Model comparison
GLM-5.3-Flash vs Qwen3-Coder 480B-A35B Instruct
GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 37.7 on the Noometry Index.
Last verified . 19 shared benchmarks.
Alibaba (Qwen)Summary
- They share 19 benchmarks with published results for both. GLM-5.3-Flash 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-5.3-Flash leads 58.4 to 36.4.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $1.50 / $7.50 for Qwen3-Coder 480B-A35B Instruct.
- GLM-5.3-Flash accepts more context: 1M tokens versus 262K.
Side by side
| GLM-5.3-Flash | Qwen3-Coder 480B-A35B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 49.6 | 37.7 |
| Released | 2026-08-20 | 2025-04 |
| Weights | Open | Open |
| Context window | 1M | 262K |
| Max output | 131K | 66K |
| Input $ / M tokens | $0.15 | $1.50 |
| Output $ / M tokens | $0.50 | $7.50 |
| Results tracked | 36 | 25 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.7 (#26), Qwen3-Coder 480B-A35B Instruct: 36.2 (#199)
| Benchmark | GLM-5.3-Flash | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena WebDev | 1614 | 1275 |
| LMArena Coding | 1506 | 1413 |
| ALE-Bench | 303.55 | 461.45 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| SWE-bench Verified (bash only) | — | 55.4% |
| SciCode | 51.6% | — |
| GSO | — | 4.9% |
| WeirdML | — | 41.2% |
| AlgoTune | — | 1.44 |
Agentic & Tool Use GLM-5.3-Flash leads
GLM-5.3-Flash: 34.3 (#41), Qwen3-Coder 480B-A35B Instruct: 23.7 (#116)
| Benchmark | GLM-5.3-Flash | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Terminal-Bench | — | 27.2% |
| APEX-Agents | 52.8% | — |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 31.7 (#69), Qwen3-Coder 480B-A35B Instruct: 24.7 (#141)
| Benchmark | GLM-5.3-Flash | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1490 | 1372 |
| Kagi LLM Benchmark | — | 49.5% |
| CritPt | 15.4% | — |
| Chess Puzzles | 14% | — |
| Mystery Game Puzzles | 8% | — |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| Epoch Capabilities Index | 151.9 | — |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#38), Qwen3-Coder 480B-A35B Instruct: 34.9 (#151)
| Benchmark | GLM-5.3-Flash | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Math | 1499 | 1364 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| OTIS Mock AIME 2024-2025 | 93.9% | — |
| ProofBench | 21% | — |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#33), Qwen3-Coder 480B-A35B Instruct: 36.4 (#159)
| Benchmark | GLM-5.3-Flash | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Expert | 1512 | 1337 |
| GPQA Diamond | 90.2% | — |
Multimodal Not comparable
GLM-5.3-Flash: 42.6 (#25), Qwen3-Coder 480B-A35B Instruct: —
| Benchmark | GLM-5.3-Flash | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Vision | 1304 | — |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.2 (#24), Qwen3-Coder 480B-A35B Instruct: 47.8 (#143)
| Benchmark | GLM-5.3-Flash | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Non-English | 1462 | 1345 |
| LMArena Chinese | 1529 | 1356 |
| LMArena French | 1495 | 1394 |
| LMArena German | 1457 | 1324 |
| LMArena Japanese | 1420 | 1309 |
| LMArena Korean | 1443 | 1307 |
| LMArena Russian | 1472 | 1367 |
| LMArena Spanish | 1471 | 1360 |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.8 (#17), Qwen3-Coder 480B-A35B Instruct: 71.8 (#142)
| Benchmark | GLM-5.3-Flash | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Instruction Following | 1480 | 1356 |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.3 (#41), Qwen3-Coder 480B-A35B Instruct: 42.0 (#126)
| Benchmark | GLM-5.3-Flash | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Longer Query | 1479 | 1379 |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.4 (#49), Qwen3-Coder 480B-A35B Instruct: 55.5 (#142)
| Benchmark | GLM-5.3-Flash | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Text | 1471 | 1356 |
| LMArena Creative Writing | 1440 | 1333 |
| LMArena Multi-Turn | 1471 | 1365 |
Frequently asked questions
Is GLM-5.3-Flash better than Qwen3-Coder 480B-A35B Instruct?
GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 37.7 on the Noometry Index.
Which is cheaper, GLM-5.3-Flash or Qwen3-Coder 480B-A35B Instruct?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Qwen3-Coder 480B-A35B Instruct lists at $1.50 and $7.50.
Is GLM-5.3-Flash or Qwen3-Coder 480B-A35B Instruct better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 36.2 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3-Flash does, with 1M tokens against 262K.
How many benchmarks do GLM-5.3-Flash and Qwen3-Coder 480B-A35B Instruct share?
19 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and Qwen3-Coder 480B-A35B Instruct has 25.