Model comparison
GLM-5 vs Grok 4.7
Grok 4.7 is the stronger model overall, scoring 53.1 to 46.1 on the Noometry Index. GLM-5 costs 1.9× less per token, which makes it the better buy when Grok 4.7's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
Summary
- They share 22 benchmarks with published results for both. GLM-5 scores higher in 3 categories and Grok 4.7 in 6 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.7 leads 49.1 to 27.6.
- The biggest single-benchmark swing is Chess Puzzles: 10% for GLM-5 and 38% for Grok 4.7.
- GLM-5 is cheaper at $1 / $3.20 per million input/output tokens, against $2 / $6 for Grok 4.7.
- Grok 4.7 accepts more context: 500K tokens versus 205K.
- GLM-5 has downloadable open weights; the other is API-only.
Side by side
| GLM-5 | Grok 4.7 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | xAI |
| Noometry Index | 46.1 | 53.1 |
| Released | 2026-02-11 | 2026-09-21 |
| Weights | Open | Proprietary |
| Context window | 205K | 500K |
| Max output | 131K | 500K |
| Input $ / M tokens | $1 | $2 |
| Output $ / M tokens | $3.20 | $6 |
| Results tracked | 45 | 39 |
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Category by category
Coding Grok 4.7 leads
GLM-5: 49.0 (#52), Grok 4.7: 58.0 (#18)
| Benchmark | GLM-5 | Grok 4.7 |
|---|---|---|
| LMArena WebDev | 1434 | 1639 |
| LMArena Coding | 1461 | 1427 |
| SWE-bench Verified | 72.1% | — |
| FrontierCode | — | 47.6% |
| SWE-bench Verified (bash only) | 72.8% | — |
| CursorBench | — | 46.3% |
| SWE-bench Multilingual | 69.7% | — |
| FrontierSWE | — | 29.5% |
| SciCode | — | 57.8% |
| WeirdML | 48.2% | — |
| ALE-Bench | 765.62 | — |
Agentic & Tool Use Grok 4.7 leads
GLM-5: 31.1 (#71), Grok 4.7: 36.7 (#37)
| Benchmark | GLM-5 | Grok 4.7 |
|---|---|---|
| Vending-Bench 2 | 4,432 | 10,537 |
| Terminal-Bench | 52.4% | — |
| APEX-Agents | — | 54.6% |
| τ²-bench Airline | 82.5% | — |
| τ²-bench Banking | 9.8% | — |
| τ²-bench Retail | 73.7% | — |
| τ²-bench Telecom | 86.8% | — |
| GDP.pdf | — | 22.8% |
Reasoning Grok 4.7 leads
GLM-5: 27.6 (#116), Grok 4.7: 49.1 (#40)
| Benchmark | GLM-5 | Grok 4.7 |
|---|---|---|
| NYT Connections (extended) | 74.8% | 76.8% |
| Chess Puzzles | 10% | 38% |
| LMArena Hard Prompts | 1452 | 1413 |
| Epoch Capabilities Index | 145.83 | 153.53 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 53.2% | — |
| Kagi LLM Benchmark | 75% | — |
| ARC-AGI-1 | 44.7% | — |
| CritPt | — | 18% |
| Mystery Game Puzzles | — | 29% |
| DTBench | — | 96% |
| LMCA | — | 49.4% |
| ForecastBench | 61 | — |
Math Grok 4.7 leads
GLM-5: 46.4 (#71), Grok 4.7: 57.8 (#39)
| Benchmark | GLM-5 | Grok 4.7 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 80% | 98.1% |
| LMArena Math | 1440 | 1407 |
| FrontierMath (Tiers 1-3) | — | 53% |
| FrontierMath Tier 4 | — | 17.1% |
| MathArena Final-Answer Competitions | 65.7% | — |
| ProofBench | — | 34% |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Grok 4.7 leads
GLM-5: 52.3 (#64), Grok 4.7: 62.8 (#22)
| Benchmark | GLM-5 | Grok 4.7 |
|---|---|---|
| GPQA Diamond | 87.8% | 92.7% |
| LMArena Expert | 1454 | 1422 |
| SimpleQA Verified | — | 56% |
| Vectara Hallucination Rate | 10.1% | — |
Multimodal Not comparable
GLM-5: —, Grok 4.7: 35.5 (#87)
| Benchmark | GLM-5 | Grok 4.7 |
|---|---|---|
| LMArena Vision | — | 1228 |
| Blueprint-Bench 2 | — | 32.5% |
| Furniture Assembly | — | 20.8% |
Multilingual GLM-5 leads
GLM-5: 53.7 (#58), Grok 4.7: 50.8 (#116)
| Benchmark | GLM-5 | Grok 4.7 |
|---|---|---|
| LMArena Non-English | 1430 | 1389 |
| LMArena Chinese | 1511 | 1455 |
| LMArena French | 1455 | 1455 |
| LMArena Russian | 1436 | 1397 |
| LMArena Spanish | 1454 | 1400 |
| LMArena German | 1445 | — |
| LMArena Japanese | 1416 | — |
| LMArena Korean | 1423 | — |
Instruction Following GLM-5 leads
GLM-5: 75.2 (#67), Grok 4.7: 74.1 (#105)
| Benchmark | GLM-5 | Grok 4.7 |
|---|---|---|
| LMArena Instruction Following | 1428 | 1404 |
Long Context GLM-5 leads
GLM-5: 44.7 (#60), Grok 4.7: 43.1 (#104)
| Benchmark | GLM-5 | Grok 4.7 |
|---|---|---|
| LMArena Longer Query | 1446 | 1413 |
| CL-bench | 18.7% | — |
Writing & Preference Grok 4.7 leads
GLM-5: 66.0 (#38), Grok 4.7: 70.0 (#24)
| Benchmark | GLM-5 | Grok 4.7 |
|---|---|---|
| LMArena Text | 1446 | 1399 |
| LMArena Creative Writing | 1439 | 1391 |
| EQ-Bench Creative Writing | 1601 | 2007 |
| LMArena Multi-Turn | 1456 | 1393 |
Frequently asked questions
Is GLM-5 better than Grok 4.7?
Grok 4.7 is the stronger model overall, scoring 53.1 to 46.1 on the Noometry Index. GLM-5 costs 1.9× less per token, which makes it the better buy when Grok 4.7's lead doesn't matter for your workload.
Which is cheaper, GLM-5 or Grok 4.7?
GLM-5 is cheaper. It lists at $1 per million input tokens and $3.20 per million output tokens; Grok 4.7 lists at $2 and $6.
Is GLM-5 or Grok 4.7 better for coding?
Grok 4.7 scores higher on coding benchmarks: 58.0 versus 49.0 in the Noometry coding category.
Which has the bigger context window?
Grok 4.7 does, with 500K tokens against 205K.
How many benchmarks do GLM-5 and Grok 4.7 share?
22 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and Grok 4.7 has 39.