Model comparison
GLM-4.5V vs Grok-2 (Dec 2024)
GLM-4.5V is the stronger model overall, scoring 39.8 to 33.7 on the Noometry Index.
Last verified . 13 shared benchmarks.
Summary
- They share 13 benchmarks with published results for both. GLM-4.5V scores higher in 8 categories and Grok-2 (Dec 2024) in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-4.5V leads 37.4 to 20.8.
- GLM-4.5V has downloadable open weights; the other is API-only.
Side by side
| GLM-4.5V | Grok-2 (Dec 2024) | |
|---|---|---|
| Provider | Z.ai (Zhipu) | xAI |
| Noometry Index | 39.8 | 33.7 |
| Released | 2025-08-11 | 2024-08-13 |
| Weights | Open | Proprietary |
| Context window | 64K | — |
| Max output | 16K | — |
| Input $ / M tokens | $0.60 | — |
| Output $ / M tokens | $1.80 | — |
| Results tracked | 15 | 34 |
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Category by category
Coding GLM-4.5V leads
GLM-4.5V: 39.5 (#155), Grok-2 (Dec 2024): 33.3 (#258)
| Benchmark | GLM-4.5V | Grok-2 (Dec 2024) |
|---|---|---|
| LMArena Coding | 1347 | 1287 |
| WeirdML | — | 22.2% |
| LiveBench Coding | — | 46.4% |
Reasoning GLM-4.5V leads
GLM-4.5V: 27.4 (#119), Grok-2 (Dec 2024): 16.9 (#299)
| Benchmark | GLM-4.5V | Grok-2 (Dec 2024) |
|---|---|---|
| LMArena Hard Prompts | 1334 | 1272 |
| SimpleBench | — | 22.7% |
| Kagi LLM Benchmark | 59.8% | — |
| LiveBench Reasoning | — | 54.8% |
| DTBench | — | 65.2% |
| LiveBench Data Analysis | — | 54.5% |
| Epoch Capabilities Index | — | 130.48 |
| LiveBench | — | 54.3% |
Math GLM-4.5V leads
GLM-4.5V: 37.4 (#159), Grok-2 (Dec 2024): 20.8 (#284)
| Benchmark | GLM-4.5V | Grok-2 (Dec 2024) |
|---|---|---|
| LMArena Math | 1354 | 1283 |
| OTIS Mock AIME 2024-2025 | — | 11.5% |
| LiveBench Math | — | 54.9% |
| MATH Level 5 | — | 63.5% |
| FrontierMath (Feb 2025 set) | — | 0.7% |
Knowledge GLM-4.5V leads
GLM-4.5V: 37.5 (#156), Grok-2 (Dec 2024): 29.8 (#233)
| Benchmark | GLM-4.5V | Grok-2 (Dec 2024) |
|---|---|---|
| LMArena Expert | 1353 | 1254 |
| GPQA Diamond | — | 53.8% |
| Confabulations | — | 20.1% |
Multimodal Not comparable
GLM-4.5V: 34.3 (#92), Grok-2 (Dec 2024): —
| Benchmark | GLM-4.5V | Grok-2 (Dec 2024) |
|---|---|---|
| LMArena Vision | 1154 | — |
Multilingual GLM-4.5V leads
GLM-4.5V: 44.6 (#177), Grok-2 (Dec 2024): 43.1 (#188)
| Benchmark | GLM-4.5V | Grok-2 (Dec 2024) |
|---|---|---|
| LMArena Non-English | 1303 | 1282 |
| LMArena Chinese | 1337 | 1289 |
| LMArena Russian | 1298 | 1286 |
| LMArena Spanish | 1336 | 1281 |
| LMArena French | — | 1318 |
| LMArena German | — | 1287 |
| LMArena Japanese | — | 1244 |
| LMArena Korean | — | 1237 |
Instruction Following GLM-4.5V leads
GLM-4.5V: 69.2 (#175), Grok-2 (Dec 2024): 66.9 (#202)
| Benchmark | GLM-4.5V | Grok-2 (Dec 2024) |
|---|---|---|
| LMArena Instruction Following | 1311 | 1270 |
| LiveBench Instruction Following | — | 69.6% |
Long Context Too close to call
GLM-4.5V: 39.6 (#171), Grok-2 (Dec 2024): 38.8 (#190)
| Benchmark | GLM-4.5V | Grok-2 (Dec 2024) |
|---|---|---|
| LMArena Longer Query | 1304 | 1276 |
Writing & Preference GLM-4.5V leads
GLM-4.5V: 52.5 (#170), Grok-2 (Dec 2024): 48.6 (#198)
| Benchmark | GLM-4.5V | Grok-2 (Dec 2024) |
|---|---|---|
| LMArena Text | 1333 | 1305 |
| LMArena Creative Writing | 1295 | 1284 |
| LMArena Multi-Turn | 1332 | 1290 |
| Short-Story Creative Writing | — | 63.6% |
| LiveBench Language | — | 45.6% |
Frequently asked questions
Is GLM-4.5V better than Grok-2 (Dec 2024)?
GLM-4.5V is the stronger model overall, scoring 39.8 to 33.7 on the Noometry Index.
Is GLM-4.5V or Grok-2 (Dec 2024) better for coding?
GLM-4.5V scores higher on coding benchmarks: 39.5 versus 33.3 in the Noometry coding category.
How many benchmarks do GLM-4.5V and Grok-2 (Dec 2024) share?
13 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Grok-2 (Dec 2024) has 34.