Model comparison
GLM-4.6 vs Qwen3-Coder 480B-A35B Instruct
GLM-4.6 is the stronger model overall, scoring 40.9 to 37.7 on the Noometry Index.
Last verified . 22 shared benchmarks.
Alibaba (Qwen)Summary
- They share 22 benchmarks with published results for both. GLM-4.6 scores higher in 8 categories and Qwen3-Coder 480B-A35B Instruct in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where GLM-4.6 leads 32.1 to 23.7.
- GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $1.50 / $7.50 for Qwen3-Coder 480B-A35B Instruct.
- Qwen3-Coder 480B-A35B Instruct accepts more context: 262K tokens versus 205K.
Side by side
| GLM-4.6 | Qwen3-Coder 480B-A35B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 40.9 | 37.7 |
| Released | 2025-09-30 | 2025-04 |
| Weights | Open | Open |
| Context window | 205K | 262K |
| Max output | 131K | 66K |
| Input $ / M tokens | $0.60 | $1.50 |
| Output $ / M tokens | $2.20 | $7.50 |
| Results tracked | 29 | 25 |
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Category by category
Coding GLM-4.6 leads
GLM-4.6: 39.9 (#137), Qwen3-Coder 480B-A35B Instruct: 36.2 (#199)
| Benchmark | GLM-4.6 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| SWE-bench Verified (bash only) | 55.4% | 55.4% |
| LMArena WebDev | 1339 | 1275 |
| LMArena Coding | 1449 | 1413 |
| ALE-Bench | 340.82 | 461.45 |
| SciCode | 38.4% | — |
| GSO | — | 4.9% |
| WeirdML | — | 41.2% |
| AlgoTune | — | 1.44 |
Agentic & Tool Use GLM-4.6 leads
GLM-4.6: 32.1 (#60), Qwen3-Coder 480B-A35B Instruct: 23.7 (#116)
| Benchmark | GLM-4.6 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Terminal-Bench | 24.5% | 27.2% |
| Berkeley Function Calling Leaderboard | 72.4% | — |
Reasoning Qwen3-Coder 480B-A35B Instruct leads
GLM-4.6: 23.1 (#168), Qwen3-Coder 480B-A35B Instruct: 24.7 (#141)
| Benchmark | GLM-4.6 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | 49.5% |
| LMArena Hard Prompts | 1440 | 1372 |
| CritPt | 1.1% | — |
Math GLM-4.6 leads
GLM-4.6: 36.4 (#115), Qwen3-Coder 480B-A35B Instruct: 34.9 (#151)
| Benchmark | GLM-4.6 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Math | 1431 | 1364 |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GLM-4.6 leads
GLM-4.6: 39.6 (#118), Qwen3-Coder 480B-A35B Instruct: 36.4 (#159)
| Benchmark | GLM-4.6 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Expert | 1432 | 1337 |
| Vectara Hallucination Rate | 9.5% | — |
Multilingual GLM-4.6 leads
GLM-4.6: 53.6 (#62), Qwen3-Coder 480B-A35B Instruct: 47.8 (#143)
| Benchmark | GLM-4.6 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Non-English | 1426 | 1345 |
| LMArena Chinese | 1500 | 1356 |
| LMArena French | 1459 | 1394 |
| LMArena German | 1447 | 1324 |
| LMArena Japanese | 1394 | 1309 |
| LMArena Korean | 1401 | 1307 |
| LMArena Russian | 1418 | 1367 |
| LMArena Spanish | 1436 | 1360 |
Instruction Following GLM-4.6 leads
GLM-4.6: 74.5 (#93), Qwen3-Coder 480B-A35B Instruct: 71.8 (#142)
| Benchmark | GLM-4.6 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Instruction Following | 1410 | 1356 |
Long Context GLM-4.6 leads
GLM-4.6: 43.4 (#91), Qwen3-Coder 480B-A35B Instruct: 42.0 (#126)
| Benchmark | GLM-4.6 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Longer Query | 1423 | 1379 |
Writing & Preference GLM-4.6 leads
GLM-4.6: 61.3 (#86), Qwen3-Coder 480B-A35B Instruct: 55.5 (#142)
| Benchmark | GLM-4.6 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Text | 1440 | 1356 |
| LMArena Creative Writing | 1411 | 1333 |
| LMArena Multi-Turn | 1427 | 1365 |
| EQ-Bench Creative Writing | 1411 | — |
Frequently asked questions
Is GLM-4.6 better than Qwen3-Coder 480B-A35B Instruct?
GLM-4.6 is the stronger model overall, scoring 40.9 to 37.7 on the Noometry Index.
Which is cheaper, GLM-4.6 or Qwen3-Coder 480B-A35B Instruct?
GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Qwen3-Coder 480B-A35B Instruct lists at $1.50 and $7.50.
Is GLM-4.6 or Qwen3-Coder 480B-A35B Instruct better for coding?
GLM-4.6 scores higher on coding benchmarks: 39.9 versus 36.2 in the Noometry coding category.
Which has the bigger context window?
Qwen3-Coder 480B-A35B Instruct does, with 262K tokens against 205K.
How many benchmarks do GLM-4.6 and Qwen3-Coder 480B-A35B Instruct share?
22 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Qwen3-Coder 480B-A35B Instruct has 25.