Model comparison
GLM-4.7 vs Grok 4.20 Multi-Agent
Grok 4.20 Multi-Agent is the stronger model overall, scoring 45.5 to 41.7 on the Noometry Index. GLM-4.7 costs 1.6× less per token, which makes it the better buy when Grok 4.20 Multi-Agent's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
Summary
- They share 17 benchmarks with published results for both. GLM-4.7 scores higher in 3 categories and Grok 4.20 Multi-Agent in 5 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.20 Multi-Agent leads 42.4 to 23.7.
- GLM-4.7 is cheaper at $0.60 / $2.20 per million input/output tokens, against $1.25 / $2.50 for Grok 4.20 Multi-Agent.
- Grok 4.20 Multi-Agent accepts more context: 1M tokens versus 205K.
- GLM-4.7 has downloadable open weights; the other is API-only.
Side by side
| GLM-4.7 | Grok 4.20 Multi-Agent | |
|---|---|---|
| Provider | Z.ai (Zhipu) | xAI |
| Noometry Index | 41.7 | 45.5 |
| Released | 2025-12-22 | 2026-03-09 |
| Weights | Open | Proprietary |
| Context window | 205K | 1M |
| Max output | 131K | 30K |
| Input $ / M tokens | $0.60 | $1.25 |
| Output $ / M tokens | $2.20 | $2.50 |
| Results tracked | 36 | 20 |
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Category by category
Coding GLM-4.7 leads
GLM-4.7: 43.6 (#74), Grok 4.20 Multi-Agent: 42.4 (#91)
| Benchmark | GLM-4.7 | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Coding | 1454 | 1458 |
| LMArena WebDev | 1435 | — |
| SciCode | 45.1% | — |
| ALE-Bench | 399.48 | — |
Agentic & Tool Use Not comparable
GLM-4.7: 26.4 (#96), Grok 4.20 Multi-Agent: —
| Benchmark | GLM-4.7 | Grok 4.20 Multi-Agent |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| LMArena Search | — | 1204 |
| Vending-Bench 2 | 2,377 | — |
Reasoning Grok 4.20 Multi-Agent leads
GLM-4.7: 23.7 (#161), Grok 4.20 Multi-Agent: 42.4 (#44)
| Benchmark | GLM-4.7 | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Hard Prompts | 1443 | 1448 |
| SimpleBench | 47.7% | — |
| NYT Connections (extended) | — | 89.6% |
| CritPt | 1.7% | — |
| Chess Puzzles | 6% | — |
| Epoch Capabilities Index | 143.53 | — |
Math GLM-4.7 leads
GLM-4.7: 37.7 (#92), Grok 4.20 Multi-Agent: 36.6 (#106)
| Benchmark | GLM-4.7 | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Math | 1423 | 1441 |
| OTIS Mock AIME 2024-2025 | 83.3% | — |
| ProofBench | 6% | — |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge GLM-4.7 leads
GLM-4.7: 47.0 (#74), Grok 4.20 Multi-Agent: 39.7 (#116)
| Benchmark | GLM-4.7 | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Expert | 1424 | 1445 |
| GPQA Diamond | 83.3% | — |
| SimpleQA Verified | 32.2% | — |
| Vectara Hallucination Rate | 11.7% | — |
Multimodal Not comparable
GLM-4.7: —, Grok 4.20 Multi-Agent: 39.8 (#44)
| Benchmark | GLM-4.7 | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Vision | — | 1259 |
Multilingual Grok 4.20 Multi-Agent leads
GLM-4.7: 53.0 (#75), Grok 4.20 Multi-Agent: 54.6 (#40)
| Benchmark | GLM-4.7 | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Non-English | 1417 | 1440 |
| LMArena Chinese | 1495 | 1475 |
| LMArena French | 1432 | 1467 |
| LMArena German | 1426 | 1456 |
| LMArena Japanese | 1439 | 1405 |
| LMArena Korean | 1401 | 1416 |
| LMArena Russian | 1423 | 1457 |
| LMArena Spanish | 1433 | 1447 |
Instruction Following Too close to call
GLM-4.7: 74.6 (#90), Grok 4.20 Multi-Agent: 75.0 (#80)
| Benchmark | GLM-4.7 | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Instruction Following | 1411 | 1420 |
Long Context Too close to call
GLM-4.7: 42.8 (#111), Grok 4.20 Multi-Agent: 43.7 (#84)
| Benchmark | GLM-4.7 | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Longer Query | 1432 | 1430 |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
Writing & Preference Grok 4.20 Multi-Agent leads
GLM-4.7: 61.1 (#89), Grok 4.20 Multi-Agent: 64.1 (#57)
| Benchmark | GLM-4.7 | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Text | 1436 | 1450 |
| LMArena Creative Writing | 1401 | 1435 |
| LMArena Multi-Turn | 1446 | 1451 |
| EQ-Bench Creative Writing | 1413 | — |
Frequently asked questions
Is GLM-4.7 better than Grok 4.20 Multi-Agent?
Grok 4.20 Multi-Agent is the stronger model overall, scoring 45.5 to 41.7 on the Noometry Index. GLM-4.7 costs 1.6× less per token, which makes it the better buy when Grok 4.20 Multi-Agent's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7 or Grok 4.20 Multi-Agent?
GLM-4.7 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Grok 4.20 Multi-Agent lists at $1.25 and $2.50.
Is GLM-4.7 or Grok 4.20 Multi-Agent better for coding?
GLM-4.7 scores higher on coding benchmarks: 43.6 versus 42.4 in the Noometry coding category.
Which has the bigger context window?
Grok 4.20 Multi-Agent does, with 1M tokens against 205K.
How many benchmarks do GLM-4.7 and Grok 4.20 Multi-Agent share?
17 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Grok 4.20 Multi-Agent has 20.