Model comparison
GLM-5 vs Grok 4.5
Grok 4.5 is the stronger model overall, scoring 55.0 to 46.1 on the Noometry Index. GLM-5 costs 1.9× less per token, which makes it the better buy when Grok 4.5's lead doesn't matter for your workload.
Last verified . 32 shared benchmarks.
Summary
- They share 32 benchmarks with published results for both. GLM-5 scores higher in 1 category and Grok 4.5 in 8 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.5 leads 56.1 to 27.6.
- The biggest single-benchmark swing is ARC-AGI-2: 4.9% for GLM-5 and 52.6% for Grok 4.5.
- GLM-5 is cheaper at $1 / $3.20 per million input/output tokens, against $2 / $6 for Grok 4.5.
- Grok 4.5 accepts more context: 500K tokens versus 205K.
- GLM-5 has downloadable open weights; the other is API-only.
Side by side
| GLM-5 | Grok 4.5 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | xAI |
| Noometry Index | 46.1 | 55.0 |
| Released | 2026-02-11 | 2026-07-08 |
| 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 | 52 |
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Category by category
Coding Grok 4.5 leads
GLM-5: 49.0 (#52), Grok 4.5: 52.2 (#35)
| Benchmark | GLM-5 | Grok 4.5 |
|---|---|---|
| LMArena WebDev | 1434 | 1553 |
| WeirdML | 48.2% | 46.4% |
| LMArena Coding | 1461 | 1474 |
| ALE-Bench | 765.62 | 1,309 |
| SWE-bench Verified | 72.1% | — |
| DeepSWE | — | 53.8% |
| FrontierCode | — | 42.4% |
| SWE-bench Verified (bash only) | 72.8% | — |
| SWE-bench Multilingual | 69.7% | — |
| SciCode | — | 54.1% |
Agentic & Tool Use Grok 4.5 leads
GLM-5: 31.1 (#71), Grok 4.5: 44.4 (#17)
| Benchmark | GLM-5 | Grok 4.5 |
|---|---|---|
| τ²-bench Banking | 9.8% | 47.9% |
| Vending-Bench 2 | 4,432 | 3,887 |
| Terminal-Bench | 52.4% | — |
| APEX-Agents | — | 56.2% |
| τ²-bench Airline | 82.5% | — |
| τ²-bench Retail | 73.7% | — |
| τ²-bench Telecom | 86.8% | — |
| PostTrainBench | — | 23.4% |
| GBAEval | — | 65.4% |
| GDP.pdf | — | 14% |
| LMArena Search | — | 1213 |
Reasoning Grok 4.5 leads
GLM-5: 27.6 (#116), Grok 4.5: 56.1 (#25)
| Benchmark | GLM-5 | Grok 4.5 |
|---|---|---|
| ARC-AGI-2 | 4.9% | 52.6% |
| SimpleBench | 53.2% | 70% |
| Kagi LLM Benchmark | 75% | 83.5% |
| NYT Connections (extended) | 74.8% | 79.9% |
| ARC-AGI-1 | 44.7% | 87.2% |
| Chess Puzzles | 10% | 36% |
| LMArena Hard Prompts | 1452 | 1462 |
| Epoch Capabilities Index | 145.83 | 153.92 |
| CritPt | — | 15.4% |
| DTBench | — | 96.5% |
| LMCA | — | 45.2% |
| Surface Evolver Bench | — | 74.4% |
| ForecastBench | 61 | — |
Math Grok 4.5 leads
GLM-5: 46.4 (#71), Grok 4.5: 60.9 (#35)
| Benchmark | GLM-5 | Grok 4.5 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 80% | 97.8% |
| LMArena Math | 1440 | 1459 |
| FrontierMath (Tiers 1-3) | — | 57.2% |
| FrontierMath Tier 4 | — | 24.4% |
| MathArena Final-Answer Competitions | 65.7% | — |
| ProofBench | — | 31% |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Grok 4.5 leads
GLM-5: 52.3 (#64), Grok 4.5: 62.3 (#24)
| Benchmark | GLM-5 | Grok 4.5 |
|---|---|---|
| GPQA Diamond | 87.8% | 93.4% |
| LMArena Expert | 1454 | 1466 |
| SimpleQA Verified | — | 48.3% |
| Vectara Hallucination Rate | 10.1% | — |
Multimodal Not comparable
GLM-5: —, Grok 4.5: 37.6 (#72)
| Benchmark | GLM-5 | Grok 4.5 |
|---|---|---|
| LMArena Vision | — | 1288 |
| Blueprint-Bench 2 | — | 27.3% |
| Furniture Assembly | — | 22.5% |
| LMArena Document | — | 1452 |
Multilingual Too close to call
GLM-5: 53.7 (#58), Grok 4.5: 54.4 (#42)
| Benchmark | GLM-5 | Grok 4.5 |
|---|---|---|
| LMArena Non-English | 1430 | 1440 |
| LMArena Chinese | 1511 | 1496 |
| LMArena French | 1455 | 1456 |
| LMArena German | 1445 | 1446 |
| LMArena Japanese | 1416 | 1428 |
| LMArena Korean | 1423 | 1404 |
| LMArena Russian | 1436 | 1448 |
| LMArena Spanish | 1454 | 1450 |
Instruction Following Too close to call
GLM-5: 75.2 (#67), Grok 4.5: 76.0 (#48)
| Benchmark | GLM-5 | Grok 4.5 |
|---|---|---|
| LMArena Instruction Following | 1428 | 1446 |
Long Context Too close to call
GLM-5: 44.7 (#60), Grok 4.5: 44.8 (#56)
| Benchmark | GLM-5 | Grok 4.5 |
|---|---|---|
| LMArena Longer Query | 1446 | 1463 |
| CL-bench | 18.7% | — |
Writing & Preference Too close to call
GLM-5: 66.0 (#38), Grok 4.5: 65.8 (#42)
| Benchmark | GLM-5 | Grok 4.5 |
|---|---|---|
| LMArena Text | 1446 | 1448 |
| LMArena Creative Writing | 1439 | 1442 |
| EQ-Bench Creative Writing | 1601 | 1579 |
| LMArena Multi-Turn | 1456 | 1456 |
Frequently asked questions
Is GLM-5 better than Grok 4.5?
Grok 4.5 is the stronger model overall, scoring 55.0 to 46.1 on the Noometry Index. GLM-5 costs 1.9× 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-5 or Grok 4.5?
GLM-5 is cheaper. It lists at $1 per million input tokens and $3.20 per million output tokens; Grok 4.5 lists at $2 and $6.
Is GLM-5 or Grok 4.5 better for coding?
Grok 4.5 scores higher on coding benchmarks: 52.2 versus 49.0 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-5 and Grok 4.5 share?
32 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and Grok 4.5 has 52.