Model comparison
GLM-5 vs Qwen3-1.7B
GLM-5 is the stronger model overall, scoring 45.9 to 26.2 on the Noometry Index.
Last verified . 3 shared benchmarks.
Alibaba (Qwen)Summary
- They share 3 benchmarks with published results for both. GLM-5 scores higher in 4 categories and Qwen3-1.7B in 0 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5 leads 52.2 to 19.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 80% for GLM-5 and 8.1% for Qwen3-1.7B.
Side by side
| GLM-5 | Qwen3-1.7B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 45.9 | 26.2 |
| Released | 2026-02-11 | 2025-04-29 |
| Weights | Open | Open |
| Context window | 205K | — |
| Max output | 131K | — |
| Input $ / M tokens | $1 | — |
| Output $ / M tokens | $3.20 | — |
| Results tracked | 45 | 4 |
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Category by category
Coding Not comparable
GLM-5: 49.0 (#47), Qwen3-1.7B: —
| Benchmark | GLM-5 | Qwen3-1.7B |
|---|---|---|
| SWE-bench Verified | 72.1% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| LMArena WebDev | 1434 | — |
| SWE-bench Multilingual | 69.7% | — |
| WeirdML | 48.2% | — |
| LMArena Coding | 1461 | — |
| ALE-Bench | 765.62 | — |
Agentic & Tool Use GLM-5 leads
GLM-5: 30.9 (#65), Qwen3-1.7B: 24.5 (#108)
| Benchmark | GLM-5 | Qwen3-1.7B |
|---|---|---|
| Terminal-Bench | 52.4% | — |
| Berkeley Function Calling Leaderboard | — | 28.4% |
| 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 (#100), Qwen3-1.7B: 18.5 (#263)
| Benchmark | GLM-5 | Qwen3-1.7B |
|---|---|---|
| Chess Puzzles | 10% | 0% |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 53.2% | — |
| Kagi LLM Benchmark | 75% | — |
| NYT Connections (extended) | 74.8% | — |
| ARC-AGI-1 | 44.7% | — |
| LMArena Hard Prompts | 1451 | — |
| Epoch Capabilities Index | 145.84 | — |
| ForecastBench | 61 | — |
Math GLM-5 leads
GLM-5: 44.8 (#66), Qwen3-1.7B: 15.4 (#288)
| Benchmark | GLM-5 | Qwen3-1.7B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 80% | 8.1% |
| MathArena Final-Answer Competitions | 65.7% | — |
| LMArena Math | 1440 | — |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GLM-5 leads
GLM-5: 52.2 (#59), Qwen3-1.7B: 19.6 (#273)
| Benchmark | GLM-5 | Qwen3-1.7B |
|---|---|---|
| GPQA Diamond | 87.8% | 38% |
| Vectara Hallucination Rate | 10.1% | — |
| LMArena Expert | 1454 | — |
Multilingual Not comparable
GLM-5: 53.9 (#57), Qwen3-1.7B: —
| Benchmark | GLM-5 | Qwen3-1.7B |
|---|---|---|
| LMArena Non-English | 1430 | — |
| LMArena Chinese | 1511 | — |
| LMArena French | 1455 | — |
| LMArena German | 1445 | — |
| LMArena Japanese | 1416 | — |
| LMArena Korean | 1421 | — |
| LMArena Russian | 1436 | — |
| LMArena Spanish | 1452 | — |
Instruction Following Not comparable
GLM-5: 75.4 (#64), Qwen3-1.7B: —
| Benchmark | GLM-5 | Qwen3-1.7B |
|---|---|---|
| LMArena Instruction Following | 1427 | — |
Long Context Not comparable
GLM-5: 44.6 (#58), Qwen3-1.7B: —
| Benchmark | GLM-5 | Qwen3-1.7B |
|---|---|---|
| CL-bench | 18.7% | — |
| LMArena Longer Query | 1446 | — |
Writing & Preference Not comparable
GLM-5: 66.2 (#38), Qwen3-1.7B: —
| Benchmark | GLM-5 | Qwen3-1.7B |
|---|---|---|
| LMArena Text | 1446 | — |
| LMArena Creative Writing | 1438 | — |
| EQ-Bench Creative Writing | 1601 | — |
| LMArena Multi-Turn | 1456 | — |
Frequently asked questions
Is GLM-5 better than Qwen3-1.7B?
GLM-5 is the stronger model overall, scoring 45.9 to 26.2 on the Noometry Index.
How many benchmarks do GLM-5 and Qwen3-1.7B share?
3 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and Qwen3-1.7B has 4.