Model comparison
GLM-5.1 vs Qwen2.5 32B Instruct
GLM-5.1 is the stronger model overall, scoring 47.8 to 30.1 on the Noometry Index. Qwen2.5 32B Instruct costs 1.8× less per token, which makes it the better buy when GLM-5.1's lead doesn't matter for your workload.
Last verified . 4 shared benchmarks.
Alibaba (Qwen)Summary
- They share 4 benchmarks with published results for both. GLM-5.1 scores higher in 4 categories and Qwen2.5 32B Instruct in 0 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.1 leads 49.7 to 16.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.3% for GLM-5.1 and 7.4% for Qwen2.5 32B Instruct.
- Qwen2.5 32B Instruct is cheaper at $0.70 / $2.80 per million input/output tokens, against $1.40 / $4.40 for GLM-5.1.
- GLM-5.1 accepts more context: 200K tokens versus 131K.
Side by side
| GLM-5.1 | Qwen2.5 32B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 47.8 | 30.1 |
| Released | 2026-04-07 | 2024-09 |
| Weights | Open | Open |
| Context window | 200K | 131K |
| Max output | 131K | 8K |
| Input $ / M tokens | $1.40 | $0.70 |
| Output $ / M tokens | $4.40 | $2.80 |
| Results tracked | 41 | 7 |
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Category by category
Coding GLM-5.1 leads
GLM-5.1: 48.7 (#55), Qwen2.5 32B Instruct: 38.7 (#169)
| Benchmark | GLM-5.1 | Qwen2.5 32B Instruct |
|---|---|---|
| SWE-bench Verified | 74.2% | — |
| LMArena WebDev | 1508 | — |
| SciCode | 43.8% | — |
| WeirdML | 57.1% | — |
| BigCodeBench Instruct | — | 45% |
| LMArena Coding | 1485 | — |
| BigCodeBench Complete | — | 52.3% |
| ALE-Bench | 887.1 | — |
Agentic & Tool Use Not comparable
GLM-5.1: 24.9 (#113), Qwen2.5 32B Instruct: —
| Benchmark | GLM-5.1 | Qwen2.5 32B Instruct |
|---|---|---|
| APEX-Agents | 40.9% | — |
| ExploitBench | 18.1% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 5,634 | — |
Reasoning GLM-5.1 leads
GLM-5.1: 39.1 (#60), Qwen2.5 32B Instruct: 19.2 (#266)
| Benchmark | GLM-5.1 | Qwen2.5 32B Instruct |
|---|---|---|
| Chess Puzzles | 19% | 0% |
| Epoch Capabilities Index | 149.84 | 128.52 |
| SimpleBench | 55.1% | — |
| NYT Connections (extended) | 77.7% | — |
| CritPt | 4.6% | — |
| Thematic Generalization | 69.8% | — |
| LMArena Hard Prompts | 1472 | — |
Math GLM-5.1 leads
GLM-5.1: 49.7 (#60), Qwen2.5 32B Instruct: 16.2 (#296)
| Benchmark | GLM-5.1 | Qwen2.5 32B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.3% | 7.4% |
| FrontierMath (Tiers 1-3) | 36.8% | — |
| MathArena Final-Answer Competitions | 67.1% | — |
| ProofBench | 22.2% | — |
| LMArena Math | 1473 | — |
| MATH Level 5 | — | 56.1% |
| FrontierMath (Feb 2025 set) | 33.4% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge GLM-5.1 leads
GLM-5.1: 54.9 (#50), Qwen2.5 32B Instruct: 24.9 (#266)
| Benchmark | GLM-5.1 | Qwen2.5 32B Instruct |
|---|---|---|
| GPQA Diamond | 89.9% | 46.1% |
| SimpleQA Verified | 34% | — |
| LMArena Expert | 1476 | — |
Multilingual Not comparable
GLM-5.1: 55.0 (#36), Qwen2.5 32B Instruct: —
| Benchmark | GLM-5.1 | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Non-English | 1447 | — |
| LMArena Chinese | 1515 | — |
| LMArena French | 1474 | — |
| LMArena German | 1465 | — |
| LMArena Japanese | 1434 | — |
| LMArena Korean | 1418 | — |
| LMArena Russian | 1454 | — |
| LMArena Spanish | 1469 | — |
Instruction Following Not comparable
GLM-5.1: 76.3 (#42), Qwen2.5 32B Instruct: —
| Benchmark | GLM-5.1 | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Instruction Following | 1451 | — |
Long Context Not comparable
GLM-5.1: 44.9 (#53), Qwen2.5 32B Instruct: —
| Benchmark | GLM-5.1 | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Longer Query | 1466 | — |
Writing & Preference Not comparable
GLM-5.1: 66.9 (#31), Qwen2.5 32B Instruct: —
| Benchmark | GLM-5.1 | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Text | 1461 | — |
| LMArena Creative Writing | 1453 | — |
| EQ-Bench Creative Writing | 1592 | — |
| LMArena Multi-Turn | 1472 | — |
Frequently asked questions
Is GLM-5.1 better than Qwen2.5 32B Instruct?
GLM-5.1 is the stronger model overall, scoring 47.8 to 30.1 on the Noometry Index. Qwen2.5 32B Instruct costs 1.8× less per token, which makes it the better buy when GLM-5.1's lead doesn't matter for your workload.
Which is cheaper, GLM-5.1 or Qwen2.5 32B Instruct?
Qwen2.5 32B Instruct is cheaper. It lists at $0.70 per million input tokens and $2.80 per million output tokens; GLM-5.1 lists at $1.40 and $4.40.
Is GLM-5.1 or Qwen2.5 32B Instruct better for coding?
GLM-5.1 scores higher on coding benchmarks: 48.7 versus 38.7 in the Noometry coding category.
Which has the bigger context window?
GLM-5.1 does, with 200K tokens against 131K.
How many benchmarks do GLM-5.1 and Qwen2.5 32B Instruct share?
4 benchmarks have published results for both models. GLM-5.1 has 41 scored results on Noometry and Qwen2.5 32B Instruct has 7.