Model comparison
GLM-5 vs Qwen3.5-Flash
GLM-5 is the stronger model overall, scoring 46.1 to 42.5 on the Noometry Index. Qwen3.5-Flash costs 8.9× less per token, which makes it the better buy when GLM-5's lead doesn't matter for your workload.
Last verified . 27 shared benchmarks.
Alibaba (Qwen)Summary
- They share 27 benchmarks with published results for both. GLM-5 scores higher in 7 categories and Qwen3.5-Flash in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where GLM-5 leads 49.0 to 34.2.
- The biggest single-benchmark swing is Chess Puzzles: 10% for GLM-5 and 21% for Qwen3.5-Flash.
- Qwen3.5-Flash is cheaper at $0.10 / $0.40 per million input/output tokens, against $1 / $3.20 for GLM-5.
- Qwen3.5-Flash accepts more context: 1M tokens versus 205K.
- GLM-5 has downloadable open weights; the other is API-only.
Side by side
| GLM-5 | Qwen3.5-Flash | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 46.1 | 42.5 |
| Released | 2026-02-11 | 2026-02-23 |
| Weights | Open | Proprietary |
| Context window | 205K | 1M |
| Max output | 131K | 66K |
| Input $ / M tokens | $1 | $0.10 |
| Output $ / M tokens | $3.20 | $0.40 |
| Results tracked | 45 | 32 |
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Category by category
Coding GLM-5 leads
GLM-5: 49.0 (#52), Qwen3.5-Flash: 34.2 (#242)
| Benchmark | GLM-5 | Qwen3.5-Flash |
|---|---|---|
| LMArena WebDev | 1434 | 1244 |
| LMArena Coding | 1461 | 1412 |
| ALE-Bench | 765.62 | 221.8 |
| SWE-bench Verified | 72.1% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| SWE-bench Multilingual | 69.7% | — |
| WeirdML | 48.2% | — |
Agentic & Tool Use Not comparable
GLM-5: 31.1 (#71), Qwen3.5-Flash: —
| Benchmark | GLM-5 | Qwen3.5-Flash |
|---|---|---|
| Vending-Bench 2 | 4,432 | 462.69 |
| Terminal-Bench | 52.4% | — |
| τ²-bench Airline | 82.5% | — |
| τ²-bench Banking | 9.8% | — |
| τ²-bench Retail | 73.7% | — |
| τ²-bench Telecom | 86.8% | — |
Reasoning Qwen3.5-Flash leads
GLM-5: 27.6 (#116), Qwen3.5-Flash: 33.7 (#72)
| Benchmark | GLM-5 | Qwen3.5-Flash |
|---|---|---|
| Chess Puzzles | 10% | 21% |
| LMArena Hard Prompts | 1452 | 1403 |
| Epoch Capabilities Index | 145.83 | 143.98 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 53.2% | — |
| Kagi LLM Benchmark | 75% | — |
| NYT Connections (extended) | 74.8% | — |
| ARC-AGI-1 | 44.7% | — |
| Mystery Game Puzzles | — | 20% |
| DTBench | — | 82.9% |
| LMCA | — | 29.1% |
| ForecastBench | 61 | — |
Math GLM-5 leads
GLM-5: 46.4 (#71), Qwen3.5-Flash: 37.4 (#158)
| Benchmark | GLM-5 | Qwen3.5-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 80% | 84.4% |
| LMArena Math | 1440 | 1407 |
| FrontierMath (Feb 2025 set) | 16.4% | 6.2% |
| FrontierMath Tier 4 (v1) | 2.1% | 0% |
| FrontierMath (Tiers 1-3) | — | 18.2% |
| MathArena Final-Answer Competitions | 65.7% | — |
Knowledge GLM-5 leads
GLM-5: 52.3 (#64), Qwen3.5-Flash: 43.2 (#93)
| Benchmark | GLM-5 | Qwen3.5-Flash |
|---|---|---|
| GPQA Diamond | 87.8% | 82.3% |
| Vectara Hallucination Rate | 10.1% | 10.5% |
| LMArena Expert | 1454 | 1407 |
| SimpleQA Verified | — | 20.3% |
Multilingual GLM-5 leads
GLM-5: 53.7 (#58), Qwen3.5-Flash: 50.5 (#121)
| Benchmark | GLM-5 | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | 1430 | 1385 |
| LMArena Chinese | 1511 | 1446 |
| LMArena French | 1455 | 1412 |
| LMArena German | 1445 | 1390 |
| LMArena Japanese | 1416 | 1368 |
| LMArena Korean | 1423 | 1344 |
| LMArena Russian | 1436 | 1379 |
| LMArena Spanish | 1454 | 1400 |
Instruction Following GLM-5 leads
GLM-5: 75.2 (#67), Qwen3.5-Flash: 72.6 (#139)
| Benchmark | GLM-5 | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | 1428 | 1374 |
Long Context GLM-5 leads
GLM-5: 44.7 (#60), Qwen3.5-Flash: 42.4 (#124)
| Benchmark | GLM-5 | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | 1446 | 1392 |
| CL-bench | 18.7% | — |
Writing & Preference GLM-5 leads
GLM-5: 66.0 (#38), Qwen3.5-Flash: 57.9 (#122)
| Benchmark | GLM-5 | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | 1446 | 1397 |
| LMArena Creative Writing | 1439 | 1343 |
| LMArena Multi-Turn | 1456 | 1393 |
| EQ-Bench Creative Writing | 1601 | — |
Frequently asked questions
Is GLM-5 better than Qwen3.5-Flash?
GLM-5 is the stronger model overall, scoring 46.1 to 42.5 on the Noometry Index. Qwen3.5-Flash costs 8.9× less per token, which makes it the better buy when GLM-5's lead doesn't matter for your workload.
Which is cheaper, GLM-5 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 lists at $1 and $3.20.
Is GLM-5 or Qwen3.5-Flash better for coding?
GLM-5 scores higher on coding benchmarks: 49.0 versus 34.2 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5-Flash does, with 1M tokens against 205K.
How many benchmarks do GLM-5 and Qwen3.5-Flash share?
27 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and Qwen3.5-Flash has 32.