Model comparison
GLM-5.2 vs Qwen3 235B-A22B
GLM-5.2 is the stronger model overall, scoring 50.9 to 43.2 on the Noometry Index. Qwen3 235B-A22B costs 1.8× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Last verified . 34 shared benchmarks.
Alibaba (Qwen)Summary
- They share 34 benchmarks with published results for both. GLM-5.2 scores higher in 7 categories and Qwen3 235B-A22B in 2 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.2 leads 42.1 to 15.8.
- The biggest single-benchmark swing is ARC-AGI-1: 77% for GLM-5.2 and 11% for Qwen3 235B-A22B.
- Qwen3 235B-A22B is cheaper at $0.70 / $2.80 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 131K.
Side by side
| GLM-5.2 | Qwen3 235B-A22B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 50.9 | 43.2 |
| Released | 2026-06-13 | 2025-04 |
| Weights | Open | Open |
| Context window | 1M | 131K |
| Max output | 131K | 16K |
| Input $ / M tokens | $1.40 | $0.70 |
| Output $ / M tokens | $4.40 | $2.80 |
| Results tracked | 50 | 49 |
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Category by category
Coding GLM-5.2 leads
GLM-5.2: 50.9 (#37), Qwen3 235B-A22B: 43.8 (#72)
| Benchmark | GLM-5.2 | Qwen3 235B-A22B |
|---|---|---|
| SciCode | 50.5% | 42.4% |
| WeirdML | 70.1% | 41% |
| LMArena Coding | 1484 | 1445 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| Aider Polyglot | — | 59.6% |
| LMArena WebDev | 1604 | — |
| ALE-Bench | 1,047 | — |
Agentic & Tool Use Qwen3 235B-A22B leads
GLM-5.2: 31.6 (#63), Qwen3 235B-A22B: 33.7 (#48)
| Benchmark | GLM-5.2 | Qwen3 235B-A22B |
|---|---|---|
| Vending-Bench 2 | 8,314 | -11.34 |
| Berkeley Function Calling Leaderboard | — | 52.1% |
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |
Reasoning GLM-5.2 leads
GLM-5.2: 42.1 (#46), Qwen3 235B-A22B: 15.8 (#302)
| Benchmark | GLM-5.2 | Qwen3 235B-A22B |
|---|---|---|
| ARC-AGI-2 | 22.8% | 1.3% |
| SimpleBench | 58.8% | 31% |
| Kagi LLM Benchmark | 62.6% | 69.4% |
| ARC-AGI-1 | 77% | 11% |
| CritPt | 20.9% | 0% |
| Chess Puzzles | 21% | 12% |
| LMArena Hard Prompts | 1480 | 1433 |
| Mystery Game Puzzles | 19% | 9% |
| DTBench | 93.6% | 80.3% |
| LMCA | 45.8% | 29.3% |
| Epoch Capabilities Index | 151.76 | 143.87 |
| NYT Connections (extended) | 74.3% | — |
| EBR-Bench | 9.5% | — |
| Surface Evolver Bench | 55.6% | — |
| ForecastBench | — | 59.7 |
Math GLM-5.2 leads
GLM-5.2: 55.4 (#35), Qwen3 235B-A22B: 48.5 (#51)
| Benchmark | GLM-5.2 | Qwen3 235B-A22B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 86.4% | 86.7% |
| LMArena Math | 1482 | 1431 |
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| ProofBench | 35% | — |
| Omni-MATH | — | 71.8% |
| MATH Level 5 | — | 68.9% |
| FrontierMath (Feb 2025 set) | — | 8.5% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge GLM-5.2 leads
GLM-5.2: 57.1 (#36), Qwen3 235B-A22B: 49.5 (#67)
| Benchmark | GLM-5.2 | Qwen3 235B-A22B |
|---|---|---|
| GPQA Diamond | 91.9% | 80.1% |
| SimpleQA Verified | 34.2% | 40.4% |
| LMArena Expert | 1484 | 1460 |
| MMLU-Pro | — | 84.4% |
| Confabulations | — | 15.6% |
| Vectara Hallucination Rate | — | 9.3% |
| GPQA (HELM) | — | 72.7% |
Multilingual GLM-5.2 leads
GLM-5.2: 56.0 (#25), Qwen3 235B-A22B: 52.5 (#84)
| Benchmark | GLM-5.2 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Non-English | 1459 | 1410 |
| LMArena Chinese | 1518 | 1481 |
| LMArena French | 1476 | 1445 |
| LMArena German | 1470 | 1432 |
| LMArena Japanese | 1451 | 1400 |
| LMArena Korean | 1445 | 1391 |
| LMArena Russian | 1467 | 1411 |
| LMArena Spanish | 1477 | 1429 |
Instruction Following GLM-5.2 leads
GLM-5.2: 77.1 (#32), Qwen3 235B-A22B: 72.8 (#132)
| Benchmark | GLM-5.2 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Instruction Following | 1465 | 1408 |
| IFEval | — | 83.5% |
Long Context Too close to call
GLM-5.2: 45.3 (#43), Qwen3 235B-A22B: 46.1 (#25)
| Benchmark | GLM-5.2 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Longer Query | 1479 | 1426 |
| Fiction.LiveBench | — | 75% |
Writing & Preference GLM-5.2 leads
GLM-5.2: 70.7 (#20), Qwen3 235B-A22B: 59.8 (#103)
| Benchmark | GLM-5.2 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Text | 1470 | 1419 |
| LMArena Creative Writing | 1465 | 1384 |
| EQ-Bench Creative Writing | 1757 | 1366 |
| LMArena Multi-Turn | 1468 | 1432 |
| Short-Story Creative Writing | — | 83% |
| WildBench | — | 86.6% |
| EQ-Bench 4 | 1222 | — |
Frequently asked questions
Is GLM-5.2 better than Qwen3 235B-A22B?
GLM-5.2 is the stronger model overall, scoring 50.9 to 43.2 on the Noometry Index. Qwen3 235B-A22B costs 1.8× 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 235B-A22B?
Qwen3 235B-A22B is cheaper. It lists at $0.70 per million input tokens and $2.80 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.
Is GLM-5.2 or Qwen3 235B-A22B better for coding?
GLM-5.2 scores higher on coding benchmarks: 50.9 versus 43.8 in the Noometry coding category.
Which has the bigger context window?
GLM-5.2 does, with 1M tokens against 131K.
How many benchmarks do GLM-5.2 and Qwen3 235B-A22B share?
34 benchmarks have published results for both models. GLM-5.2 has 50 scored results on Noometry and Qwen3 235B-A22B has 49.