Model comparison
GLM-4.6 vs Grok 4.6
Grok 4.6 is the stronger model overall, scoring 56.9 to 41.4 on the Noometry Index. GLM-4.6 costs 3.0× less per token, which makes it the better buy when Grok 4.6's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
Summary
- They share 21 benchmarks with published results for both. GLM-4.6 scores higher in 1 category and Grok 4.6 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.6 leads 61.4 to 23.7.
- The biggest single-benchmark swing is CritPt: 1.1% for GLM-4.6 and 19.7% for Grok 4.6.
- GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $2 / $6 for Grok 4.6.
- Grok 4.6 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 4.6 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | xAI |
| Noometry Index | 41.4 | 56.9 |
| Released | 2025-09-30 | 2026-08-12 |
| 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 | 49 |
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Category by category
Coding Grok 4.6 leads
GLM-4.6: 40.1 (#148), Grok 4.6: 58.5 (#16)
| Benchmark | GLM-4.6 | Grok 4.6 |
|---|---|---|
| LMArena WebDev | 1340 | 1617 |
| SciCode | 38.4% | 56.5% |
| LMArena Coding | 1449 | 1465 |
| ALE-Bench | 340.82 | 1,508 |
| DeepSWE | — | 67.5% |
| FrontierCode | — | 48% |
| SWE-bench Verified (bash only) | 55.4% | — |
| CursorBench | — | 41.4% |
| FrontierSWE | — | 25.3% |
| WeirdML | — | 67.3% |
Agentic & Tool Use Grok 4.6 leads
GLM-4.6: 32.3 (#66), Grok 4.6: 39.4 (#27)
| Benchmark | GLM-4.6 | Grok 4.6 |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| APEX-Agents | — | 65.3% |
| Berkeley Function Calling Leaderboard | 72.4% | — |
| GDP.pdf | — | 17.2% |
| Vending-Bench 2 | — | 9,047 |
Reasoning Grok 4.6 leads
GLM-4.6: 23.7 (#172), Grok 4.6: 61.4 (#20)
| Benchmark | GLM-4.6 | Grok 4.6 |
|---|---|---|
| CritPt | 1.1% | 19.7% |
| LMArena Hard Prompts | 1440 | 1447 |
| ARC-AGI-2 | — | 67.1% |
| SimpleBench | — | 75.9% |
| Kagi LLM Benchmark | 47.4% | — |
| NYT Connections (extended) | — | 80% |
| ARC-AGI-1 | — | 87.5% |
| Chess Puzzles | — | 40% |
| EBR-Bench | — | 30.5% |
| Mystery Game Puzzles | — | 34% |
| DTBench | — | 97.3% |
| LMCA | — | 48.5% |
| Epoch Capabilities Index | — | 156.44 |
Math Grok 4.6 leads
GLM-4.6: 39.1 (#111), Grok 4.6: 67.0 (#24)
| Benchmark | GLM-4.6 | Grok 4.6 |
|---|---|---|
| LMArena Math | 1432 | 1423 |
| FrontierMath (Tiers 1-3) | — | 66% |
| FrontierMath Tier 4 | — | 31.7% |
| OTIS Mock AIME 2024-2025 | — | 99.2% |
| ProofBench | — | 51% |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Grok 4.6 leads
GLM-4.6: 40.2 (#124), Grok 4.6: 63.3 (#20)
| Benchmark | GLM-4.6 | Grok 4.6 |
|---|---|---|
| LMArena Expert | 1431 | 1467 |
| GPQA Diamond | — | 94% |
| SimpleQA Verified | — | 49.3% |
| Vectara Hallucination Rate | 9.5% | — |
Multimodal Not comparable
GLM-4.6: —, Grok 4.6: 43.6 (#23)
| Benchmark | GLM-4.6 | Grok 4.6 |
|---|---|---|
| LMArena Vision | — | 1263 |
| Blueprint-Bench 2 | — | 33.2% |
| Furniture Assembly | — | 40% |
| LMArena Document | — | 1452 |
Multilingual Too close to call
GLM-4.6: 53.5 (#66), Grok 4.6: 53.0 (#74)
| Benchmark | GLM-4.6 | Grok 4.6 |
|---|---|---|
| LMArena Non-English | 1426 | 1420 |
| LMArena Chinese | 1499 | 1480 |
| LMArena French | 1459 | 1461 |
| LMArena German | 1447 | 1431 |
| LMArena Japanese | 1393 | 1376 |
| LMArena Korean | 1400 | 1397 |
| LMArena Russian | 1419 | 1422 |
| LMArena Spanish | 1436 | 1404 |
Instruction Following Grok 4.6 leads
GLM-4.6: 74.3 (#98), Grok 4.6: 75.4 (#63)
| Benchmark | GLM-4.6 | Grok 4.6 |
|---|---|---|
| LMArena Instruction Following | 1410 | 1431 |
Long Context Grok 4.6 leads
GLM-4.6: 43.4 (#94), Grok 4.6: 44.5 (#66)
| Benchmark | GLM-4.6 | Grok 4.6 |
|---|---|---|
| LMArena Longer Query | 1422 | 1454 |
Writing & Preference Grok 4.6 leads
GLM-4.6: 61.1 (#90), Grok 4.6: 62.3 (#80)
| Benchmark | GLM-4.6 | Grok 4.6 |
|---|---|---|
| LMArena Text | 1440 | 1428 |
| LMArena Creative Writing | 1411 | 1428 |
| LMArena Multi-Turn | 1427 | 1425 |
| EQ-Bench Creative Writing | 1411 | — |
Frequently asked questions
Is GLM-4.6 better than Grok 4.6?
Grok 4.6 is the stronger model overall, scoring 56.9 to 41.4 on the Noometry Index. GLM-4.6 costs 3.0× less per token, which makes it the better buy when Grok 4.6's lead doesn't matter for your workload.
Which is cheaper, GLM-4.6 or Grok 4.6?
GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Grok 4.6 lists at $2 and $6.
Is GLM-4.6 or Grok 4.6 better for coding?
Grok 4.6 scores higher on coding benchmarks: 58.5 versus 40.1 in the Noometry coding category.
Which has the bigger context window?
Grok 4.6 does, with 500K tokens against 205K.
How many benchmarks do GLM-4.6 and Grok 4.6 share?
21 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Grok 4.6 has 49.