Model comparison
GLM-4.5 vs Qwen3.5 397B
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-4.5 #68 (Estimated); Qwen3.5 397B #71 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.5 and Qwen3.5 397B share 0 comparable benchmark results. 0 of 8 categories are comparable. 1 result is unique to GLM-4.5; 55 to Qwen3.5 397B.
Updated July 20, 2026- Shared results
- 0
- GLM-4.5 only
- 1
- Qwen3.5 397B only
- 55
- Comparable categories
- 0 / 8
Benchmark data for GLM-4.5 and Qwen3.5 397B is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
Qwen3.5 397B is priced at $0.60 input / $3.60 output per 1M tokens, versus $0.60 input / $2.20 output per 1M tokens for GLM-4.5.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-4.5 | Qwen3.5 397B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.5$0.6 input / $2.2 output | Qwen3.5 397B$0.6 input / $3.6 output | GLM-4.5 has the lower combined listed price. |
| Generation speedtokens per second | GLM-4.551 tok/s | Qwen3.5 397B96 tok/s | Qwen3.5 397B has the higher measured throughput. |
| First-answer latencyseconds to first token | GLM-4.51.45 s | Qwen3.5 397B2.44 s | GLM-4.5 reaches the first token sooner. |
| Context windowmaximum listed tokens | GLM-4.5128K | Qwen3.5 397B128K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic18 benchmarks
| Benchmark | GLM-4.5 | Qwen3.5 397B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | — | 52.5% | Not comparable |
| BrowseCompSource | — | 62% | Not comparable |
| Claw-EvalSource | — | 56.8% | Not comparable |
| QwenClawBenchSource | — | 51.8% | Not comparable |
| τ³-bench resultsSource | — | 68.4% | Not comparable |
| VITA-BenchSource | — | 43.7% | 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 |
| τ²-bench resultsSource | — | 95.6% | Not comparable |
| Gert LabsSource | — | 46.76% | Not comparable |
| ResearchClawBenchSource | — | 14.2% | Not comparable |
| AA Agentic IndexSource | — | 19.9% | Not comparable |
| APEX-Agents-AASource | — | 15.3% | Not comparable |
| GDPval-AASource | — | 23.1% | Not comparable |
| GDPval-AASource | — | 962 | Not comparable |
Coding5 benchmarks
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | GLM-4.5 | Qwen3.5 397B | Result |
|---|---|---|---|
| GPQASource | — | 88.4% | Not comparable |
| SuperGPQASource | — | 70.4% | Not comparable |
| MMLU-ProSource | — | 87.8% | Not comparable |
| MMLU-ReduxSource | — | 94.9% | Not comparable |
| C-EvalSource | — | 93% | Not comparable |
| HLESource | — | 28.7% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 33.7% | Not comparable |
| AA-GPQA DiamondSource | — | 89.3% | Not comparable |
| AA-HLESource | — | 27.3% | Not comparable |
| AA-Omniscience IndexSource | — | -29.8% | Not comparable |
| AA-Omniscience AccuracySource | — | 31.4% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 89.1% | Not comparable |
Math5 benchmarks
Multilingual2 benchmarks
Multimodal8 benchmarks
| Benchmark | GLM-4.5 | Qwen3.5 397B | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1202 | — | 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 (3)
Can I compare GLM-4.5 and Qwen3.5 397B on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for GLM-4.5 and Qwen3.5 397B today?
GLM-4.5: $0.60 input / $2.20 output per 1M tokens Qwen3.5 397B: $0.60 input / $3.60 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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