Model comparison
Gemini 1.5 Pro (May 2024) vs GLM-5.2
GLM-5.2 is the stronger model overall, scoring 51.1 to 32.1 on the Noometry Index.
Last verified . 24 shared benchmarks.
GoogleSummary
- They share 24 benchmarks with published results for both. Gemini 1.5 Pro (May 2024) scores higher in 0 categories and GLM-5.2 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.2 leads 42.3 to 12.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 23.1% for Gemini 1.5 Pro (May 2024) and 86.4% for GLM-5.2.
- GLM-5.2 has downloadable open weights; the other is API-only.
Side by side
| Gemini 1.5 Pro (May 2024) | GLM-5.2 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | |
| Noometry Index | 32.1 | 51.1 |
| Released | 2024-02-15 | 2026-06-13 |
| Weights | Proprietary | Open |
| Context window | — | 1M |
| Max output | — | 131K |
| Input $ / M tokens | — | $1.40 |
| Output $ / M tokens | — | $4.40 |
| Results tracked | 45 | 51 |
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Category by category
Coding GLM-5.2 leads
Gemini 1.5 Pro (May 2024): 34.2 (#241), GLM-5.2: 51.3 (#41)
| Benchmark | Gemini 1.5 Pro (May 2024) | GLM-5.2 |
|---|---|---|
| WeirdML | 22.2% | 70.1% |
| LMArena Coding | 1294 | 1485 |
| SWE-bench Verified | — | 78.7% |
| DeepSWE | — | 43.8% |
| FrontierCode | — | 24.5% |
| LMArena WebDev | — | 1603 |
| SciCode | — | 50.5% |
| BigCodeBench Instruct | 43.8% | — |
| BigCodeBench Complete | 57.5% | — |
| CadEval | 34% | — |
| ALE-Bench | — | 1,047 |
| HumanEval+ | 79.3% | — |
| MBPP+ | 74.6% | — |
Agentic & Tool Use GLM-5.2 leads
Gemini 1.5 Pro (May 2024): 17.9 (#145), GLM-5.2: 32.4 (#63)
| Benchmark | Gemini 1.5 Pro (May 2024) | GLM-5.2 |
|---|---|---|
| APEX-Agents | — | 45.2% |
| TheAgentCompany | 3.4% | — |
| τ²-bench Banking | — | 37.1% |
| Cybench | 7.5% | — |
| PostTrainBench | — | 31.7% |
| BALROG | 21% | — |
| GBAEval | — | 0% |
| Vending-Bench 2 | — | 8,314 |
Reasoning GLM-5.2 leads
Gemini 1.5 Pro (May 2024): 12.3 (#338), GLM-5.2: 42.3 (#52)
| Benchmark | Gemini 1.5 Pro (May 2024) | GLM-5.2 |
|---|---|---|
| ARC-AGI-2 | 0.8% | 22.8% |
| SimpleBench | 27.1% | 58.8% |
| LMArena Hard Prompts | 1296 | 1480 |
| DTBench | 59% | 93.6% |
| Epoch Capabilities Index | 131.73 | 151.78 |
| Kagi LLM Benchmark | — | 62.6% |
| NYT Connections (extended) | — | 74.3% |
| ARC-AGI-1 | — | 77% |
| CritPt | — | 20.9% |
| Chess Puzzles | — | 21% |
| EBR-Bench | — | 9.5% |
| Mystery Game Puzzles | — | 19% |
| LMCA | — | 45.8% |
| Surface Evolver Bench | — | 55.6% |
| BIG-Bench Hard | 89.2% | — |
| ForecastBench | 58.4 | — |
Math GLM-5.2 leads
Gemini 1.5 Pro (May 2024): 25.8 (#266), GLM-5.2: 55.7 (#43)
| Benchmark | Gemini 1.5 Pro (May 2024) | GLM-5.2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 23.1% | 86.4% |
| LMArena Math | 1315 | 1482 |
| FrontierMath (Tiers 1-3) | — | 59.2% |
| FrontierMath Tier 4 | — | 29.3% |
| MathArena Final-Answer Competitions | — | 67.6% |
| ProofBench | — | 35% |
| Omni-MATH | 36.4% | — |
| MATH Level 5 | 70.4% | — |
Knowledge GLM-5.2 leads
Gemini 1.5 Pro (May 2024): 29.4 (#239), GLM-5.2: 57.1 (#40)
| Benchmark | Gemini 1.5 Pro (May 2024) | GLM-5.2 |
|---|---|---|
| GPQA Diamond | 57.2% | 91.9% |
| LMArena Expert | 1279 | 1486 |
| Humanity's Last Exam | 4.6% | — |
| SimpleQA Verified | — | 34.2% |
| MMLU-Pro | 73.7% | — |
| Confabulations | 13.5% | — |
| GPQA (HELM) | 53.4% | — |
| MMLU | 86.9% | — |
Multimodal Not comparable
Gemini 1.5 Pro (May 2024): 36.8 (#77), GLM-5.2: —
| Benchmark | Gemini 1.5 Pro (May 2024) | GLM-5.2 |
|---|---|---|
| LMArena Vision | 1161 | — |
| Video-MME | 75% | — |
Multilingual GLM-5.2 leads
Gemini 1.5 Pro (May 2024): 45.3 (#174), GLM-5.2: 55.8 (#26)
| Benchmark | Gemini 1.5 Pro (May 2024) | GLM-5.2 |
|---|---|---|
| LMArena Non-English | 1312 | 1459 |
| LMArena Chinese | 1331 | 1519 |
| LMArena French | 1302 | 1479 |
| LMArena German | 1286 | 1468 |
| LMArena Japanese | 1292 | 1451 |
| LMArena Korean | 1298 | 1445 |
| LMArena Russian | 1320 | 1466 |
| LMArena Spanish | 1311 | 1477 |
Instruction Following GLM-5.2 leads
Gemini 1.5 Pro (May 2024): 68.6 (#185), GLM-5.2: 76.9 (#34)
| Benchmark | Gemini 1.5 Pro (May 2024) | GLM-5.2 |
|---|---|---|
| LMArena Instruction Following | 1297 | 1465 |
| IFEval | 83.7% | — |
Long Context GLM-5.2 leads
Gemini 1.5 Pro (May 2024): 39.8 (#169), GLM-5.2: 45.3 (#43)
| Benchmark | Gemini 1.5 Pro (May 2024) | GLM-5.2 |
|---|---|---|
| LMArena Longer Query | 1308 | 1479 |
Writing & Preference GLM-5.2 leads
Gemini 1.5 Pro (May 2024): 52.4 (#172), GLM-5.2: 70.4 (#21)
| Benchmark | Gemini 1.5 Pro (May 2024) | GLM-5.2 |
|---|---|---|
| LMArena Text | 1319 | 1470 |
| LMArena Creative Writing | 1333 | 1462 |
| LMArena Multi-Turn | 1296 | 1469 |
| EQ-Bench Creative Writing | — | 1757 |
| WildBench | 81.3% | — |
| EQ-Bench 4 | — | 1222 |
Frequently asked questions
Is Gemini 1.5 Pro (May 2024) better than GLM-5.2?
GLM-5.2 is the stronger model overall, scoring 51.1 to 32.1 on the Noometry Index.
Is Gemini 1.5 Pro (May 2024) or GLM-5.2 better for coding?
GLM-5.2 scores higher on coding benchmarks: 51.3 versus 34.2 in the Noometry coding category.
How many benchmarks do Gemini 1.5 Pro (May 2024) and GLM-5.2 share?
24 benchmarks have published results for both models. Gemini 1.5 Pro (May 2024) has 45 scored results on Noometry and GLM-5.2 has 51.