Model comparison
GLM-4.7 vs Qwen3.5-122B-A10B
Head-to-head evidence from 20 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-4.7 #42 (Supported); Qwen3.5-122B-A10B #47 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.7 and Qwen3.5-122B-A10B share 20 comparable benchmark results. 3 of 8 categories are comparable. 10 results are unique to GLM-4.7; 11 to Qwen3.5-122B-A10B.
Updated July 23, 2026- Shared results
- 20
- GLM-4.7 only
- 10
- Qwen3.5-122B-A10B only
- 11
- Comparable categories
- 3 / 8
Pick GLM-4.7 if you want the stronger benchmark profile. Qwen3.5-122B-A10B only becomes the better choice if knowledge is the priority or you need the larger 262K context window.
Confidence note. This is a partial-evidence comparison with 20 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
GLM-4.7 has the cleaner BenchAlign overall profile here, landing at 61.16 versus 60.56. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
GLM-4.7's sharpest advantage is in coding, where it averages 75.4 against 72. The single biggest benchmark swing on the page is BrowseComp, 52% to 63.8%. Qwen3.5-122B-A10B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Qwen3.5-122B-A10B 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 | Qwen3.5-122B-A10B |
|---|---|---|---|
| Knowledge | GLM-4.751.8 | Margin→ 31.8 | Qwen3.5-122B-A10B83.6 |
| Agentic | GLM-4.745.7 | Margin→ 10.7 | Qwen3.5-122B-A10B56.4 |
| Coding | GLM-4.775.4 | Margin← 3.4 | Qwen3.5-122B-A10B72.0 |
| Reasoning | GLM-4.7Not measured | MarginNo overlap | Qwen3.5-122B-A10B60.2 |
| Math | GLM-4.71.8 | MarginNo overlap | Qwen3.5-122B-A10BNot measured |
| Multilingual | GLM-4.7Not measured | MarginNo overlap | Qwen3.5-122B-A10B82.2 |
| Multimodal | GLM-4.7Not measured | MarginNo overlap | Qwen3.5-122B-A10B77.2 |
| Inst. Following | GLM-4.7Not measured | MarginNo overlap | Qwen3.5-122B-A10B93.4 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
BrowseComp
AgenticA 52%B 63.8%Winner: Qwen3.5-122B-A10BΔ 11.8BrowseComp: GLM-4.7 scored 52%; Qwen3.5-122B-A10B scored 63.8%. Qwen3.5-122B-A10B wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 41%B 49.4%Winner: Qwen3.5-122B-A10BΔ 8.4Terminal-Bench 2.0: GLM-4.7 scored 41%; Qwen3.5-122B-A10B scored 49.4%. Qwen3.5-122B-A10B wins this benchmark. - Source ↗
MMLU-Pro
KnowledgeA 84.3%B 86.7%Winner: Qwen3.5-122B-A10BΔ 2.4MMLU-Pro: GLM-4.7 scored 84.3%; Qwen3.5-122B-A10B scored 86.7%. Qwen3.5-122B-A10B wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 73.8%B 72%Winner: GLM-4.7Δ 1.8SWE-bench Verified: GLM-4.7 scored 73.8%; Qwen3.5-122B-A10B scored 72%. GLM-4.7 wins this benchmark. - Source ↗
GPQA
KnowledgeA 85.7%B 86.6%Winner: Qwen3.5-122B-A10BΔ 0.9GPQA: GLM-4.7 scored 85.7%; Qwen3.5-122B-A10B scored 86.6%. Qwen3.5-122B-A10B wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-4.7 | Qwen3.5-122B-A10B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | Qwen3.5-122B-A10B$0 input / $0 output | Listed prices are equal. |
| Generation speedtokens per second | GLM-4.782 tok/s | Qwen3.5-122B-A10BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | Qwen3.5-122B-A10BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-4.7200K | Qwen3.5-122B-A10B262K | Qwen3.5-122B-A10B lists the larger context window. |
Benchmark Deep Dive
AgenticQwen3.5-122B-A10B wins9 benchmarks
| Benchmark | GLM-4.7 | Qwen3.5-122B-A10B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 41% | 49.4% | Qwen3.5-122B-A10B leads |
| BrowseCompSource | 52% | 63.8% | Qwen3.5-122B-A10B leads |
| VITA-BenchSource | 15.5% | — | Not comparable |
| AA Agentic IndexSource | 25.4% | 20.7% | GLM-4.7 leads |
| τ²-bench resultsSource | 95.9% | 93.6% | GLM-4.7 leads |
| Gert LabsSource | 39.95% | — | Not comparable |
| GDPval-AASource | 33.3% | 23.9% | GLM-4.7 leads |
| GDPval-AASource | 1165 | 978 | GLM-4.7 leads |
| OSWorld-VerifiedSource | — | 58% | Not comparable |
CodingGLM-4.7 wins6 benchmarks
| Benchmark | GLM-4.7 | Qwen3.5-122B-A10B | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.8% | 72% | GLM-4.7 leads |
| LiveCodeBenchSource | 84.9% | — | Not comparable |
| SWE-RebenchSource | 58.7% | — | Not comparable |
| AA Coding IndexSource | 45.3% | 45.7% | Qwen3.5-122B-A10B leads |
| AA-SciCodeSource | 45.1% | 42.0% | GLM-4.7 leads |
| AA LiveCodeBenchSource | 89.4% | — | Not comparable |
Reasoning3 benchmarks
KnowledgeQwen3.5-122B-A10B wins10 benchmarks
| Benchmark | GLM-4.7 | Qwen3.5-122B-A10B | Result |
|---|---|---|---|
| GPQASource | 85.7% | 86.6% | Qwen3.5-122B-A10B leads |
| MMLU-ProSource | 84.3% | 86.7% | Qwen3.5-122B-A10B leads |
| HLESource | 24.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 33.7% | 32.3% | GLM-4.7 leads |
| AA-GPQA DiamondSource | 85.9% | 85.7% | GLM-4.7 leads |
| AA-HLESource | 25.1% | 23.4% | GLM-4.7 leads |
| AA-Omniscience IndexSource | -34.6% | -39.6% | GLM-4.7 leads |
| AA-Omniscience AccuracySource | 29.3% | 24.7% | GLM-4.7 leads |
| AA-Omniscience Hallucination RateSource | 90.3% | 85.5% | Qwen3.5-122B-A10B leads |
| SuperGPQASource | — | 67.1% | Not comparable |
Math3 benchmarks
Multilingual1 benchmarks
| Benchmark | GLM-4.7 | Qwen3.5-122B-A10B | Result |
|---|---|---|---|
| MMLU-ProXSource | — | 82.2% | Not comparable |
Multimodal7 benchmarks
Frequently Asked Questions (4)
Which is better, GLM-4.7 or Qwen3.5-122B-A10B?
GLM-4.7 is ahead on BenchLM's BenchAlign leaderboard, 61.16 to 60.56. The biggest single separator in this matchup is BrowseComp, where the scores are 52% and 63.8%.
Which is better for knowledge tasks, GLM-4.7 or Qwen3.5-122B-A10B?
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 83.6 versus 51.8. 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 Qwen3.5-122B-A10B?
GLM-4.7 has the edge for coding in this comparison, averaging 75.4 versus 72. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-4.7 or Qwen3.5-122B-A10B?
Qwen3.5-122B-A10B has the edge for agentic tasks in this comparison, averaging 56.4 versus 45.7. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
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