Model comparison
GLM-5.2 vs Qwen2.5 14B Instruct
GLM-5.2 has enough public results to be ranked (#38); Qwen2.5 14B Instruct does not yet, so treat this comparison as directional.
Last verified . 0 shared benchmarks.
Alibaba (Qwen)Summary
- The widest gap is in coding, where GLM-5.2 leads 50.9 to 37.1.
- Qwen2.5 14B Instruct is cheaper at $0.35 / $1.40 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 131K.
Side by side
| GLM-5.2 | Qwen2.5 14B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 50.9 | 38.2 |
| Released | 2026-06-13 | 2024-09 |
| Weights | Open | Open |
| Context window | 1M | 131K |
| Max output | 131K | 8K |
| Input $ / M tokens | $1.40 | $0.35 |
| Output $ / M tokens | $4.40 | $1.40 |
| Results tracked | 50 | 3 |
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Category by category
Coding GLM-5.2 leads
GLM-5.2: 50.9 (#37), Qwen2.5 14B Instruct: 37.1 (#187)
| Benchmark | GLM-5.2 | Qwen2.5 14B Instruct |
|---|---|---|
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| LMArena WebDev | 1604 | — |
| SciCode | 50.5% | — |
| WeirdML | 70.1% | — |
| BigCodeBench Instruct | — | 39.8% |
| LMArena Coding | 1484 | — |
| BigCodeBench Complete | — | 52.2% |
| ALE-Bench | 1,047 | — |
Agentic & Tool Use Not comparable
GLM-5.2: 31.6 (#63), Qwen2.5 14B Instruct: —
| Benchmark | GLM-5.2 | Qwen2.5 14B Instruct |
|---|---|---|
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 8,314 | — |
Reasoning Not comparable
GLM-5.2: 42.1 (#46), Qwen2.5 14B Instruct: —
| Benchmark | GLM-5.2 | Qwen2.5 14B Instruct |
|---|---|---|
| ARC-AGI-2 | 22.8% | — |
| SimpleBench | 58.8% | — |
| Kagi LLM Benchmark | 62.6% | — |
| NYT Connections (extended) | 74.3% | — |
| ARC-AGI-1 | 77% | — |
| CritPt | 20.9% | — |
| Chess Puzzles | 21% | — |
| EBR-Bench | 9.5% | — |
| LMArena Hard Prompts | 1480 | — |
| Mystery Game Puzzles | 19% | — |
| DTBench | 93.6% | — |
| LMCA | 45.8% | — |
| Surface Evolver Bench | 55.6% | — |
| Epoch Capabilities Index | 151.76 | — |
Math Not comparable
GLM-5.2: 55.4 (#35), Qwen2.5 14B Instruct: —
| Benchmark | GLM-5.2 | Qwen2.5 14B Instruct |
|---|---|---|
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| OTIS Mock AIME 2024-2025 | 86.4% | — |
| ProofBench | 35% | — |
| LMArena Math | 1482 | — |
Knowledge Not comparable
GLM-5.2: 57.1 (#36), Qwen2.5 14B Instruct: —
| Benchmark | GLM-5.2 | Qwen2.5 14B Instruct |
|---|---|---|
| GPQA Diamond | 91.9% | — |
| SimpleQA Verified | 34.2% | — |
| LMArena Expert | 1484 | — |
| MMLU | — | 79.9% |
Multilingual Not comparable
GLM-5.2: 56.0 (#25), Qwen2.5 14B Instruct: —
| Benchmark | GLM-5.2 | Qwen2.5 14B Instruct |
|---|---|---|
| LMArena Non-English | 1459 | — |
| LMArena Chinese | 1518 | — |
| LMArena French | 1476 | — |
| LMArena German | 1470 | — |
| LMArena Japanese | 1451 | — |
| LMArena Korean | 1445 | — |
| LMArena Russian | 1467 | — |
| LMArena Spanish | 1477 | — |
Instruction Following Not comparable
GLM-5.2: 77.1 (#32), Qwen2.5 14B Instruct: —
| Benchmark | GLM-5.2 | Qwen2.5 14B Instruct |
|---|---|---|
| LMArena Instruction Following | 1465 | — |
Long Context Not comparable
GLM-5.2: 45.3 (#43), Qwen2.5 14B Instruct: —
| Benchmark | GLM-5.2 | Qwen2.5 14B Instruct |
|---|---|---|
| LMArena Longer Query | 1479 | — |
Writing & Preference Not comparable
GLM-5.2: 70.7 (#20), Qwen2.5 14B Instruct: —
| Benchmark | GLM-5.2 | Qwen2.5 14B Instruct |
|---|---|---|
| LMArena Text | 1470 | — |
| LMArena Creative Writing | 1465 | — |
| EQ-Bench Creative Writing | 1757 | — |
| EQ-Bench 4 | 1222 | — |
| LMArena Multi-Turn | 1468 | — |
Frequently asked questions
Is GLM-5.2 better than Qwen2.5 14B Instruct?
GLM-5.2 has enough public results to be ranked (#38); Qwen2.5 14B Instruct does not yet, so treat this comparison as directional.
Which is cheaper, GLM-5.2 or Qwen2.5 14B Instruct?
Qwen2.5 14B Instruct is cheaper. It lists at $0.35 per million input tokens and $1.40 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.
Is GLM-5.2 or Qwen2.5 14B Instruct better for coding?
GLM-5.2 scores higher on coding benchmarks: 50.9 versus 37.1 in the Noometry coding category.
Which has the bigger context window?
GLM-5.2 does, with 1M tokens against 131K.
How many benchmarks do GLM-5.2 and Qwen2.5 14B Instruct share?
0 benchmarks have published results for both models. GLM-5.2 has 50 scored results on Noometry and Qwen2.5 14B Instruct has 3.