Model comparison
GLM-4.6 vs Qwen3-30B-A3B
GLM-4.6 is the stronger model overall, scoring 41.4 to 38.9 on the Noometry Index. Qwen3-30B-A3B costs 4.7× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
Alibaba (Qwen)Summary
- They share 21 benchmarks with published results for both. GLM-4.6 scores higher in 8 categories and Qwen3-30B-A3B in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in long context, where GLM-4.6 leads 43.4 to 31.0.
- The biggest single-benchmark swing is Berkeley Function Calling Leaderboard: 72.4% for GLM-4.6 and 41.4% for Qwen3-30B-A3B.
- Qwen3-30B-A3B is cheaper at $0.12 / $0.50 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
- GLM-4.6 accepts more context: 205K tokens versus 41K.
Side by side
| GLM-4.6 | Qwen3-30B-A3B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 41.4 | 38.9 |
| Released | 2025-09-30 | 2025-04-28 |
| Weights | Open | Open |
| Context window | 205K | 41K |
| Max output | 131K | 16K |
| Input $ / M tokens | $0.60 | $0.12 |
| Output $ / M tokens | $2.20 | $0.50 |
| Results tracked | 29 | 32 |
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Category by category
Coding GLM-4.6 leads
GLM-4.6: 40.1 (#148), Qwen3-30B-A3B: 37.5 (#194)
| Benchmark | GLM-4.6 | Qwen3-30B-A3B |
|---|---|---|
| SciCode | 38.4% | 33.3% |
| LMArena Coding | 1449 | 1416 |
| SWE-bench Verified (bash only) | 55.4% | — |
| LMArena WebDev | 1340 | — |
| WeirdML | — | 29.8% |
| ALE-Bench | 340.82 | — |
Agentic & Tool Use GLM-4.6 leads
GLM-4.6: 32.3 (#66), Qwen3-30B-A3B: 29.8 (#82)
| Benchmark | GLM-4.6 | Qwen3-30B-A3B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 72.4% | 41.4% |
| Terminal-Bench | 24.5% | — |
Reasoning GLM-4.6 leads
GLM-4.6: 23.7 (#172), Qwen3-30B-A3B: 22.2 (#204)
| Benchmark | GLM-4.6 | Qwen3-30B-A3B |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | 54.9% |
| CritPt | 1.1% | 0.3% |
| LMArena Hard Prompts | 1440 | 1398 |
| Chess Puzzles | — | 8% |
| DTBench | — | 69.3% |
| LMCA | — | 22.4% |
| Epoch Capabilities Index | — | 139.63 |
Math GLM-4.6 leads
GLM-4.6: 39.1 (#111), Qwen3-30B-A3B: 37.4 (#157)
| Benchmark | GLM-4.6 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Math | 1432 | 1394 |
| MathArena Final-Answer Competitions | — | 47.8% |
| OTIS Mock AIME 2024-2025 | — | 70.3% |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Qwen3-30B-A3B leads
GLM-4.6: 40.2 (#124), Qwen3-30B-A3B: 41.8 (#105)
| Benchmark | GLM-4.6 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Expert | 1431 | 1396 |
| GPQA Diamond | — | 70.1% |
| Confabulations | — | 12.3% |
| Vectara Hallucination Rate | 9.5% | — |
Multilingual GLM-4.6 leads
GLM-4.6: 53.5 (#66), Qwen3-30B-A3B: 49.5 (#132)
| Benchmark | GLM-4.6 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Non-English | 1426 | 1372 |
| LMArena Chinese | 1499 | 1433 |
| LMArena French | 1459 | 1418 |
| LMArena German | 1447 | 1380 |
| LMArena Japanese | 1393 | 1337 |
| LMArena Korean | 1400 | 1331 |
| LMArena Russian | 1419 | 1370 |
| LMArena Spanish | 1436 | 1404 |
Instruction Following GLM-4.6 leads
GLM-4.6: 74.3 (#98), Qwen3-30B-A3B: 72.0 (#142)
| Benchmark | GLM-4.6 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Instruction Following | 1410 | 1363 |
Long Context GLM-4.6 leads
GLM-4.6: 43.4 (#94), Qwen3-30B-A3B: 31.0 (#283)
| Benchmark | GLM-4.6 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Longer Query | 1422 | 1379 |
| Fiction.LiveBench | — | 40.6% |
Writing & Preference GLM-4.6 leads
GLM-4.6: 61.1 (#90), Qwen3-30B-A3B: 55.6 (#143)
| Benchmark | GLM-4.6 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Text | 1440 | 1384 |
| LMArena Creative Writing | 1411 | 1317 |
| LMArena Multi-Turn | 1427 | 1378 |
| Short-Story Creative Writing | — | 75.3% |
| EQ-Bench Creative Writing | 1411 | — |
Frequently asked questions
Is GLM-4.6 better than Qwen3-30B-A3B?
GLM-4.6 is the stronger model overall, scoring 41.4 to 38.9 on the Noometry Index. Qwen3-30B-A3B costs 4.7× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.
Which is cheaper, GLM-4.6 or Qwen3-30B-A3B?
Qwen3-30B-A3B is cheaper. It lists at $0.12 per million input tokens and $0.50 per million output tokens; GLM-4.6 lists at $0.60 and $2.20.
Is GLM-4.6 or Qwen3-30B-A3B better for coding?
GLM-4.6 scores higher on coding benchmarks: 40.1 versus 37.5 in the Noometry coding category.
Which has the bigger context window?
GLM-4.6 does, with 205K tokens against 41K.
How many benchmarks do GLM-4.6 and Qwen3-30B-A3B share?
21 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Qwen3-30B-A3B has 32.