Model comparison
GLM-5.3 vs Grok 4.20 Multi-Agent
GLM-5.3 is the stronger model overall, scoring 54.8 to 46.2 on the Noometry Index.
Last verified . 18 shared benchmarks.
Summary
- They share 18 benchmarks with published results for both. GLM-5.3 scores higher in 8 categories and Grok 4.20 Multi-Agent in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3 leads 62.3 to 39.4.
- The biggest single-benchmark swing is NYT Connections (extended): 74.2% for GLM-5.3 and 89.6% for Grok 4.20 Multi-Agent.
- Grok 4.20 Multi-Agent is cheaper at $1.25 / $2.50 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.
- GLM-5.3 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3 | Grok 4.20 Multi-Agent | |
|---|---|---|
| Provider | Z.ai (Zhipu) | xAI |
| Noometry Index | 54.8 | 46.2 |
| Released | 2026-08-14 | 2026-03-09 |
| Weights | Open | Proprietary |
| Context window | 1M | 1M |
| Max output | 131K | 30K |
| Input $ / M tokens | $1.40 | $1.25 |
| Output $ / M tokens | $4.40 | $2.50 |
| Results tracked | 42 | 20 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.5 (#14), Grok 4.20 Multi-Agent: 43.0 (#92)
| Benchmark | GLM-5.3 | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Coding | 1496 | 1457 |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| CursorBench | 42.6% | — |
| LMArena WebDev | 1622 | — |
| FrontierSWE | 30.2% | — |
| SciCode | 59% | — |
| WeirdML | 75.4% | — |
| ALE-Bench | 1,317 | — |
Agentic & Tool Use Not comparable
GLM-5.3: 36.4 (#38), Grok 4.20 Multi-Agent: —
| Benchmark | GLM-5.3 | Grok 4.20 Multi-Agent |
|---|---|---|
| APEX-Agents | 56.6% | — |
| LMArena Search | — | 1204 |
| Vending-Bench 2 | 8,164 | — |
Reasoning GLM-5.3 leads
GLM-5.3: 46.1 (#46), Grok 4.20 Multi-Agent: 43.9 (#48)
| Benchmark | GLM-5.3 | Grok 4.20 Multi-Agent |
|---|---|---|
| NYT Connections (extended) | 74.2% | 89.6% |
| LMArena Hard Prompts | 1489 | 1448 |
| CritPt | 19.1% | — |
| Chess Puzzles | 21% | — |
| Mystery Game Puzzles | 33% | — |
| DTBench | 87.7% | — |
| LMCA | 55.5% | — |
| Bench to the Future 3 | 0.15 | — |
| Epoch Capabilities Index | 155.61 | — |
Math GLM-5.3 leads
GLM-5.3: 62.3 (#33), Grok 4.20 Multi-Agent: 39.4 (#104)
| Benchmark | GLM-5.3 | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Math | 1489 | 1442 |
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| OTIS Mock AIME 2024-2025 | 91.1% | — |
| ProofBench | 49% | — |
Knowledge GLM-5.3 leads
GLM-5.3: 58.3 (#37), Grok 4.20 Multi-Agent: 40.4 (#119)
| Benchmark | GLM-5.3 | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Expert | 1516 | 1445 |
| GPQA Diamond | 90.9% | — |
| SimpleQA Verified | 41% | — |
Multimodal Not comparable
GLM-5.3: —, Grok 4.20 Multi-Agent: 40.5 (#48)
| Benchmark | GLM-5.3 | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Vision | — | 1259 |
Multilingual GLM-5.3 leads
GLM-5.3: 55.7 (#28), Grok 4.20 Multi-Agent: 54.4 (#43)
| Benchmark | GLM-5.3 | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Non-English | 1457 | 1440 |
| LMArena Chinese | 1528 | 1475 |
| LMArena French | 1499 | 1466 |
| LMArena German | 1499 | 1456 |
| LMArena Japanese | 1453 | 1405 |
| LMArena Korean | 1472 | 1416 |
| LMArena Russian | 1463 | 1457 |
| LMArena Spanish | 1460 | 1447 |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.5 (#23), Grok 4.20 Multi-Agent: 74.8 (#84)
| Benchmark | GLM-5.3 | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Instruction Following | 1477 | 1420 |
Long Context GLM-5.3 leads
GLM-5.3: 45.4 (#41), Grok 4.20 Multi-Agent: 43.7 (#88)
| Benchmark | GLM-5.3 | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Longer Query | 1482 | 1431 |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.7 (#6), Grok 4.20 Multi-Agent: 64.0 (#59)
| Benchmark | GLM-5.3 | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Text | 1471 | 1450 |
| LMArena Creative Writing | 1457 | 1436 |
| LMArena Multi-Turn | 1472 | 1452 |
| EQ-Bench Creative Writing | 2075 | — |
Frequently asked questions
Is GLM-5.3 better than Grok 4.20 Multi-Agent?
GLM-5.3 is the stronger model overall, scoring 54.8 to 46.2 on the Noometry Index.
Which is cheaper, GLM-5.3 or Grok 4.20 Multi-Agent?
Grok 4.20 Multi-Agent is cheaper. It lists at $1.25 per million input tokens and $2.50 per million output tokens; GLM-5.3 lists at $1.40 and $4.40.
Is GLM-5.3 or Grok 4.20 Multi-Agent better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 43.0 in the Noometry coding category.
Which has the bigger context window?
Both accept 1M tokens.
How many benchmarks do GLM-5.3 and Grok 4.20 Multi-Agent share?
18 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and Grok 4.20 Multi-Agent has 20.