Model comparison
GLM-5.2 vs Grok 4.7
Grok 4.7 is the stronger model overall, scoring 53.1 to 51.1 on the Noometry Index.
Last verified . 33 shared benchmarks.
Summary
- They share 33 benchmarks with published results for both. GLM-5.2 scores higher in 4 categories and Grok 4.7 in 5 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.7 leads 49.1 to 42.3.
- The biggest single-benchmark swing is FrontierCode: 24.5% for GLM-5.2 and 47.6% for Grok 4.7.
- GLM-5.2 is cheaper at $1.40 / $4.40 per million input/output tokens, against $2 / $6 for Grok 4.7.
- GLM-5.2 accepts more context: 1M tokens versus 500K.
- GLM-5.2 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.2 | Grok 4.7 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | xAI |
| Noometry Index | 51.1 | 53.1 |
| Released | 2026-06-13 | 2026-09-21 |
| Weights | Open | Proprietary |
| Context window | 1M | 500K |
| Max output | 131K | 500K |
| Input $ / M tokens | $1.40 | $2 |
| Output $ / M tokens | $4.40 | $6 |
| Results tracked | 51 | 39 |
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Category by category
Coding Grok 4.7 leads
GLM-5.2: 51.3 (#41), Grok 4.7: 58.0 (#18)
| Benchmark | GLM-5.2 | Grok 4.7 |
|---|---|---|
| FrontierCode | 24.5% | 47.6% |
| LMArena WebDev | 1603 | 1639 |
| SciCode | 50.5% | 57.8% |
| LMArena Coding | 1485 | 1427 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| CursorBench | — | 46.3% |
| FrontierSWE | — | 29.5% |
| WeirdML | 70.1% | — |
| ALE-Bench | 1,047 | — |
Agentic & Tool Use Grok 4.7 leads
GLM-5.2: 32.4 (#63), Grok 4.7: 36.7 (#37)
| Benchmark | GLM-5.2 | Grok 4.7 |
|---|---|---|
| APEX-Agents | 45.2% | 54.6% |
| Vending-Bench 2 | 8,314 | 10,537 |
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |
| GDP.pdf | — | 22.8% |
Reasoning Grok 4.7 leads
GLM-5.2: 42.3 (#52), Grok 4.7: 49.1 (#40)
| Benchmark | GLM-5.2 | Grok 4.7 |
|---|---|---|
| NYT Connections (extended) | 74.3% | 76.8% |
| CritPt | 20.9% | 18% |
| Chess Puzzles | 21% | 38% |
| LMArena Hard Prompts | 1480 | 1413 |
| Mystery Game Puzzles | 19% | 29% |
| DTBench | 93.6% | 96% |
| LMCA | 45.8% | 49.4% |
| Epoch Capabilities Index | 151.78 | 153.53 |
| ARC-AGI-2 | 22.8% | — |
| SimpleBench | 58.8% | — |
| Kagi LLM Benchmark | 62.6% | — |
| ARC-AGI-1 | 77% | — |
| EBR-Bench | 9.5% | — |
| Surface Evolver Bench | 55.6% | — |
Math Grok 4.7 leads
GLM-5.2: 55.7 (#43), Grok 4.7: 57.8 (#39)
| Benchmark | GLM-5.2 | Grok 4.7 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 59.2% | 53% |
| FrontierMath Tier 4 | 29.3% | 17.1% |
| OTIS Mock AIME 2024-2025 | 86.4% | 98.1% |
| ProofBench | 35% | 34% |
| LMArena Math | 1482 | 1407 |
| MathArena Final-Answer Competitions | 67.6% | — |
Knowledge Grok 4.7 leads
GLM-5.2: 57.1 (#40), Grok 4.7: 62.8 (#22)
| Benchmark | GLM-5.2 | Grok 4.7 |
|---|---|---|
| GPQA Diamond | 91.9% | 92.7% |
| SimpleQA Verified | 34.2% | 56% |
| LMArena Expert | 1486 | 1422 |
Multimodal Not comparable
GLM-5.2: —, Grok 4.7: 35.5 (#87)
| Benchmark | GLM-5.2 | Grok 4.7 |
|---|---|---|
| LMArena Vision | — | 1228 |
| Blueprint-Bench 2 | — | 32.5% |
| Furniture Assembly | — | 20.8% |
Multilingual GLM-5.2 leads
GLM-5.2: 55.8 (#26), Grok 4.7: 50.8 (#116)
| Benchmark | GLM-5.2 | Grok 4.7 |
|---|---|---|
| LMArena Non-English | 1459 | 1389 |
| LMArena Chinese | 1519 | 1455 |
| LMArena French | 1479 | 1455 |
| LMArena Russian | 1466 | 1397 |
| LMArena Spanish | 1477 | 1400 |
| LMArena German | 1468 | — |
| LMArena Japanese | 1451 | — |
| LMArena Korean | 1445 | — |
Instruction Following GLM-5.2 leads
GLM-5.2: 76.9 (#34), Grok 4.7: 74.1 (#105)
| Benchmark | GLM-5.2 | Grok 4.7 |
|---|---|---|
| LMArena Instruction Following | 1465 | 1404 |
Long Context GLM-5.2 leads
GLM-5.2: 45.3 (#43), Grok 4.7: 43.1 (#104)
| Benchmark | GLM-5.2 | Grok 4.7 |
|---|---|---|
| LMArena Longer Query | 1479 | 1413 |
Writing & Preference Too close to call
GLM-5.2: 70.4 (#21), Grok 4.7: 70.0 (#24)
| Benchmark | GLM-5.2 | Grok 4.7 |
|---|---|---|
| LMArena Text | 1470 | 1399 |
| LMArena Creative Writing | 1462 | 1391 |
| EQ-Bench Creative Writing | 1757 | 2007 |
| LMArena Multi-Turn | 1469 | 1393 |
| EQ-Bench 4 | 1222 | — |
Frequently asked questions
Is GLM-5.2 better than Grok 4.7?
Grok 4.7 is the stronger model overall, scoring 53.1 to 51.1 on the Noometry Index.
Which is cheaper, GLM-5.2 or Grok 4.7?
GLM-5.2 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; Grok 4.7 lists at $2 and $6.
Is GLM-5.2 or Grok 4.7 better for coding?
Grok 4.7 scores higher on coding benchmarks: 58.0 versus 51.3 in the Noometry coding category.
Which has the bigger context window?
GLM-5.2 does, with 1M tokens against 500K.
How many benchmarks do GLM-5.2 and Grok 4.7 share?
33 benchmarks have published results for both models. GLM-5.2 has 51 scored results on Noometry and Grok 4.7 has 39.