Model comparison
GLM-4.7 vs Qwen3.5 397B
Head-to-head evidence from 23 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 397B #71 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.7 and Qwen3.5 397B share 23 comparable benchmark results. 4 of 8 categories are comparable. 7 results are unique to GLM-4.7; 32 to Qwen3.5 397B.
Updated July 22, 2026- Shared results
- 23
- GLM-4.7 only
- 7
- Qwen3.5 397B only
- 32
- Comparable categories
- 4 / 8
Pick GLM-4.7 if you want the stronger benchmark profile. Qwen3.5 397B only becomes the better choice if mathematics 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 23 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 BenchAlign aggregate, 61.16 to 57.01. 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 66.5. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 41% to 52.5%. Qwen3.5 397B does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
Qwen3.5 397B is also the more expensive model on tokens at $0.60 input / $3.60 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for GLM-4.7. That is roughly Infinityx on output cost alone. GLM-4.7 is the reasoning model in the pair, while Qwen3.5 397B 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. GLM-4.7 gives you the larger context window at 200K, compared with 128K for Qwen3.5 397B.
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 397B |
|---|---|---|---|
| Math | GLM-4.71.8 | Margin→ 88.8 | Qwen3.5 397B90.6 |
| Agentic | GLM-4.745.7 | Margin→ 10.8 | Qwen3.5 397B56.5 |
| Coding | GLM-4.775.4 | Margin← 8.9 | Qwen3.5 397B66.5 |
| Knowledge | GLM-4.751.8 | Margin→ 4.8 | Qwen3.5 397B56.6 |
| Reasoning | GLM-4.7Not measured | MarginNo overlap | Qwen3.5 397B63.2 |
| Multilingual | GLM-4.7Not measured | MarginNo overlap | Qwen3.5 397B84.7 |
| Multimodal | GLM-4.7Not measured | MarginNo overlap | Qwen3.5 397B79.6 |
| Inst. Following | GLM-4.7Not measured | MarginNo overlap | Qwen3.5 397B92.6 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 41%B 52.5%Winner: Qwen3.5 397BΔ 11.5Terminal-Bench 2.0: GLM-4.7 scored 41%; Qwen3.5 397B scored 52.5%. Qwen3.5 397B wins this benchmark. - Source ↗
BrowseComp
AgenticA 52%B 62%Winner: Qwen3.5 397BΔ 10BrowseComp: GLM-4.7 scored 52%; Qwen3.5 397B scored 62%. Qwen3.5 397B wins this benchmark. - Source ↗
HLE
KnowledgeA 24.8%B 28.7%Winner: Qwen3.5 397BΔ 3.9HLE: GLM-4.7 scored 24.8%; Qwen3.5 397B scored 28.7%. Qwen3.5 397B wins this benchmark. - Source ↗
MMLU-Pro
KnowledgeA 84.3%B 87.8%Winner: Qwen3.5 397BΔ 3.5MMLU-Pro: GLM-4.7 scored 84.3%; Qwen3.5 397B scored 87.8%. Qwen3.5 397B wins this benchmark. - Source ↗
GPQA
KnowledgeA 85.7%B 88.4%Winner: Qwen3.5 397BΔ 2.7GPQA: GLM-4.7 scored 85.7%; Qwen3.5 397B scored 88.4%. Qwen3.5 397B 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 397B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | Qwen3.5 397B$0.6 input / $3.6 output | GLM-4.7 has the lower combined listed price. |
| Generation speedtokens per second | GLM-4.782 tok/s | Qwen3.5 397B96 tok/s | Qwen3.5 397B has the higher measured throughput. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | Qwen3.5 397B2.44 s | GLM-4.7 reaches the first token sooner. |
| Context windowmaximum listed tokens | GLM-4.7200K | Qwen3.5 397B128K | GLM-4.7 lists the larger context window. |
Benchmark Deep Dive
AgenticQwen3.5 397B wins18 benchmarks
| Benchmark | GLM-4.7 | Qwen3.5 397B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 41% | 52.5% | Qwen3.5 397B leads |
| BrowseCompSource | 52% | 62% | Qwen3.5 397B leads |
| VITA-BenchSource | 15.5% | 43.7% | Qwen3.5 397B leads |
| AA Agentic IndexSource | 25.4% | 19.9% | GLM-4.7 leads |
| τ²-bench resultsSource | 95.9% | 95.6% | GLM-4.7 leads |
| Gert LabsSource | 39.95% | 46.76% | Qwen3.5 397B leads |
| GDPval-AASource | 33.3% | 23.1% | GLM-4.7 leads |
| GDPval-AASource | 1165 | 962 | GLM-4.7 leads |
| Claw-EvalSource | — | 56.8% | Not comparable |
| QwenClawBenchSource | — | 51.8% | Not comparable |
| τ³-bench resultsSource | — | 68.4% | Not comparable |
| DeepPlanningSource | — | 37.6% | Not comparable |
| ToolathlonSource | — | 36.3% | Not comparable |
| MCP AtlasSource | — | 46.1% | Not comparable |
| MCP-TasksSource | — | 74.2% | Not comparable |
| WideResearchSource | — | 74.0% | Not comparable |
| ResearchClawBenchSource | — | 14.2% | Not comparable |
| APEX-Agents-AASource | — | 15.3% | Not comparable |
CodingGLM-4.7 wins8 benchmarks
| Benchmark | GLM-4.7 | Qwen3.5 397B | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.8% | 76.2% | Qwen3.5 397B leads |
| LiveCodeBenchSource | 84.9% | — | Not comparable |
| SWE-RebenchSource | 58.7% | — | Not comparable |
| AA Coding IndexSource | 45.3% | 48.2% | Qwen3.5 397B leads |
| AA-SciCodeSource | 45.1% | 42.0% | GLM-4.7 leads |
| AA LiveCodeBenchSource | 89.4% | — | Not comparable |
| LiveCodeBench v6Source | — | 83.6% | Not comparable |
| SWE-bench ProSource | — | 50.9% | Not comparable |
Reasoning4 benchmarks
KnowledgeQwen3.5 397B wins12 benchmarks
| Benchmark | GLM-4.7 | Qwen3.5 397B | Result |
|---|---|---|---|
| GPQASource | 85.7% | 88.4% | Qwen3.5 397B leads |
| MMLU-ProSource | 84.3% | 87.8% | Qwen3.5 397B leads |
| HLESource | 24.8% | 28.7% | Qwen3.5 397B leads |
| Artificial Analysis Intelligence IndexSource | 33.7% | 33.7% | GLM-4.7 leads |
| AA-GPQA DiamondSource | 85.9% | 89.3% | Qwen3.5 397B leads |
| AA-HLESource | 25.1% | 27.3% | Qwen3.5 397B leads |
| AA-Omniscience IndexSource | -34.6% | -29.8% | Qwen3.5 397B leads |
| AA-Omniscience AccuracySource | 29.3% | 31.4% | Qwen3.5 397B leads |
| AA-Omniscience Hallucination RateSource | 90.3% | 89.1% | Qwen3.5 397B leads |
| SuperGPQASource | — | 70.4% | Not comparable |
| MMLU-ReduxSource | — | 94.9% | Not comparable |
| C-EvalSource | — | 93% | Not comparable |
MathQwen3.5 397B wins8 benchmarks
| Benchmark | GLM-4.7 | Qwen3.5 397B | 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 |
| AIME26Source | — | 93.3% | Not comparable |
| HMMT Feb 2025Source | — | 94.8% | Not comparable |
| HMMT Nov 2025Source | — | 92.7% | Not comparable |
| HMMT Feb 2026Source | — | 87.9% | Not comparable |
| MMAnswerBenchSource | — | 80.9% | Not comparable |
Multilingual2 benchmarks
Multimodal8 benchmarks
| Benchmark | GLM-4.7 | Qwen3.5 397B | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1255 | — | Not comparable |
| MMMU-ProSource | — | 79% | Not comparable |
| MathVisionSource | — | 88.6% | Not comparable |
| CharXivSource | — | 80.8% | Not comparable |
| VideoMMMUSource | — | 84.7% | Not comparable |
| ScreenSpot ProSource | — | 65.6% | Not comparable |
| V*Source | — | 95.8% | Not comparable |
| AA-MMMU-ProSource | — | 77.3% | Not comparable |
Frequently Asked Questions (5)
Which is better, GLM-4.7 or Qwen3.5 397B?
GLM-4.7 is ahead on BenchLM's BenchAlign leaderboard, 61.16 to 57.01. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 41% and 52.5%.
Which is better for knowledge tasks, GLM-4.7 or Qwen3.5 397B?
Qwen3.5 397B has the edge for knowledge tasks in this comparison, averaging 56.6 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 397B?
GLM-4.7 has the edge for coding in this comparison, averaging 75.4 versus 66.5. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Which is better for math, GLM-4.7 or Qwen3.5 397B?
Qwen3.5 397B has the edge for math in this comparison, averaging 90.6 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 Qwen3.5 397B?
Qwen3.5 397B has the edge for agentic tasks in this comparison, averaging 56.5 versus 45.7. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
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