Model comparison
GLM-5 vs Qwen3-Coder 480B-A35B Instruct
GLM-5 is the stronger model overall, scoring 46.1 to 38.1 on the Noometry Index.
Last verified . 23 shared benchmarks.
Alibaba (Qwen)Summary
- They share 23 benchmarks with published results for both. GLM-5 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 leads 52.3 to 37.0.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 75% for GLM-5 and 49.5% for Qwen3-Coder 480B-A35B Instruct.
- GLM-5 is cheaper at $1 / $3.20 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-5 | Qwen3-Coder 480B-A35B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 46.1 | 38.1 |
| Released | 2026-02-11 | 2025-04 |
| Weights | Open | Open |
| Context window | 205K | 262K |
| Max output | 131K | 66K |
| Input $ / M tokens | $1 | $1.50 |
| Output $ / M tokens | $3.20 | $7.50 |
| Results tracked | 45 | 25 |
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Category by category
Coding GLM-5 leads
GLM-5: 49.0 (#52), Qwen3-Coder 480B-A35B Instruct: 35.5 (#223)
| Benchmark | GLM-5 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| SWE-bench Verified (bash only) | 72.8% | 55.4% |
| LMArena WebDev | 1434 | 1275 |
| WeirdML | 48.2% | 41.2% |
| LMArena Coding | 1461 | 1412 |
| ALE-Bench | 765.62 | 461.45 |
| SWE-bench Verified | 72.1% | — |
| SWE-bench Multilingual | 69.7% | — |
| GSO | — | 4.9% |
| AlgoTune | — | 1.44 |
Agentic & Tool Use GLM-5 leads
GLM-5: 31.1 (#71), Qwen3-Coder 480B-A35B Instruct: 23.9 (#123)
| Benchmark | GLM-5 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Terminal-Bench | 52.4% | 27.2% |
| τ²-bench Airline | 82.5% | — |
| τ²-bench Banking | 9.8% | — |
| τ²-bench Retail | 73.7% | — |
| τ²-bench Telecom | 86.8% | — |
| Vending-Bench 2 | 4,432 | — |
Reasoning GLM-5 leads
GLM-5: 27.6 (#116), Qwen3-Coder 480B-A35B Instruct: 25.5 (#149)
| Benchmark | GLM-5 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Kagi LLM Benchmark | 75% | 49.5% |
| LMArena Hard Prompts | 1452 | 1372 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 53.2% | — |
| NYT Connections (extended) | 74.8% | — |
| ARC-AGI-1 | 44.7% | — |
| Chess Puzzles | 10% | — |
| Epoch Capabilities Index | 145.83 | — |
| ForecastBench | 61 | — |
Math GLM-5 leads
GLM-5: 46.4 (#71), Qwen3-Coder 480B-A35B Instruct: 37.6 (#150)
| Benchmark | GLM-5 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Math | 1440 | 1365 |
| MathArena Final-Answer Competitions | 65.7% | — |
| OTIS Mock AIME 2024-2025 | 80% | — |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GLM-5 leads
GLM-5: 52.3 (#64), Qwen3-Coder 480B-A35B Instruct: 37.0 (#162)
| Benchmark | GLM-5 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Expert | 1454 | 1338 |
| GPQA Diamond | 87.8% | — |
| Vectara Hallucination Rate | 10.1% | — |
Multilingual GLM-5 leads
GLM-5: 53.7 (#58), Qwen3-Coder 480B-A35B Instruct: 47.7 (#148)
| Benchmark | GLM-5 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Non-English | 1430 | 1346 |
| LMArena Chinese | 1511 | 1357 |
| LMArena French | 1455 | 1398 |
| LMArena German | 1445 | 1325 |
| LMArena Japanese | 1416 | 1310 |
| LMArena Korean | 1423 | 1305 |
| LMArena Russian | 1436 | 1366 |
| LMArena Spanish | 1454 | 1360 |
Instruction Following GLM-5 leads
GLM-5: 75.2 (#67), Qwen3-Coder 480B-A35B Instruct: 71.6 (#147)
| Benchmark | GLM-5 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Instruction Following | 1428 | 1355 |
Long Context GLM-5 leads
GLM-5: 44.7 (#60), Qwen3-Coder 480B-A35B Instruct: 42.0 (#131)
| Benchmark | GLM-5 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Longer Query | 1446 | 1378 |
| CL-bench | 18.7% | — |
Writing & Preference GLM-5 leads
GLM-5: 66.0 (#38), Qwen3-Coder 480B-A35B Instruct: 55.3 (#147)
| Benchmark | GLM-5 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Text | 1446 | 1357 |
| LMArena Creative Writing | 1439 | 1333 |
| LMArena Multi-Turn | 1456 | 1365 |
| EQ-Bench Creative Writing | 1601 | — |
Frequently asked questions
Is GLM-5 better than Qwen3-Coder 480B-A35B Instruct?
GLM-5 is the stronger model overall, scoring 46.1 to 38.1 on the Noometry Index.
Which is cheaper, GLM-5 or Qwen3-Coder 480B-A35B Instruct?
GLM-5 is cheaper. It lists at $1 per million input tokens and $3.20 per million output tokens; Qwen3-Coder 480B-A35B Instruct lists at $1.50 and $7.50.
Is GLM-5 or Qwen3-Coder 480B-A35B Instruct better for coding?
GLM-5 scores higher on coding benchmarks: 49.0 versus 35.5 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-5 and Qwen3-Coder 480B-A35B Instruct share?
23 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and Qwen3-Coder 480B-A35B Instruct has 25.