Model comparison
GLM-4.5 vs Qwen3 235B-A22B
Qwen3 235B-A22B is the stronger model overall, scoring 43.5 to 42.0 on the Noometry Index.
Last verified . 23 shared benchmarks.
Alibaba (Qwen)Summary
- They share 23 benchmarks with published results for both. GLM-4.5 scores higher in 3 categories and Qwen3 235B-A22B in 5 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3 235B-A22B leads 49.6 to 35.9.
- The biggest single-benchmark swing is Fiction.LiveBench: 58.3% for GLM-4.5 and 75% for Qwen3 235B-A22B.
- GLM-4.5 is cheaper at $0.60 / $2.20 per million input/output tokens, against $0.70 / $2.80 for Qwen3 235B-A22B.
Side by side
| GLM-4.5 | Qwen3 235B-A22B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 42.0 | 43.5 |
| Released | 2025-07-27 | 2025-04 |
| Weights | Open | Open |
| Context window | 131K | 131K |
| Max output | 98K | 16K |
| Input $ / M tokens | $0.60 | $0.70 |
| Output $ / M tokens | $2.20 | $2.80 |
| Results tracked | 27 | 49 |
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Category by category
Coding Qwen3 235B-A22B leads
GLM-4.5: 41.4 (#125), Qwen3 235B-A22B: 44.3 (#75)
| Benchmark | GLM-4.5 | Qwen3 235B-A22B |
|---|---|---|
| WeirdML | 40.6% | 41% |
| LMArena Coding | 1434 | 1445 |
| SWE-bench Verified (bash only) | 54.2% | — |
| Aider Polyglot | — | 59.6% |
| SciCode | — | 42.4% |
| ALE-Bench | 344.82 | — |
| AlgoTune | 1.52 | — |
Agentic & Tool Use Not comparable
GLM-4.5: —, Qwen3 235B-A22B: 33.9 (#51)
| Benchmark | GLM-4.5 | Qwen3 235B-A22B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 52.1% |
| Vending-Bench 2 | — | -11.34 |
Reasoning GLM-4.5 leads
GLM-4.5: 28.6 (#100), Qwen3 235B-A22B: 15.7 (#311)
| Benchmark | GLM-4.5 | Qwen3 235B-A22B |
|---|---|---|
| Kagi LLM Benchmark | 57.9% | 69.4% |
| LMArena Hard Prompts | 1429 | 1433 |
| ARC-AGI-2 | — | 1.3% |
| SimpleBench | — | 31% |
| ARC-AGI-1 | — | 11% |
| CritPt | — | 0% |
| Chess Puzzles | — | 12% |
| Mystery Game Puzzles | — | 9% |
| DTBench | — | 80.3% |
| LMCA | — | 29.3% |
| Epoch Capabilities Index | — | 143.85 |
| ForecastBench | — | 59.7 |
Math Qwen3 235B-A22B leads
GLM-4.5: 39.0 (#116), Qwen3 235B-A22B: 50.4 (#57)
| Benchmark | GLM-4.5 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Math | 1427 | 1432 |
| OTIS Mock AIME 2024-2025 | — | 86.7% |
| Omni-MATH | — | 71.8% |
| MATH Level 5 | — | 68.9% |
| FrontierMath (Feb 2025 set) | — | 8.5% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Qwen3 235B-A22B leads
GLM-4.5: 35.9 (#179), Qwen3 235B-A22B: 49.6 (#73)
| Benchmark | GLM-4.5 | Qwen3 235B-A22B |
|---|---|---|
| Confabulations | 11.3% | 15.6% |
| LMArena Expert | 1433 | 1463 |
| GPQA Diamond | — | 80.1% |
| Humanity's Last Exam | 8.3% | — |
| SimpleQA Verified | — | 40.4% |
| MMLU-Pro | — | 84.4% |
| Vectara Hallucination Rate | — | 9.3% |
| GPQA (HELM) | — | 72.7% |
Multilingual Too close to call
GLM-4.5: 52.8 (#77), Qwen3 235B-A22B: 52.3 (#89)
| Benchmark | GLM-4.5 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Non-English | 1417 | 1409 |
| LMArena Chinese | 1465 | 1481 |
| LMArena French | 1418 | 1445 |
| LMArena German | 1407 | 1433 |
| LMArena Japanese | 1415 | 1399 |
| LMArena Korean | 1380 | 1391 |
| LMArena Russian | 1414 | 1411 |
| LMArena Spanish | 1454 | 1430 |
Instruction Following GLM-4.5 leads
GLM-4.5: 74.1 (#104), Qwen3 235B-A22B: 72.6 (#136)
| Benchmark | GLM-4.5 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Instruction Following | 1404 | 1408 |
| IFEval | — | 83.5% |
Long Context Qwen3 235B-A22B leads
GLM-4.5: 38.2 (#201), Qwen3 235B-A22B: 46.1 (#26)
| Benchmark | GLM-4.5 | Qwen3 235B-A22B |
|---|---|---|
| Fiction.LiveBench | 58.3% | 75% |
| LMArena Longer Query | 1412 | 1426 |
Writing & Preference Qwen3 235B-A22B leads
GLM-4.5: 57.5 (#127), Qwen3 235B-A22B: 59.6 (#108)
| Benchmark | GLM-4.5 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Text | 1430 | 1419 |
| LMArena Creative Writing | 1395 | 1384 |
| Short-Story Creative Writing | 73.4% | 83% |
| EQ-Bench Creative Writing | 1343 | 1366 |
| LMArena Multi-Turn | 1415 | 1432 |
| WildBench | — | 86.6% |
Frequently asked questions
Is GLM-4.5 better than Qwen3 235B-A22B?
Qwen3 235B-A22B is the stronger model overall, scoring 43.5 to 42.0 on the Noometry Index.
Which is cheaper, GLM-4.5 or Qwen3 235B-A22B?
GLM-4.5 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Qwen3 235B-A22B lists at $0.70 and $2.80.
Is GLM-4.5 or Qwen3 235B-A22B better for coding?
Qwen3 235B-A22B scores higher on coding benchmarks: 44.3 versus 41.4 in the Noometry coding category.
Which has the bigger context window?
Both accept 131K tokens.
How many benchmarks do GLM-4.5 and Qwen3 235B-A22B share?
23 benchmarks have published results for both models. GLM-4.5 has 27 scored results on Noometry and Qwen3 235B-A22B has 49.