Model comparison
GLM-5 vs Qwen3.7 Plus
Head-to-head evidence from 30 shared benchmark results across 8 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5 #28 (Supported); Qwen3.7 Plus #21 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and Qwen3.7 Plus share 30 comparable benchmark results. 7 of 8 categories are comparable. 19 results are unique to GLM-5; 39 to Qwen3.7 Plus.
Updated July 23, 2026- Shared results
- 30
- GLM-5 only
- 19
- Qwen3.7 Plus only
- 39
- Comparable categories
- 7 / 8
Pick Qwen3.7 Plus if you want the stronger benchmark profile. GLM-5 only becomes the better choice if instruction following 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 30 shared benchmark results across 8 evidence categories; 7 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Qwen3.7 Plus has the cleaner BenchAlign overall profile here, landing at 67.22 versus 66.06. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Qwen3.7 Plus's sharpest advantage is in mathematics, where it averages 92.9 against 56.3. The single biggest benchmark swing on the page is HLE, 50.4% to 34.7%. GLM-5 does hit back in instruction following, so the answer changes if that is the part of the workload you care about most.
Qwen3.7 Plus 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. Qwen3.7 Plus 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 | Qwen3.7 Plus |
|---|---|---|---|
| Math | GLM-556.3 | Margin→ 36.6 | Qwen3.7 Plus92.9 |
| Reasoning | GLM-560.8 | Margin→ 30.9 | Qwen3.7 Plus91.7 |
| Agentic | GLM-556.2 | Margin→ 15.5 | Qwen3.7 Plus71.7 |
| Coding | GLM-566.3 | Margin→ 9.3 | Qwen3.7 Plus75.6 |
| Inst. Following | GLM-592.6 | Margin← 8.1 | Qwen3.7 Plus84.5 |
| Knowledge | GLM-566.4 | Margin← 6.3 | Qwen3.7 Plus60.1 |
| Multilingual | GLM-583.1 | Margin→ 2.3 | Qwen3.7 Plus85.4 |
| Multimodal | GLM-5Not measured | MarginNo overlap | Qwen3.7 Plus81.5 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
HLE
KnowledgeA 50.4%B 34.7%Winner: GLM-5Δ 15.7HLE: GLM-5 scored 50.4%; Qwen3.7 Plus scored 34.7%. GLM-5 wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 56.2%B 70.3%Winner: Qwen3.7 PlusΔ 14.1Terminal-Bench 2.0: GLM-5 scored 56.2%; Qwen3.7 Plus scored 70.3%. Qwen3.7 Plus wins this benchmark. - Source ↗
HMMT Feb 2026
MathA 86.4%B 92.9%Winner: Qwen3.7 PlusΔ 6.5HMMT Feb 2026: GLM-5 scored 86.4%; Qwen3.7 Plus scored 92.9%. Qwen3.7 Plus wins this benchmark. - Source ↗
SuperGPQA
KnowledgeA 66.8%B 71.4%Winner: Qwen3.7 PlusΔ 4.6SuperGPQA: GLM-5 scored 66.8%; Qwen3.7 Plus scored 71.4%. Qwen3.7 Plus wins this benchmark. - Source ↗
GPQA
KnowledgeA 86%B 90.3%Winner: Qwen3.7 PlusΔ 4.3GPQA: GLM-5 scored 86%; Qwen3.7 Plus scored 90.3%. Qwen3.7 Plus wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | Qwen3.7 Plus | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | Qwen3.7 PlusNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-574 tok/s | Qwen3.7 PlusNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | Qwen3.7 PlusNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | Qwen3.7 Plus1M | Qwen3.7 Plus lists the larger context window. |
Benchmark Deep Dive
AgenticQwen3.7 Plus wins22 benchmarks
| Benchmark | GLM-5 | Qwen3.7 Plus | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.2% | 70.3% | Qwen3.7 Plus leads |
| Claw-EvalSource | 57.7% | 62.7% | Qwen3.7 Plus leads |
| QwenClawBenchSource | 54.1% | 61.8% | Qwen3.7 Plus leads |
| τ³-bench resultsSource | 65.6% | — | Not comparable |
| DeepPlanningSource | 14.6% | 62.3% | Qwen3.7 Plus leads |
| ToolathlonSource | 38% | — | Not comparable |
| MCP AtlasSource | 31.1% | 73.2% | Qwen3.7 Plus leads |
| MCP-TasksSource | 60.8% | — | Not comparable |
| WideResearchSource | 69.8% | — | Not comparable |
| τ²-bench resultsSource | 98.2% | 93% | GLM-5 leads |
| CyberGymSource | 43.2% | — | Not comparable |
| APEX-Agents-AASource | 14.5% | 22.4% | Qwen3.7 Plus leads |
| Gert LabsSource | 50.99% | — | Not comparable |
| QwenWebBenchSource | — | 1536 | Not comparable |
| BFCL v4Source | — | 72.9% | Not comparable |
| VITA-BenchSource | — | 45.6% | Not comparable |
| OSWorld-VerifiedSource | — | 73.3% | Not comparable |
| AndroidWorldSource | — | 81.0% | Not comparable |
| AA Agentic IndexSource | — | 20.8% | Not comparable |
| GDPval-AASource | — | 21.8% | Not comparable |
| GDPval-AASource | — | 936 | Not comparable |
| OSWorld 2.0Source | — | 2.8% | Not comparable |
CodingQwen3.7 Plus wins12 benchmarks
| Benchmark | GLM-5 | Qwen3.7 Plus | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 77.8% | 77.7% | GLM-5 leads |
| SWE-bench Verified*Source | 72.8% | — | Not comparable |
| SWE-bench ProSource | 55.1% | 57.6% | Qwen3.7 Plus leads |
| SWE MultilingualSource | 73.3% | 75.8% | Qwen3.7 Plus leads |
| SWE-RebenchSource | 62.8% | — | Not comparable |
| React Native EvalsSource | 74.8% | — | Not comparable |
| AA-SciCodeSource | 46.2% | 45.5% | GLM-5 leads |
| Terminal-Bench 2.0Source | — | 70.3% | Not comparable |
| NL2RepoSource | — | 41.1% | Not comparable |
| SciCodeSource | — | 51.3% | Not comparable |
| LiveCodeBenchSource | — | 89.6% | Not comparable |
| AA Coding IndexSource | — | 55.9% | Not comparable |
ReasoningQwen3.7 Plus wins5 benchmarks
KnowledgeGLM-5 wins14 benchmarks
| Benchmark | GLM-5 | Qwen3.7 Plus | Result |
|---|---|---|---|
| GPQASource | 86% | 90.3% | Qwen3.7 Plus leads |
| GPQA-DSource | 86.0% | 90.3% | Qwen3.7 Plus leads |
| SuperGPQASource | 66.8% | 71.4% | Qwen3.7 Plus leads |
| MMLU-ProSource | 85.7% | 88.5% | Qwen3.7 Plus leads |
| MMLU-Pro (Arcee)Source | 85.8% | — | Not comparable |
| HLESource | 50.4% | 34.7% | GLM-5 leads |
| Artificial Analysis Intelligence IndexSource | 39.5% | 39.0% | GLM-5 leads |
| AA-GPQA DiamondSource | 82.0% | 90.0% | Qwen3.7 Plus leads |
| AA-HLESource | 27.2% | 33.4% | Qwen3.7 Plus leads |
| AA-Omniscience IndexSource | 2.0% | 2.4% | Qwen3.7 Plus leads |
| AA-Omniscience AccuracySource | 26.9% | 22.2% | GLM-5 leads |
| AA-Omniscience Hallucination RateSource | 34.0% | 25.5% | Qwen3.7 Plus leads |
| MMLU-ReduxSource | — | 94.5% | Not comparable |
| MMMLUSource | — | 89.0% | Not comparable |
MathQwen3.7 Plus wins10 benchmarks
| Benchmark | GLM-5 | Qwen3.7 Plus | 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% | 92.9% | Qwen3.7 Plus leads |
| MMAnswerBenchSource | 82.5% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 16.434% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 2.100% | — | Not comparable |
| IMOAnswerBenchSource | — | 86.0% | Not comparable |
| ApexSource | — | 22.7% | Not comparable |
MultilingualQwen3.7 Plus wins5 benchmarks
Multimodal17 benchmarks
| Benchmark | GLM-5 | Qwen3.7 Plus | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1278 | 1288 | Qwen3.7 Plus leads |
| MMMU-ProSource | — | 79% | Not comparable |
| MathVisionSource | — | 90.3% | Not comparable |
| CharXivSource | — | 85.9% | Not comparable |
| ERQASource | — | 69.8% | Not comparable |
| MedXpertQA (MM)Source | — | 71.0% | Not comparable |
| ScreenSpot ProSource | — | 79.0% | Not comparable |
| SimpleVQASource | — | 81.7% | Not comparable |
| MMSearch-PlusSource | — | 41.4% | Not comparable |
| RealWorldQASource | — | 86.9% | Not comparable |
| OmniDocBench 1.5Source | — | 91.4% | Not comparable |
| OCRBench V2Source | — | 70.7% | Not comparable |
| ODINW13Source | — | 51.1% | Not comparable |
| Video-MME (with subtitle)Source | — | 88.0% | Not comparable |
| VideoMMMUSource | — | 85.4% | Not comparable |
| MLVU (M-Avg)Source | — | 87.4% | Not comparable |
| AA-MMMU-ProSource | — | 80.5% | Not comparable |
Frequently Asked Questions (8)
Which is better, GLM-5 or Qwen3.7 Plus?
Qwen3.7 Plus is ahead on BenchLM's BenchAlign leaderboard, 67.22 to 66.06. The biggest single separator in this matchup is HLE, where the scores are 50.4% and 34.7%.
Which is better for knowledge tasks, GLM-5 or Qwen3.7 Plus?
GLM-5 has the edge for knowledge tasks in this comparison, averaging 66.4 versus 60.1. Inside this category, HLE is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-5 or Qwen3.7 Plus?
Qwen3.7 Plus has the edge for coding in this comparison, averaging 75.6 versus 66.3. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Which is better for math, GLM-5 or Qwen3.7 Plus?
Qwen3.7 Plus has the edge for math in this comparison, averaging 92.9 versus 56.3. Inside this category, HMMT Feb 2026 is the benchmark that creates the most daylight between them.
Which is better for reasoning, GLM-5 or Qwen3.7 Plus?
Qwen3.7 Plus has the edge for reasoning in this comparison, averaging 91.7 versus 60.8. Inside this category, CritPt is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-5 or Qwen3.7 Plus?
Qwen3.7 Plus has the edge for agentic tasks in this comparison, averaging 71.7 versus 56.2. Inside this category, DeepPlanning is the benchmark that creates the most daylight between them.
Which is better for instruction following, GLM-5 or Qwen3.7 Plus?
GLM-5 has the edge for instruction following in this comparison, averaging 92.6 versus 84.5. Inside this category, AA-IFBench is the benchmark that creates the most daylight between them.
Which is better for multilingual tasks, GLM-5 or Qwen3.7 Plus?
Qwen3.7 Plus has the edge for multilingual tasks in this comparison, averaging 85.4 versus 83.1. Inside this category, NOVA-63 is the benchmark that creates the most daylight between them.
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