | Muse Spark | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1305 | — | Not comparable |
| CharXivSource | — | 86.4% | Not comparable |
| MMMU-ProSource | — | 80.4% | Not comparable |
| ERQASource | — | 64.7% | Not comparable |
| SimpleVQASource | — | 71.3% | Not comparable |
| ScreenSpot ProSource | — | 84.1% | Not comparable |
| ZeroBenchSource | — | 33.0% | Not comparable |
| MedXpertQA (MM)Source | — | 78.4% | Not comparable |
| AA-MMMU-ProSource | — | 80.5% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GLM-5.1 | Muse Spark | Result |
|---|---|---|---|
| AA-IFBenchSource | 76.3% | 75.9% | GLM-5.1 leads |
Frequently Asked Questions (5)
Which is better, GLM-5.1 or Muse Spark?
Muse Spark is ahead on BenchLM's BenchAlign leaderboard, 71.04 to 67.74. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 58.4% and 52.4%.
Which is better for knowledge tasks, GLM-5.1 or Muse Spark?
GLM-5.1 has the edge for knowledge tasks in this comparison, averaging 52.3 versus 50.4. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-5.1 or Muse Spark?
Muse Spark has the edge for coding in this comparison, averaging 67.8 versus 61.3. Inside this category, Vibe Code Bench is the benchmark that creates the most daylight between them.
Which is better for math, GLM-5.1 or Muse Spark?
GLM-5.1 has the edge for math in this comparison, averaging 62 versus 32.9. Inside this category, FrontierMath v2 (Tiers 1-3) is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-5.1 or Muse Spark?
GLM-5.1 has the edge for agentic tasks in this comparison, averaging 65.4 versus 59. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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