Model comparison
GLM-5.3 vs Grok-2 (Dec 2024)
GLM-5.3 is the stronger model overall, scoring 54.8 to 33.7 on the Noometry Index.
Last verified . 22 shared benchmarks.
Summary
- They share 22 benchmarks with published results for both. GLM-5.3 scores higher in 8 categories and Grok-2 (Dec 2024) in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3 leads 62.3 to 20.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 91.1% for GLM-5.3 and 11.5% for Grok-2 (Dec 2024).
- GLM-5.3 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3 | Grok-2 (Dec 2024) | |
|---|---|---|
| Provider | Z.ai (Zhipu) | xAI |
| Noometry Index | 54.8 | 33.7 |
| Released | 2026-08-14 | 2024-08-13 |
| Weights | Open | Proprietary |
| Context window | 1M | — |
| Max output | 131K | — |
| Input $ / M tokens | $1.40 | — |
| Output $ / M tokens | $4.40 | — |
| Results tracked | 42 | 34 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.5 (#14), Grok-2 (Dec 2024): 33.3 (#258)
| Benchmark | GLM-5.3 | Grok-2 (Dec 2024) |
|---|---|---|
| WeirdML | 75.4% | 22.2% |
| LMArena Coding | 1496 | 1287 |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| CursorBench | 42.6% | — |
| LMArena WebDev | 1622 | — |
| FrontierSWE | 30.2% | — |
| SciCode | 59% | — |
| LiveBench Coding | — | 46.4% |
| ALE-Bench | 1,317 | — |
Agentic & Tool Use Not comparable
GLM-5.3: 36.4 (#38), Grok-2 (Dec 2024): —
| Benchmark | GLM-5.3 | Grok-2 (Dec 2024) |
|---|---|---|
| APEX-Agents | 56.6% | — |
| Vending-Bench 2 | 8,164 | — |
Reasoning GLM-5.3 leads
GLM-5.3: 46.1 (#46), Grok-2 (Dec 2024): 16.9 (#299)
| Benchmark | GLM-5.3 | Grok-2 (Dec 2024) |
|---|---|---|
| LMArena Hard Prompts | 1489 | 1272 |
| DTBench | 87.7% | 65.2% |
| Epoch Capabilities Index | 155.61 | 130.48 |
| SimpleBench | — | 22.7% |
| NYT Connections (extended) | 74.2% | — |
| CritPt | 19.1% | — |
| Chess Puzzles | 21% | — |
| LiveBench Reasoning | — | 54.8% |
| Mystery Game Puzzles | 33% | — |
| LiveBench Data Analysis | — | 54.5% |
| LMCA | 55.5% | — |
| Bench to the Future 3 | 0.15 | — |
| LiveBench | — | 54.3% |
Math GLM-5.3 leads
GLM-5.3: 62.3 (#33), Grok-2 (Dec 2024): 20.8 (#284)
| Benchmark | GLM-5.3 | Grok-2 (Dec 2024) |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 91.1% | 11.5% |
| LMArena Math | 1489 | 1283 |
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| ProofBench | 49% | — |
| LiveBench Math | — | 54.9% |
| MATH Level 5 | — | 63.5% |
| FrontierMath (Feb 2025 set) | — | 0.7% |
Knowledge GLM-5.3 leads
GLM-5.3: 58.3 (#37), Grok-2 (Dec 2024): 29.8 (#233)
| Benchmark | GLM-5.3 | Grok-2 (Dec 2024) |
|---|---|---|
| GPQA Diamond | 90.9% | 53.8% |
| LMArena Expert | 1516 | 1254 |
| SimpleQA Verified | 41% | — |
| Confabulations | — | 20.1% |
Multilingual GLM-5.3 leads
GLM-5.3: 55.7 (#28), Grok-2 (Dec 2024): 43.1 (#188)
| Benchmark | GLM-5.3 | Grok-2 (Dec 2024) |
|---|---|---|
| LMArena Non-English | 1457 | 1282 |
| LMArena Chinese | 1528 | 1289 |
| LMArena French | 1499 | 1318 |
| LMArena German | 1499 | 1287 |
| LMArena Japanese | 1453 | 1244 |
| LMArena Korean | 1472 | 1237 |
| LMArena Russian | 1463 | 1286 |
| LMArena Spanish | 1460 | 1281 |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.5 (#23), Grok-2 (Dec 2024): 66.9 (#202)
| Benchmark | GLM-5.3 | Grok-2 (Dec 2024) |
|---|---|---|
| LMArena Instruction Following | 1477 | 1270 |
| LiveBench Instruction Following | — | 69.6% |
Long Context GLM-5.3 leads
GLM-5.3: 45.4 (#41), Grok-2 (Dec 2024): 38.8 (#190)
| Benchmark | GLM-5.3 | Grok-2 (Dec 2024) |
|---|---|---|
| LMArena Longer Query | 1482 | 1276 |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.7 (#6), Grok-2 (Dec 2024): 48.6 (#198)
| Benchmark | GLM-5.3 | Grok-2 (Dec 2024) |
|---|---|---|
| LMArena Text | 1471 | 1305 |
| LMArena Creative Writing | 1457 | 1284 |
| LMArena Multi-Turn | 1472 | 1290 |
| Short-Story Creative Writing | — | 63.6% |
| EQ-Bench Creative Writing | 2075 | — |
| LiveBench Language | — | 45.6% |
Frequently asked questions
Is GLM-5.3 better than Grok-2 (Dec 2024)?
GLM-5.3 is the stronger model overall, scoring 54.8 to 33.7 on the Noometry Index.
Is GLM-5.3 or Grok-2 (Dec 2024) better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 33.3 in the Noometry coding category.
How many benchmarks do GLM-5.3 and Grok-2 (Dec 2024) share?
22 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and Grok-2 (Dec 2024) has 34.