Model comparison
GLM-5.2 vs Qwen3-Coder 480B-A35B Instruct
GLM-5.2 is the stronger model overall, scoring 50.9 to 37.7 on the Noometry Index.
Last verified . 21 shared benchmarks.
Alibaba (Qwen)Summary
- They share 21 benchmarks with published results for both. GLM-5.2 scores higher in 9 categories and Qwen3-Coder 480B-A35B Instruct in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.2 leads 57.1 to 36.4.
- The biggest single-benchmark swing is WeirdML: 70.1% for GLM-5.2 and 41.2% for Qwen3-Coder 480B-A35B Instruct.
- GLM-5.2 is cheaper at $1.40 / $4.40 per million input/output tokens, against $1.50 / $7.50 for Qwen3-Coder 480B-A35B Instruct.
- GLM-5.2 accepts more context: 1M tokens versus 262K.
Side by side
| GLM-5.2 | Qwen3-Coder 480B-A35B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 50.9 | 37.7 |
| Released | 2026-06-13 | 2025-04 |
| Weights | Open | Open |
| Context window | 1M | 262K |
| Max output | 131K | 66K |
| Input $ / M tokens | $1.40 | $1.50 |
| Output $ / M tokens | $4.40 | $7.50 |
| Results tracked | 50 | 25 |
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-Coder 480B-A35B Instruct: 36.2 (#199)
| Benchmark | GLM-5.2 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena WebDev | 1604 | 1275 |
| WeirdML | 70.1% | 41.2% |
| LMArena Coding | 1484 | 1413 |
| ALE-Bench | 1,047 | 461.45 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| SWE-bench Verified (bash only) | — | 55.4% |
| SciCode | 50.5% | — |
| GSO | — | 4.9% |
| AlgoTune | — | 1.44 |
Agentic & Tool Use GLM-5.2 leads
GLM-5.2: 31.6 (#63), Qwen3-Coder 480B-A35B Instruct: 23.7 (#116)
| Benchmark | GLM-5.2 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Terminal-Bench | — | 27.2% |
| 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-Coder 480B-A35B Instruct: 24.7 (#141)
| Benchmark | GLM-5.2 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Kagi LLM Benchmark | 62.6% | 49.5% |
| LMArena Hard Prompts | 1480 | 1372 |
| ARC-AGI-2 | 22.8% | — |
| SimpleBench | 58.8% | — |
| NYT Connections (extended) | 74.3% | — |
| ARC-AGI-1 | 77% | — |
| CritPt | 20.9% | — |
| Chess Puzzles | 21% | — |
| EBR-Bench | 9.5% | — |
| Mystery Game Puzzles | 19% | — |
| DTBench | 93.6% | — |
| LMCA | 45.8% | — |
| Surface Evolver Bench | 55.6% | — |
| Epoch Capabilities Index | 151.76 | — |
Math GLM-5.2 leads
GLM-5.2: 55.4 (#35), Qwen3-Coder 480B-A35B Instruct: 34.9 (#151)
| Benchmark | GLM-5.2 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Math | 1482 | 1364 |
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| OTIS Mock AIME 2024-2025 | 86.4% | — |
| ProofBench | 35% | — |
Knowledge GLM-5.2 leads
GLM-5.2: 57.1 (#36), Qwen3-Coder 480B-A35B Instruct: 36.4 (#159)
| Benchmark | GLM-5.2 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Expert | 1484 | 1337 |
| GPQA Diamond | 91.9% | — |
| SimpleQA Verified | 34.2% | — |
Multilingual GLM-5.2 leads
GLM-5.2: 56.0 (#25), Qwen3-Coder 480B-A35B Instruct: 47.8 (#143)
| Benchmark | GLM-5.2 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Non-English | 1459 | 1345 |
| LMArena Chinese | 1518 | 1356 |
| LMArena French | 1476 | 1394 |
| LMArena German | 1470 | 1324 |
| LMArena Japanese | 1451 | 1309 |
| LMArena Korean | 1445 | 1307 |
| LMArena Russian | 1467 | 1367 |
| LMArena Spanish | 1477 | 1360 |
Instruction Following GLM-5.2 leads
GLM-5.2: 77.1 (#32), Qwen3-Coder 480B-A35B Instruct: 71.8 (#142)
| Benchmark | GLM-5.2 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Instruction Following | 1465 | 1356 |
Long Context GLM-5.2 leads
GLM-5.2: 45.3 (#43), Qwen3-Coder 480B-A35B Instruct: 42.0 (#126)
| Benchmark | GLM-5.2 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Longer Query | 1479 | 1379 |
Writing & Preference GLM-5.2 leads
GLM-5.2: 70.7 (#20), Qwen3-Coder 480B-A35B Instruct: 55.5 (#142)
| Benchmark | GLM-5.2 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Text | 1470 | 1356 |
| LMArena Creative Writing | 1465 | 1333 |
| LMArena Multi-Turn | 1468 | 1365 |
| EQ-Bench Creative Writing | 1757 | — |
| EQ-Bench 4 | 1222 | — |
Frequently asked questions
Is GLM-5.2 better than Qwen3-Coder 480B-A35B Instruct?
GLM-5.2 is the stronger model overall, scoring 50.9 to 37.7 on the Noometry Index.
Which is cheaper, GLM-5.2 or Qwen3-Coder 480B-A35B Instruct?
GLM-5.2 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; Qwen3-Coder 480B-A35B Instruct lists at $1.50 and $7.50.
Is GLM-5.2 or Qwen3-Coder 480B-A35B Instruct better for coding?
GLM-5.2 scores higher on coding benchmarks: 50.9 versus 36.2 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-Coder 480B-A35B Instruct share?
21 benchmarks have published results for both models. GLM-5.2 has 50 scored results on Noometry and Qwen3-Coder 480B-A35B Instruct has 25.