Model comparison
GLM-5.2 vs Qwen3.5 35B-A3B
GLM-5.2 is the stronger model overall, scoring 50.9 to 41.7 on the Noometry Index. Qwen3.5 35B-A3B costs 3.1× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Last verified . 27 shared benchmarks.
Alibaba (Qwen)Summary
- They share 27 benchmarks with published results for both. GLM-5.2 scores higher in 8 categories and Qwen3.5 35B-A3B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.2 leads 42.1 to 23.9.
- The biggest single-benchmark swing is SciCode: 50.5% for GLM-5.2 and 29.3% for Qwen3.5 35B-A3B.
- Qwen3.5 35B-A3B is cheaper at $0.25 / $2 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 262K.
Side by side
| GLM-5.2 | Qwen3.5 35B-A3B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 50.9 | 41.7 |
| Released | 2026-06-13 | 2026-02-01 |
| Weights | Open | Open |
| Context window | 1M | 262K |
| Max output | 131K | 66K |
| Input $ / M tokens | $1.40 | $0.25 |
| Output $ / M tokens | $4.40 | $2 |
| Results tracked | 50 | 28 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GLM-5.2 leads
GLM-5.2: 50.9 (#37), Qwen3.5 35B-A3B: 33.5 (#244)
| Benchmark | GLM-5.2 | Qwen3.5 35B-A3B |
|---|---|---|
| LMArena WebDev | 1604 | 1254 |
| SciCode | 50.5% | 29.3% |
| LMArena Coding | 1484 | 1410 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| WeirdML | 70.1% | — |
| ALE-Bench | 1,047 | — |
Agentic & Tool Use Not comparable
GLM-5.2: 31.6 (#63), Qwen3.5 35B-A3B: —
| Benchmark | GLM-5.2 | Qwen3.5 35B-A3B |
|---|---|---|
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 8,314 | — |
Reasoning GLM-5.2 leads
GLM-5.2: 42.1 (#46), Qwen3.5 35B-A3B: 23.9 (#158)
| Benchmark | GLM-5.2 | Qwen3.5 35B-A3B |
|---|---|---|
| CritPt | 20.9% | 0.6% |
| Chess Puzzles | 21% | 10% |
| LMArena Hard Prompts | 1480 | 1400 |
| DTBench | 93.6% | 80% |
| LMCA | 45.8% | 29.5% |
| Epoch Capabilities Index | 151.76 | 142.52 |
| ARC-AGI-2 | 22.8% | — |
| SimpleBench | 58.8% | — |
| Kagi LLM Benchmark | 62.6% | — |
| NYT Connections (extended) | 74.3% | — |
| ARC-AGI-1 | 77% | — |
| EBR-Bench | 9.5% | — |
| Mystery Game Puzzles | 19% | — |
| Surface Evolver Bench | 55.6% | — |
Math GLM-5.2 leads
GLM-5.2: 55.4 (#35), Qwen3.5 35B-A3B: 38.4 (#86)
| Benchmark | GLM-5.2 | Qwen3.5 35B-A3B |
|---|---|---|
| MathArena Final-Answer Competitions | 67.6% | 56% |
| OTIS Mock AIME 2024-2025 | 86.4% | 70% |
| LMArena Math | 1482 | 1404 |
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| ProofBench | 35% | — |
Knowledge GLM-5.2 leads
GLM-5.2: 57.1 (#36), Qwen3.5 35B-A3B: 47.7 (#73)
| Benchmark | GLM-5.2 | Qwen3.5 35B-A3B |
|---|---|---|
| GPQA Diamond | 91.9% | 83.5% |
| LMArena Expert | 1484 | 1408 |
| SimpleQA Verified | 34.2% | — |
| Vectara Hallucination Rate | — | 10.5% |
Multilingual GLM-5.2 leads
GLM-5.2: 56.0 (#25), Qwen3.5 35B-A3B: 50.2 (#122)
| Benchmark | GLM-5.2 | Qwen3.5 35B-A3B |
|---|---|---|
| LMArena Non-English | 1459 | 1379 |
| LMArena Chinese | 1518 | 1458 |
| LMArena French | 1476 | 1412 |
| LMArena German | 1470 | 1367 |
| LMArena Japanese | 1451 | 1326 |
| LMArena Korean | 1445 | 1354 |
| LMArena Russian | 1467 | 1377 |
| LMArena Spanish | 1477 | 1390 |
Instruction Following GLM-5.2 leads
GLM-5.2: 77.1 (#32), Qwen3.5 35B-A3B: 73.0 (#123)
| Benchmark | GLM-5.2 | Qwen3.5 35B-A3B |
|---|---|---|
| LMArena Instruction Following | 1465 | 1379 |
Long Context GLM-5.2 leads
GLM-5.2: 45.3 (#43), Qwen3.5 35B-A3B: 42.4 (#122)
| Benchmark | GLM-5.2 | Qwen3.5 35B-A3B |
|---|---|---|
| LMArena Longer Query | 1479 | 1390 |
Writing & Preference GLM-5.2 leads
GLM-5.2: 70.7 (#20), Qwen3.5 35B-A3B: 58.1 (#118)
| Benchmark | GLM-5.2 | Qwen3.5 35B-A3B |
|---|---|---|
| LMArena Text | 1470 | 1395 |
| LMArena Creative Writing | 1465 | 1347 |
| LMArena Multi-Turn | 1468 | 1391 |
| EQ-Bench Creative Writing | 1757 | — |
| EQ-Bench 4 | 1222 | — |
Frequently asked questions
Is GLM-5.2 better than Qwen3.5 35B-A3B?
GLM-5.2 is the stronger model overall, scoring 50.9 to 41.7 on the Noometry Index. Qwen3.5 35B-A3B costs 3.1× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Which is cheaper, GLM-5.2 or Qwen3.5 35B-A3B?
Qwen3.5 35B-A3B is cheaper. It lists at $0.25 per million input tokens and $2 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.
Is GLM-5.2 or Qwen3.5 35B-A3B better for coding?
GLM-5.2 scores higher on coding benchmarks: 50.9 versus 33.5 in the Noometry coding category.
Which has the bigger context window?
GLM-5.2 does, with 1M tokens against 262K.
How many benchmarks do GLM-5.2 and Qwen3.5 35B-A3B share?
27 benchmarks have published results for both models. GLM-5.2 has 50 scored results on Noometry and Qwen3.5 35B-A3B has 28.