Model comparison
GLM-4.7 vs Qwen3.6-35B-A3B
Head-to-head evidence from 24 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Verified leaderboard positions: GLM-4.7 #32; Qwen3.6-35B-A3B #31
BenchAlign evidence: GLM-4.7 supported; Qwen3.6-35B-A3B estimated. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.7 and Qwen3.6-35B-A3B share 24 comparable benchmark results. 4 of 8 categories are comparable. 7 results are unique to GLM-4.7; 34 to Qwen3.6-35B-A3B.
Updated July 15, 2026- Shared results
- 24
- GLM-4.7 only
- 7
- Qwen3.6-35B-A3B only
- 34
- Comparable categories
- 4 / 8
Pick GLM-4.7 if you want the stronger benchmark profile. Qwen3.6-35B-A3B only becomes the better choice if mathematics is the priority or you need the larger 262K context window.
Confidence note. This is a partial-evidence comparison with 24 shared benchmark results across 5 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
GLM-4.7 is clearly ahead on the provisional aggregate, 63 to 59. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7's sharpest advantage is in coding, where it averages 75.4 against 73.8. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 41% to 51.5%. Qwen3.6-35B-A3B does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
Qwen3.6-35B-A3B gives you the larger context window at 262K, 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 | Qwen3.6-35B-A3B |
|---|---|---|---|
| Math | GLM-4.71.8 | Margin→ 86.4 | Qwen3.6-35B-A3B88.2 |
| Agentic | GLM-4.745.7 | Margin→ 5.8 | Qwen3.6-35B-A3B51.5 |
| Coding | GLM-4.775.4 | Margin← 1.6 | Qwen3.6-35B-A3B73.8 |
| Knowledge | GLM-4.752.1 | Margin← 0.3 | Qwen3.6-35B-A3B51.8 |
| Multimodal | GLM-4.7Not measured | MarginNo overlap | Qwen3.6-35B-A3B76.3 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 41%B 51.5%Winner: Qwen3.6-35B-A3BΔ 10.5Terminal-Bench 2.0: GLM-4.7 scored 41%; Qwen3.6-35B-A3B scored 51.5%. Qwen3.6-35B-A3B wins this benchmark. - Source ↗
LiveCodeBench
CodingA 84.9%B 80.4%Winner: GLM-4.7Δ 4.5LiveCodeBench: GLM-4.7 scored 84.9%; Qwen3.6-35B-A3B scored 80.4%. GLM-4.7 wins this benchmark. - Source ↗
HLE
KnowledgeA 24.8%B 21.4%Winner: GLM-4.7Δ 3.4HLE: GLM-4.7 scored 24.8%; Qwen3.6-35B-A3B scored 21.4%. GLM-4.7 wins this benchmark. - Source ↗
MMLU-Pro
KnowledgeA 84.3%B 85.2%Winner: Qwen3.6-35B-A3BΔ 0.9MMLU-Pro: GLM-4.7 scored 84.3%; Qwen3.6-35B-A3B scored 85.2%. Qwen3.6-35B-A3B wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 73.8%B 73.4%Winner: GLM-4.7Δ 0.4SWE-bench Verified: GLM-4.7 scored 73.8%; Qwen3.6-35B-A3B scored 73.4%. GLM-4.7 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-4.7 | Qwen3.6-35B-A3B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | Qwen3.6-35B-A3BNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-4.782 tok/s | Qwen3.6-35B-A3BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | Qwen3.6-35B-A3BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-4.7200K | Qwen3.6-35B-A3B262K | Qwen3.6-35B-A3B lists the larger context window. |
Benchmark Deep Dive
AgenticQwen3.6-35B-A3B wins16 benchmarks
| Benchmark | GLM-4.7 | Qwen3.6-35B-A3B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 41% | 51.5% | Qwen3.6-35B-A3B leads |
| BrowseCompSource | 52% | — | Not comparable |
| VITA-BenchSource | 15.5% | 35.6% | Qwen3.6-35B-A3B leads |
| AA Agentic IndexSource | 25.4% | 21.4% | GLM-4.7 leads |
| τ²-bench resultsSource | 95.9% | 95.3% | GLM-4.7 leads |
| Gert LabsSource | 39.95% | 42.65% | Qwen3.6-35B-A3B leads |
| GDPval-AASource | 33.3% | 27.4% | GLM-4.7 leads |
| GDPval-AASource | 1165 | 1049 | GLM-4.7 leads |
| Claw-EvalSource | — | 68.7% | Not comparable |
| QwenClawBenchSource | — | 52.6% | Not comparable |
| QwenWebBenchSource | — | 1397 | Not comparable |
| τ³-bench resultsSource | — | 67.2% | Not comparable |
| DeepPlanningSource | — | 25.9% | Not comparable |
| ToolathlonSource | — | 26.9% | Not comparable |
| MCP AtlasSource | — | 62.8% | Not comparable |
| WideResearchSource | — | 60.1% | Not comparable |
CodingGLM-4.7 wins11 benchmarks
| Benchmark | GLM-4.7 | Qwen3.6-35B-A3B | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.8% | 73.4% | GLM-4.7 leads |
| LiveCodeBenchSource | 84.9% | 80.4% | GLM-4.7 leads |
| SWE-RebenchSource | 58.7% | — | Not comparable |
| AA Coding IndexSource | 45.3% | 41.9% | GLM-4.7 leads |
| Terminal-Bench HardSource | 31.8% | 34.8% | Qwen3.6-35B-A3B leads |
| AA-SciCodeSource | 45.1% | 35.8% | GLM-4.7 leads |
| AA LiveCodeBenchSource | 89.4% | — | Not comparable |
| SWE MultilingualSource | — | 67.2% | Not comparable |
| SWE-bench ProSource | — | 49.5% | Not comparable |
| Terminal-Bench 2.0Source | — | 51.5% | Not comparable |
| NL2RepoSource | — | 29.4% | Not comparable |
Reasoning2 benchmarks
KnowledgeGLM-4.7 wins11 benchmarks
| Benchmark | GLM-4.7 | Qwen3.6-35B-A3B | Result |
|---|---|---|---|
| GPQASource | 85.7% | 86% | Qwen3.6-35B-A3B leads |
| MMLU-ProSource | 84.3% | 85.2% | Qwen3.6-35B-A3B leads |
| HLESource | 24.8% | 21.4% | GLM-4.7 leads |
| Artificial Analysis Intelligence IndexSource | 33.7% | 31.6% | GLM-4.7 leads |
| AA-GPQA DiamondSource | 85.9% | 84.1% | GLM-4.7 leads |
| AA-HLESource | 25.1% | 20.2% | GLM-4.7 leads |
| AA-Omniscience IndexSource | -34.6% | -21.4% | Qwen3.6-35B-A3B leads |
| AA-Omniscience AccuracySource | 29.3% | 18.9% | GLM-4.7 leads |
| AA-Omniscience Hallucination RateSource | 90.3% | 49.7% | Qwen3.6-35B-A3B leads |
| SuperGPQASource | — | 64.7% | Not comparable |
| C-EvalSource | — | 90% | Not comparable |
MathQwen3.6-35B-A3B wins8 benchmarks
| Benchmark | GLM-4.7 | Qwen3.6-35B-A3B | Result |
|---|---|---|---|
| AIME 2025Source | 95.7% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 2.439% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 0.000% | — | Not comparable |
| HMMT Feb 2025Source | — | 90.7% | Not comparable |
| HMMT Nov 2025Source | — | 89.1% | Not comparable |
| HMMT Feb 2026Source | — | 83.6% | Not comparable |
| MMAnswerBenchSource | — | 78.9% | Not comparable |
| AIME26Source | — | 92.7% | Not comparable |
Multimodal16 benchmarks
| Benchmark | GLM-4.7 | Qwen3.6-35B-A3B | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1260 | — | Not comparable |
| MMMUSource | — | 81.7% | Not comparable |
| MMMU-ProSource | — | 75.3% | Not comparable |
| RealWorldQASource | — | 85.3% | Not comparable |
| OmniDocBench 1.5Source | — | 89.9% | Not comparable |
| CharXivSource | — | 78% | Not comparable |
| SimpleVQASource | — | 58.9% | Not comparable |
| CC-OCRSource | — | 81.9% | Not comparable |
| AI2D_TESTSource | — | 92.7% | Not comparable |
| RefCOCO (avg)Source | — | 92.0% | Not comparable |
| ODINW13Source | — | 50.8% | Not comparable |
| Video-MME (with subtitle)Source | — | 86.6% | Not comparable |
| Video-MME (w/o subtitle)Source | — | 82.5% | Not comparable |
| VideoMMMUSource | — | 83.7% | Not comparable |
| MLVU (M-Avg)Source | — | 86.2% | Not comparable |
| AA-MMMU-ProSource | — | 75.0% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GLM-4.7 | Qwen3.6-35B-A3B | Result |
|---|---|---|---|
| AA-IFBenchSource | 67.9% | 64.4% | GLM-4.7 leads |
Frequently Asked Questions (5)
Which is better, GLM-4.7 or Qwen3.6-35B-A3B?
GLM-4.7 is ahead on BenchLM's provisional leaderboard, 63 to 59. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 41% and 51.5%.
Which is better for knowledge tasks, GLM-4.7 or Qwen3.6-35B-A3B?
GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 52.1 versus 51.8. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-4.7 or Qwen3.6-35B-A3B?
GLM-4.7 has the edge for coding in this comparison, averaging 75.4 versus 73.8. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Which is better for math, GLM-4.7 or Qwen3.6-35B-A3B?
Qwen3.6-35B-A3B has the edge for math in this comparison, averaging 88.2 versus 1.8. GLM-4.7 stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, GLM-4.7 or Qwen3.6-35B-A3B?
Qwen3.6-35B-A3B has the edge for agentic tasks in this comparison, averaging 51.5 versus 45.7. Inside this category, GDPval-AA 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.