Model comparison
GLM-5 vs Qwen3.5 397B-A17B
GLM-5 and Qwen3.5 397B-A17B score almost the same on the Noometry Index (46.1 vs 46.0), so choose on price, context window or the category you care about most.
Last verified . 29 shared benchmarks.
Alibaba (Qwen)Summary
- They share 29 benchmarks with published results for both. GLM-5 scores higher in 5 categories and Qwen3.5 397B-A17B in 4 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in coding, where GLM-5 leads 49.0 to 42.0.
- The biggest single-benchmark swing is NYT Connections (extended): 74.8% for GLM-5 and 58.9% for Qwen3.5 397B-A17B.
- Qwen3.5 397B-A17B is cheaper at $0.60 / $3.60 per million input/output tokens, against $1 / $3.20 for GLM-5.
- Qwen3.5 397B-A17B accepts more context: 262K tokens versus 205K.
Side by side
| GLM-5 | Qwen3.5 397B-A17B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 46.1 | 46.0 |
| Released | 2026-02-11 | 2026-02-01 |
| Weights | Open | Open |
| Context window | 205K | 262K |
| Max output | 131K | 66K |
| Input $ / M tokens | $1 | $0.60 |
| Output $ / M tokens | $3.20 | $3.60 |
| Results tracked | 45 | 36 |
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Category by category
Coding GLM-5 leads
GLM-5: 49.0 (#52), Qwen3.5 397B-A17B: 42.0 (#114)
| Benchmark | GLM-5 | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena WebDev | 1434 | 1400 |
| LMArena Coding | 1461 | 1465 |
| SWE-bench Verified | 72.1% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| SWE-bench Multilingual | 69.7% | — |
| WeirdML | 48.2% | — |
| ALE-Bench | 765.62 | — |
Agentic & Tool Use Qwen3.5 397B-A17B leads
GLM-5: 31.1 (#71), Qwen3.5 397B-A17B: 33.3 (#53)
| Benchmark | GLM-5 | Qwen3.5 397B-A17B |
|---|---|---|
| τ²-bench Airline | 82.5% | 81.5% |
| τ²-bench Banking | 9.8% | 9.8% |
| τ²-bench Retail | 73.7% | 84.4% |
| τ²-bench Telecom | 86.8% | 97.8% |
| Terminal-Bench | 52.4% | — |
| APEX-Agents | — | 24.9% |
| Vending-Bench 2 | 4,432 | — |
Reasoning Qwen3.5 397B-A17B leads
GLM-5: 27.6 (#116), Qwen3.5 397B-A17B: 34.5 (#70)
| Benchmark | GLM-5 | Qwen3.5 397B-A17B |
|---|---|---|
| Kagi LLM Benchmark | 75% | 73.7% |
| NYT Connections (extended) | 74.8% | 58.9% |
| Chess Puzzles | 10% | 13% |
| LMArena Hard Prompts | 1452 | 1448 |
| Epoch Capabilities Index | 145.83 | 146.65 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 53.2% | — |
| ARC-AGI-1 | 44.7% | — |
| Thematic Generalization | — | 65.1% |
| Mystery Game Puzzles | — | 18% |
| DTBench | — | 87.5% |
| LMCA | — | 37.9% |
| ForecastBench | 61 | — |
Math Too close to call
GLM-5: 46.4 (#71), Qwen3.5 397B-A17B: 46.1 (#73)
| Benchmark | GLM-5 | Qwen3.5 397B-A17B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 80% | 88.9% |
| LMArena Math | 1440 | 1454 |
| FrontierMath (Tiers 1-3) | — | 31.2% |
| MathArena Final-Answer Competitions | 65.7% | — |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Qwen3.5 397B-A17B leads
GLM-5: 52.3 (#64), Qwen3.5 397B-A17B: 53.3 (#58)
| Benchmark | GLM-5 | Qwen3.5 397B-A17B |
|---|---|---|
| GPQA Diamond | 87.8% | 86.4% |
| LMArena Expert | 1454 | 1462 |
| Vectara Hallucination Rate | 10.1% | — |
Multimodal Not comparable
GLM-5: —, Qwen3.5 397B-A17B: 40.7 (#44)
| Benchmark | GLM-5 | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Vision | — | 1263 |
Multilingual Too close to call
GLM-5: 53.7 (#58), Qwen3.5 397B-A17B: 53.7 (#59)
| Benchmark | GLM-5 | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Non-English | 1430 | 1430 |
| LMArena Chinese | 1511 | 1500 |
| LMArena French | 1455 | 1461 |
| LMArena German | 1445 | 1447 |
| LMArena Japanese | 1416 | 1426 |
| LMArena Korean | 1423 | 1384 |
| LMArena Russian | 1436 | 1429 |
| LMArena Spanish | 1454 | 1441 |
Instruction Following Too close to call
GLM-5: 75.2 (#67), Qwen3.5 397B-A17B: 75.0 (#77)
| Benchmark | GLM-5 | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Instruction Following | 1428 | 1424 |
Long Context Too close to call
GLM-5: 44.7 (#60), Qwen3.5 397B-A17B: 44.1 (#74)
| Benchmark | GLM-5 | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Longer Query | 1446 | 1442 |
| CL-bench | 18.7% | — |
Writing & Preference GLM-5 leads
GLM-5: 66.0 (#38), Qwen3.5 397B-A17B: 62.3 (#79)
| Benchmark | GLM-5 | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Text | 1446 | 1438 |
| LMArena Creative Writing | 1439 | 1401 |
| EQ-Bench Creative Writing | 1601 | 1478 |
| LMArena Multi-Turn | 1456 | 1446 |
Frequently asked questions
Is GLM-5 better than Qwen3.5 397B-A17B?
GLM-5 and Qwen3.5 397B-A17B score almost the same on the Noometry Index (46.1 vs 46.0), so choose on price, context window or the category you care about most.
Which is cheaper, GLM-5 or Qwen3.5 397B-A17B?
Qwen3.5 397B-A17B is cheaper. It lists at $0.60 per million input tokens and $3.60 per million output tokens; GLM-5 lists at $1 and $3.20.
Is GLM-5 or Qwen3.5 397B-A17B better for coding?
GLM-5 scores higher on coding benchmarks: 49.0 versus 42.0 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5 397B-A17B does, with 262K tokens against 205K.
How many benchmarks do GLM-5 and Qwen3.5 397B-A17B share?
29 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and Qwen3.5 397B-A17B has 36.