Model comparison
GLM-5.3 vs Grok 4.7
GLM-5.3 is the stronger model overall, scoring 54.8 to 53.1 on the Noometry Index.
Last verified . 35 shared benchmarks.
Summary
- They share 35 benchmarks with published results for both. GLM-5.3 scores higher in 6 categories and Grok 4.7 in 3 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-5.3 leads 75.7 to 70.0.
- The biggest single-benchmark swing is Chess Puzzles: 21% for GLM-5.3 and 38% for Grok 4.7.
- GLM-5.3 is cheaper at $1.40 / $4.40 per million input/output tokens, against $2 / $6 for Grok 4.7.
- GLM-5.3 accepts more context: 1M tokens versus 500K.
- GLM-5.3 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3 | Grok 4.7 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | xAI |
| Noometry Index | 54.8 | 53.1 |
| Released | 2026-08-14 | 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 | 42 | 39 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.5 (#14), Grok 4.7: 58.0 (#18)
| Benchmark | GLM-5.3 | Grok 4.7 |
|---|---|---|
| FrontierCode | 40.1% | 47.6% |
| CursorBench | 42.6% | 46.3% |
| LMArena WebDev | 1622 | 1639 |
| FrontierSWE | 30.2% | 29.5% |
| SciCode | 59% | 57.8% |
| LMArena Coding | 1496 | 1427 |
| DeepSWE | 69% | — |
| WeirdML | 75.4% | — |
| ALE-Bench | 1,317 | — |
Agentic & Tool Use Too close to call
GLM-5.3: 36.4 (#38), Grok 4.7: 36.7 (#37)
| Benchmark | GLM-5.3 | Grok 4.7 |
|---|---|---|
| APEX-Agents | 56.6% | 54.6% |
| Vending-Bench 2 | 8,164 | 10,537 |
| GDP.pdf | — | 22.8% |
Reasoning Grok 4.7 leads
GLM-5.3: 46.1 (#46), Grok 4.7: 49.1 (#40)
| Benchmark | GLM-5.3 | Grok 4.7 |
|---|---|---|
| NYT Connections (extended) | 74.2% | 76.8% |
| CritPt | 19.1% | 18% |
| Chess Puzzles | 21% | 38% |
| LMArena Hard Prompts | 1489 | 1413 |
| Mystery Game Puzzles | 33% | 29% |
| DTBench | 87.7% | 96% |
| LMCA | 55.5% | 49.4% |
| Epoch Capabilities Index | 155.61 | 153.53 |
| Bench to the Future 3 | 0.15 | — |
Math GLM-5.3 leads
GLM-5.3: 62.3 (#33), Grok 4.7: 57.8 (#39)
| Benchmark | GLM-5.3 | Grok 4.7 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 68.8% | 53% |
| FrontierMath Tier 4 | 29.3% | 17.1% |
| OTIS Mock AIME 2024-2025 | 91.1% | 98.1% |
| ProofBench | 49% | 34% |
| LMArena Math | 1489 | 1407 |
Knowledge Grok 4.7 leads
GLM-5.3: 58.3 (#37), Grok 4.7: 62.8 (#22)
| Benchmark | GLM-5.3 | Grok 4.7 |
|---|---|---|
| GPQA Diamond | 90.9% | 92.7% |
| SimpleQA Verified | 41% | 56% |
| LMArena Expert | 1516 | 1422 |
Multimodal Not comparable
GLM-5.3: —, Grok 4.7: 35.5 (#87)
| Benchmark | GLM-5.3 | Grok 4.7 |
|---|---|---|
| LMArena Vision | — | 1228 |
| Blueprint-Bench 2 | — | 32.5% |
| Furniture Assembly | — | 20.8% |
Multilingual GLM-5.3 leads
GLM-5.3: 55.7 (#28), Grok 4.7: 50.8 (#116)
| Benchmark | GLM-5.3 | Grok 4.7 |
|---|---|---|
| LMArena Non-English | 1457 | 1389 |
| LMArena Chinese | 1528 | 1455 |
| LMArena French | 1499 | 1455 |
| LMArena Russian | 1463 | 1397 |
| LMArena Spanish | 1460 | 1400 |
| LMArena German | 1499 | — |
| LMArena Japanese | 1453 | — |
| LMArena Korean | 1472 | — |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.5 (#23), Grok 4.7: 74.1 (#105)
| Benchmark | GLM-5.3 | Grok 4.7 |
|---|---|---|
| LMArena Instruction Following | 1477 | 1404 |
Long Context GLM-5.3 leads
GLM-5.3: 45.4 (#41), Grok 4.7: 43.1 (#104)
| Benchmark | GLM-5.3 | Grok 4.7 |
|---|---|---|
| LMArena Longer Query | 1482 | 1413 |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.7 (#6), Grok 4.7: 70.0 (#24)
| Benchmark | GLM-5.3 | Grok 4.7 |
|---|---|---|
| LMArena Text | 1471 | 1399 |
| LMArena Creative Writing | 1457 | 1391 |
| EQ-Bench Creative Writing | 2075 | 2007 |
| LMArena Multi-Turn | 1472 | 1393 |
Frequently asked questions
Is GLM-5.3 better than Grok 4.7?
GLM-5.3 is the stronger model overall, scoring 54.8 to 53.1 on the Noometry Index.
Which is cheaper, GLM-5.3 or Grok 4.7?
GLM-5.3 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-5.3 or Grok 4.7 better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 58.0 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3 does, with 1M tokens against 500K.
How many benchmarks do GLM-5.3 and Grok 4.7 share?
35 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and Grok 4.7 has 39.