Model comparison
GLM-5.3 vs Qwen3.5-Flash
GLM-5.3 is the stronger model overall, scoring 54.8 to 42.5 on the Noometry Index. Qwen3.5-Flash costs 12× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Last verified . 29 shared benchmarks.
Alibaba (Qwen)Summary
- They share 29 benchmarks with published results for both. GLM-5.3 scores higher in 8 categories and Qwen3.5-Flash in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where GLM-5.3 leads 59.5 to 34.2.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 68.8% for GLM-5.3 and 18.2% for Qwen3.5-Flash.
- Qwen3.5-Flash is cheaper at $0.10 / $0.40 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.
- GLM-5.3 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3 | Qwen3.5-Flash | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 54.8 | 42.5 |
| Released | 2026-08-14 | 2026-02-23 |
| Weights | Open | Proprietary |
| Context window | 1M | 1M |
| Max output | 131K | 66K |
| Input $ / M tokens | $1.40 | $0.10 |
| Output $ / M tokens | $4.40 | $0.40 |
| Results tracked | 42 | 32 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.5 (#14), Qwen3.5-Flash: 34.2 (#242)
| Benchmark | GLM-5.3 | Qwen3.5-Flash |
|---|---|---|
| LMArena WebDev | 1622 | 1244 |
| LMArena Coding | 1496 | 1412 |
| ALE-Bench | 1,317 | 221.8 |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| CursorBench | 42.6% | — |
| FrontierSWE | 30.2% | — |
| SciCode | 59% | — |
| WeirdML | 75.4% | — |
Agentic & Tool Use Not comparable
GLM-5.3: 36.4 (#38), Qwen3.5-Flash: —
| Benchmark | GLM-5.3 | Qwen3.5-Flash |
|---|---|---|
| Vending-Bench 2 | 8,164 | 462.69 |
| APEX-Agents | 56.6% | — |
Reasoning GLM-5.3 leads
GLM-5.3: 46.1 (#46), Qwen3.5-Flash: 33.7 (#72)
| Benchmark | GLM-5.3 | Qwen3.5-Flash |
|---|---|---|
| Chess Puzzles | 21% | 21% |
| LMArena Hard Prompts | 1489 | 1403 |
| Mystery Game Puzzles | 33% | 20% |
| DTBench | 87.7% | 82.9% |
| LMCA | 55.5% | 29.1% |
| Epoch Capabilities Index | 155.61 | 143.98 |
| NYT Connections (extended) | 74.2% | — |
| CritPt | 19.1% | — |
| Bench to the Future 3 | 0.15 | — |
Math GLM-5.3 leads
GLM-5.3: 62.3 (#33), Qwen3.5-Flash: 37.4 (#158)
| Benchmark | GLM-5.3 | Qwen3.5-Flash |
|---|---|---|
| FrontierMath (Tiers 1-3) | 68.8% | 18.2% |
| OTIS Mock AIME 2024-2025 | 91.1% | 84.4% |
| LMArena Math | 1489 | 1407 |
| FrontierMath Tier 4 | 29.3% | — |
| ProofBench | 49% | — |
| FrontierMath (Feb 2025 set) | — | 6.2% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge GLM-5.3 leads
GLM-5.3: 58.3 (#37), Qwen3.5-Flash: 43.2 (#93)
| Benchmark | GLM-5.3 | Qwen3.5-Flash |
|---|---|---|
| GPQA Diamond | 90.9% | 82.3% |
| SimpleQA Verified | 41% | 20.3% |
| LMArena Expert | 1516 | 1407 |
| Vectara Hallucination Rate | — | 10.5% |
Multilingual GLM-5.3 leads
GLM-5.3: 55.7 (#28), Qwen3.5-Flash: 50.5 (#121)
| Benchmark | GLM-5.3 | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | 1457 | 1385 |
| LMArena Chinese | 1528 | 1446 |
| LMArena French | 1499 | 1412 |
| LMArena German | 1499 | 1390 |
| LMArena Japanese | 1453 | 1368 |
| LMArena Korean | 1472 | 1344 |
| LMArena Russian | 1463 | 1379 |
| LMArena Spanish | 1460 | 1400 |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.5 (#23), Qwen3.5-Flash: 72.6 (#139)
| Benchmark | GLM-5.3 | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | 1477 | 1374 |
Long Context GLM-5.3 leads
GLM-5.3: 45.4 (#41), Qwen3.5-Flash: 42.4 (#124)
| Benchmark | GLM-5.3 | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | 1482 | 1392 |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.7 (#6), Qwen3.5-Flash: 57.9 (#122)
| Benchmark | GLM-5.3 | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | 1471 | 1397 |
| LMArena Creative Writing | 1457 | 1343 |
| LMArena Multi-Turn | 1472 | 1393 |
| EQ-Bench Creative Writing | 2075 | — |
Frequently asked questions
Is GLM-5.3 better than Qwen3.5-Flash?
GLM-5.3 is the stronger model overall, scoring 54.8 to 42.5 on the Noometry Index. Qwen3.5-Flash costs 12× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Which is cheaper, GLM-5.3 or Qwen3.5-Flash?
Qwen3.5-Flash is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; GLM-5.3 lists at $1.40 and $4.40.
Is GLM-5.3 or Qwen3.5-Flash better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 34.2 in the Noometry coding category.
Which has the bigger context window?
Both accept 1M tokens.
How many benchmarks do GLM-5.3 and Qwen3.5-Flash share?
29 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and Qwen3.5-Flash has 32.