Model comparison
GLM-4.7 vs Qwen3-Coder 480B-A35B Instruct
GLM-4.7 is the stronger model overall, scoring 42.0 to 38.1 on the Noometry Index.
Last verified . 20 shared benchmarks.
Alibaba (Qwen)Summary
- They share 20 benchmarks with published results for both. GLM-4.7 scores higher in 8 categories and Qwen3-Coder 480B-A35B Instruct in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-4.7 leads 47.0 to 37.0.
- The biggest single-benchmark swing is Terminal-Bench: 33.4% for GLM-4.7 and 27.2% for Qwen3-Coder 480B-A35B Instruct.
- GLM-4.7 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-4.7 | Qwen3-Coder 480B-A35B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 42.0 | 38.1 |
| Released | 2025-12-22 | 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 | 36 | 25 |
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Category by category
Coding GLM-4.7 leads
GLM-4.7: 44.0 (#79), Qwen3-Coder 480B-A35B Instruct: 35.5 (#223)
| Benchmark | GLM-4.7 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena WebDev | 1435 | 1275 |
| LMArena Coding | 1454 | 1412 |
| ALE-Bench | 399.48 | 461.45 |
| SWE-bench Verified (bash only) | — | 55.4% |
| SciCode | 45.1% | — |
| GSO | — | 4.9% |
| WeirdML | — | 41.2% |
| AlgoTune | — | 1.44 |
Agentic & Tool Use GLM-4.7 leads
GLM-4.7: 26.5 (#103), Qwen3-Coder 480B-A35B Instruct: 23.9 (#123)
| Benchmark | GLM-4.7 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Terminal-Bench | 33.4% | 27.2% |
| Vending-Bench 2 | 2,377 | — |
Reasoning Qwen3-Coder 480B-A35B Instruct leads
GLM-4.7: 24.3 (#164), Qwen3-Coder 480B-A35B Instruct: 25.5 (#149)
| Benchmark | GLM-4.7 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1443 | 1372 |
| SimpleBench | 47.7% | — |
| Kagi LLM Benchmark | — | 49.5% |
| CritPt | 1.7% | — |
| Chess Puzzles | 6% | — |
| Epoch Capabilities Index | 143.51 | — |
Math Too close to call
GLM-4.7: 38.6 (#135), Qwen3-Coder 480B-A35B Instruct: 37.6 (#150)
| Benchmark | GLM-4.7 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Math | 1423 | 1365 |
| OTIS Mock AIME 2024-2025 | 83.3% | — |
| ProofBench | 6% | — |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge GLM-4.7 leads
GLM-4.7: 47.0 (#80), Qwen3-Coder 480B-A35B Instruct: 37.0 (#162)
| Benchmark | GLM-4.7 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Expert | 1424 | 1338 |
| GPQA Diamond | 83.3% | — |
| SimpleQA Verified | 32.2% | — |
| Vectara Hallucination Rate | 11.7% | — |
Multilingual GLM-4.7 leads
GLM-4.7: 52.8 (#79), Qwen3-Coder 480B-A35B Instruct: 47.7 (#148)
| Benchmark | GLM-4.7 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Non-English | 1417 | 1346 |
| LMArena Chinese | 1495 | 1357 |
| LMArena French | 1432 | 1398 |
| LMArena German | 1424 | 1325 |
| LMArena Japanese | 1439 | 1310 |
| LMArena Korean | 1399 | 1305 |
| LMArena Russian | 1423 | 1366 |
| LMArena Spanish | 1434 | 1360 |
Instruction Following GLM-4.7 leads
GLM-4.7: 74.4 (#95), Qwen3-Coder 480B-A35B Instruct: 71.6 (#147)
| Benchmark | GLM-4.7 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Instruction Following | 1411 | 1355 |
Long Context Too close to call
GLM-4.7: 42.8 (#116), Qwen3-Coder 480B-A35B Instruct: 42.0 (#131)
| Benchmark | GLM-4.7 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Longer Query | 1432 | 1378 |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
Writing & Preference GLM-4.7 leads
GLM-4.7: 60.9 (#93), Qwen3-Coder 480B-A35B Instruct: 55.3 (#147)
| Benchmark | GLM-4.7 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Text | 1435 | 1357 |
| LMArena Creative Writing | 1401 | 1333 |
| LMArena Multi-Turn | 1446 | 1365 |
| EQ-Bench Creative Writing | 1413 | — |
Frequently asked questions
Is GLM-4.7 better than Qwen3-Coder 480B-A35B Instruct?
GLM-4.7 is the stronger model overall, scoring 42.0 to 38.1 on the Noometry Index.
Which is cheaper, GLM-4.7 or Qwen3-Coder 480B-A35B Instruct?
GLM-4.7 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-4.7 or Qwen3-Coder 480B-A35B Instruct better for coding?
GLM-4.7 scores higher on coding benchmarks: 44.0 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-4.7 and Qwen3-Coder 480B-A35B Instruct share?
20 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Qwen3-Coder 480B-A35B Instruct has 25.