Model comparison
GLM-5.2 vs Qwen3.6 Flash
GLM-5.2 is the stronger model overall, scoring 50.9 to 38.4 on the Noometry Index. Qwen3.6 Flash costs 5.1× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Last verified . 11 shared benchmarks.
Alibaba (Qwen)Summary
- They share 11 benchmarks with published results for both. GLM-5.2 scores higher in 3 categories and Qwen3.6 Flash in 0 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.2 leads 55.4 to 38.0.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 59.2% for GLM-5.2 and 22.5% for Qwen3.6 Flash.
- Qwen3.6 Flash is cheaper at $0.19 / $1.13 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.2 | Qwen3.6 Flash | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 50.9 | 38.4 |
| Released | 2026-06-13 | 2026-04-27 |
| Weights | Open | Proprietary |
| Context window | 1M | 1M |
| Max output | 131K | 66K |
| Input $ / M tokens | $1.40 | $0.19 |
| Output $ / M tokens | $4.40 | $1.13 |
| Results tracked | 50 | 13 |
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Category by category
Coding Not comparable
GLM-5.2: 50.9 (#37), Qwen3.6 Flash: —
| Benchmark | GLM-5.2 | Qwen3.6 Flash |
|---|---|---|
| ALE-Bench | 1,047 | 326.4 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| LMArena WebDev | 1604 | — |
| SciCode | 50.5% | — |
| WeirdML | 70.1% | — |
| LMArena Coding | 1484 | — |
Agentic & Tool Use Not comparable
GLM-5.2: 31.6 (#63), Qwen3.6 Flash: —
| Benchmark | GLM-5.2 | Qwen3.6 Flash |
|---|---|---|
| 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), Qwen3.6 Flash: 28.4 (#90)
| Benchmark | GLM-5.2 | Qwen3.6 Flash |
|---|---|---|
| SimpleBench | 58.8% | 35.2% |
| Chess Puzzles | 21% | 20% |
| Mystery Game Puzzles | 19% | 18% |
| DTBench | 93.6% | 77.1% |
| LMCA | 45.8% | 31% |
| Epoch Capabilities Index | 151.76 | 143.26 |
| ARC-AGI-2 | 22.8% | — |
| Kagi LLM Benchmark | 62.6% | — |
| NYT Connections (extended) | 74.3% | — |
| ARC-AGI-1 | 77% | — |
| CritPt | 20.9% | — |
| EBR-Bench | 9.5% | — |
| LMArena Hard Prompts | 1480 | — |
| Surface Evolver Bench | 55.6% | — |
Math GLM-5.2 leads
GLM-5.2: 55.4 (#35), Qwen3.6 Flash: 38.0 (#88)
| Benchmark | GLM-5.2 | Qwen3.6 Flash |
|---|---|---|
| FrontierMath (Tiers 1-3) | 59.2% | 22.5% |
| OTIS Mock AIME 2024-2025 | 86.4% | 84.4% |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| ProofBench | 35% | — |
| LMArena Math | 1482 | — |
| FrontierMath (Feb 2025 set) | — | 10.3% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge GLM-5.2 leads
GLM-5.2: 57.1 (#36), Qwen3.6 Flash: 42.2 (#90)
| Benchmark | GLM-5.2 | Qwen3.6 Flash |
|---|---|---|
| GPQA Diamond | 91.9% | 83.3% |
| SimpleQA Verified | 34.2% | 15.9% |
| LMArena Expert | 1484 | — |
Multilingual Not comparable
GLM-5.2: 56.0 (#25), Qwen3.6 Flash: —
| Benchmark | GLM-5.2 | Qwen3.6 Flash |
|---|---|---|
| LMArena Non-English | 1459 | — |
| LMArena Chinese | 1518 | — |
| LMArena French | 1476 | — |
| LMArena German | 1470 | — |
| LMArena Japanese | 1451 | — |
| LMArena Korean | 1445 | — |
| LMArena Russian | 1467 | — |
| LMArena Spanish | 1477 | — |
Instruction Following Not comparable
GLM-5.2: 77.1 (#32), Qwen3.6 Flash: —
| Benchmark | GLM-5.2 | Qwen3.6 Flash |
|---|---|---|
| LMArena Instruction Following | 1465 | — |
Long Context Not comparable
GLM-5.2: 45.3 (#43), Qwen3.6 Flash: —
| Benchmark | GLM-5.2 | Qwen3.6 Flash |
|---|---|---|
| LMArena Longer Query | 1479 | — |
Writing & Preference Not comparable
GLM-5.2: 70.7 (#20), Qwen3.6 Flash: —
| Benchmark | GLM-5.2 | Qwen3.6 Flash |
|---|---|---|
| LMArena Text | 1470 | — |
| LMArena Creative Writing | 1465 | — |
| EQ-Bench Creative Writing | 1757 | — |
| EQ-Bench 4 | 1222 | — |
| LMArena Multi-Turn | 1468 | — |
Frequently asked questions
Is GLM-5.2 better than Qwen3.6 Flash?
GLM-5.2 is the stronger model overall, scoring 50.9 to 38.4 on the Noometry Index. Qwen3.6 Flash costs 5.1× 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 Qwen3.6 Flash?
Qwen3.6 Flash is cheaper. It lists at $0.19 per million input tokens and $1.13 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.
Which has the bigger context window?
Both accept 1M tokens.
How many benchmarks do GLM-5.2 and Qwen3.6 Flash share?
11 benchmarks have published results for both models. GLM-5.2 has 50 scored results on Noometry and Qwen3.6 Flash has 13.