Model comparison
GLM-4.6 vs Grok 4.5
Grok 4.5 is the stronger model overall, scoring 55.0 to 40.9 on the Noometry Index. GLM-4.6 costs 3.0× less per token, which makes it the better buy when Grok 4.5's lead doesn't matter for your workload.
Last verified . 23 shared benchmarks.
Summary
- They share 23 benchmarks with published results for both. GLM-4.6 scores higher in 0 categories and Grok 4.5 in 9 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.5 leads 56.0 to 23.1.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 47.4% for GLM-4.6 and 83.5% for Grok 4.5.
- GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $2 / $6 for Grok 4.5.
- Grok 4.5 accepts more context: 500K tokens versus 205K.
- GLM-4.6 has downloadable open weights; the other is API-only.
Side by side
| GLM-4.6 | Grok 4.5 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | xAI |
| Noometry Index | 40.9 | 55.0 |
| Released | 2025-09-30 | 2026-07-08 |
| Weights | Open | Proprietary |
| Context window | 205K | 500K |
| Max output | 131K | 500K |
| Input $ / M tokens | $0.60 | $2 |
| Output $ / M tokens | $2.20 | $6 |
| Results tracked | 29 | 52 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Grok 4.5 leads
GLM-4.6: 39.9 (#137), Grok 4.5: 51.7 (#34)
| Benchmark | GLM-4.6 | Grok 4.5 |
|---|---|---|
| LMArena WebDev | 1339 | 1552 |
| SciCode | 38.4% | 54.1% |
| LMArena Coding | 1449 | 1473 |
| ALE-Bench | 340.82 | 1,309 |
| DeepSWE | — | 53.8% |
| FrontierCode | — | 42.4% |
| SWE-bench Verified (bash only) | 55.4% | — |
| WeirdML | — | 46.4% |
Agentic & Tool Use Grok 4.5 leads
GLM-4.6: 32.1 (#60), Grok 4.5: 44.4 (#15)
| Benchmark | GLM-4.6 | Grok 4.5 |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| APEX-Agents | — | 56.2% |
| Berkeley Function Calling Leaderboard | 72.4% | — |
| τ²-bench Banking | — | 47.9% |
| PostTrainBench | — | 23.4% |
| GBAEval | — | 65.4% |
| GDP.pdf | — | 14% |
| LMArena Search | — | 1213 |
| Vending-Bench 2 | — | 3,887 |
Reasoning Grok 4.5 leads
GLM-4.6: 23.1 (#168), Grok 4.5: 56.0 (#23)
| Benchmark | GLM-4.6 | Grok 4.5 |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | 83.5% |
| CritPt | 1.1% | 15.4% |
| LMArena Hard Prompts | 1440 | 1462 |
| ARC-AGI-2 | — | 52.6% |
| SimpleBench | — | 70% |
| NYT Connections (extended) | — | 79.9% |
| ARC-AGI-1 | — | 87.2% |
| Chess Puzzles | — | 36% |
| DTBench | — | 96.5% |
| LMCA | — | 45.2% |
| Surface Evolver Bench | — | 74.4% |
| Epoch Capabilities Index | — | 154.02 |
Math Grok 4.5 leads
GLM-4.6: 36.4 (#115), Grok 4.5: 60.7 (#29)
| Benchmark | GLM-4.6 | Grok 4.5 |
|---|---|---|
| LMArena Math | 1431 | 1457 |
| FrontierMath (Tiers 1-3) | — | 57.2% |
| FrontierMath Tier 4 | — | 24.4% |
| OTIS Mock AIME 2024-2025 | — | 97.8% |
| ProofBench | — | 31% |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Grok 4.5 leads
GLM-4.6: 39.6 (#118), Grok 4.5: 62.3 (#21)
| Benchmark | GLM-4.6 | Grok 4.5 |
|---|---|---|
| LMArena Expert | 1432 | 1466 |
| GPQA Diamond | — | 93.4% |
| SimpleQA Verified | — | 48.3% |
| Vectara Hallucination Rate | 9.5% | — |
Multimodal Not comparable
GLM-4.6: —, Grok 4.5: 37.8 (#63)
| Benchmark | GLM-4.6 | Grok 4.5 |
|---|---|---|
| LMArena Vision | — | 1287 |
| Blueprint-Bench 2 | — | 27.3% |
| Furniture Assembly | — | 22.5% |
| LMArena Document | — | 1452 |
Multilingual Too close to call
GLM-4.6: 53.6 (#62), Grok 4.5: 54.5 (#43)
| Benchmark | GLM-4.6 | Grok 4.5 |
|---|---|---|
| LMArena Non-English | 1426 | 1439 |
| LMArena Chinese | 1500 | 1497 |
| LMArena French | 1459 | 1458 |
| LMArena German | 1447 | 1448 |
| LMArena Japanese | 1394 | 1425 |
| LMArena Korean | 1401 | 1402 |
| LMArena Russian | 1418 | 1447 |
| LMArena Spanish | 1436 | 1452 |
Instruction Following Grok 4.5 leads
GLM-4.6: 74.5 (#93), Grok 4.5: 76.3 (#45)
| Benchmark | GLM-4.6 | Grok 4.5 |
|---|---|---|
| LMArena Instruction Following | 1410 | 1446 |
Long Context Grok 4.5 leads
GLM-4.6: 43.4 (#91), Grok 4.5: 44.8 (#54)
| Benchmark | GLM-4.6 | Grok 4.5 |
|---|---|---|
| LMArena Longer Query | 1423 | 1463 |
Writing & Preference Grok 4.5 leads
GLM-4.6: 61.3 (#86), Grok 4.5: 65.9 (#40)
| Benchmark | GLM-4.6 | Grok 4.5 |
|---|---|---|
| LMArena Text | 1440 | 1448 |
| LMArena Creative Writing | 1411 | 1442 |
| EQ-Bench Creative Writing | 1411 | 1579 |
| LMArena Multi-Turn | 1427 | 1457 |
Frequently asked questions
Is GLM-4.6 better than Grok 4.5?
Grok 4.5 is the stronger model overall, scoring 55.0 to 40.9 on the Noometry Index. GLM-4.6 costs 3.0× less per token, which makes it the better buy when Grok 4.5's lead doesn't matter for your workload.
Which is cheaper, GLM-4.6 or Grok 4.5?
GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Grok 4.5 lists at $2 and $6.
Is GLM-4.6 or Grok 4.5 better for coding?
Grok 4.5 scores higher on coding benchmarks: 51.7 versus 39.9 in the Noometry coding category.
Which has the bigger context window?
Grok 4.5 does, with 500K tokens against 205K.
How many benchmarks do GLM-4.6 and Grok 4.5 share?
23 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Grok 4.5 has 52.