Model comparison
GLM-5.3-Flash vs Grok 4.5
Grok 4.5 is the stronger model overall, scoring 55.0 to 49.6 on the Noometry Index. GLM-5.3-Flash costs 13× less per token, which makes it the better buy when Grok 4.5's lead doesn't matter for your workload.
Last verified . 34 shared benchmarks.
Summary
- They share 34 benchmarks with published results for both. GLM-5.3-Flash scores higher in 5 categories and Grok 4.5 in 5 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.5 leads 56.0 to 31.7.
- The biggest single-benchmark swing is Chess Puzzles: 14% for GLM-5.3-Flash and 36% for Grok 4.5.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $2 / $6 for Grok 4.5.
- GLM-5.3-Flash accepts more context: 1M tokens versus 500K.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3-Flash | Grok 4.5 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | xAI |
| Noometry Index | 49.6 | 55.0 |
| Released | 2026-08-20 | 2026-07-08 |
| Weights | Open | Proprietary |
| Context window | 1M | 500K |
| Max output | 131K | 500K |
| Input $ / M tokens | $0.15 | $2 |
| Output $ / M tokens | $0.50 | $6 |
| Results tracked | 36 | 52 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.7 (#26), Grok 4.5: 51.7 (#34)
| Benchmark | GLM-5.3-Flash | Grok 4.5 |
|---|---|---|
| DeepSWE | 63.4% | 53.8% |
| FrontierCode | 31.8% | 42.4% |
| LMArena WebDev | 1614 | 1552 |
| SciCode | 51.6% | 54.1% |
| LMArena Coding | 1506 | 1473 |
| ALE-Bench | 303.55 | 1,309 |
| WeirdML | — | 46.4% |
Agentic & Tool Use Grok 4.5 leads
GLM-5.3-Flash: 34.3 (#41), Grok 4.5: 44.4 (#15)
| Benchmark | GLM-5.3-Flash | Grok 4.5 |
|---|---|---|
| APEX-Agents | 52.8% | 56.2% |
| GDP.pdf | 14% | 14% |
| τ²-bench Banking | — | 47.9% |
| PostTrainBench | — | 23.4% |
| GBAEval | — | 65.4% |
| LMArena Search | — | 1213 |
| Vending-Bench 2 | — | 3,887 |
Reasoning Grok 4.5 leads
GLM-5.3-Flash: 31.7 (#69), Grok 4.5: 56.0 (#23)
| Benchmark | GLM-5.3-Flash | Grok 4.5 |
|---|---|---|
| CritPt | 15.4% | 15.4% |
| Chess Puzzles | 14% | 36% |
| LMArena Hard Prompts | 1490 | 1462 |
| Surface Evolver Bench | 52.5% | 74.4% |
| Epoch Capabilities Index | 151.9 | 154.02 |
| ARC-AGI-2 | — | 52.6% |
| SimpleBench | — | 70% |
| Kagi LLM Benchmark | — | 83.5% |
| NYT Connections (extended) | — | 79.9% |
| ARC-AGI-1 | — | 87.2% |
| Mystery Game Puzzles | 8% | — |
| DTBench | — | 96.5% |
| LMCA | — | 45.2% |
| Bench to the Future 3 | 0.15 | — |
Math Grok 4.5 leads
GLM-5.3-Flash: 53.1 (#38), Grok 4.5: 60.7 (#29)
| Benchmark | GLM-5.3-Flash | Grok 4.5 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 57.2% |
| FrontierMath Tier 4 | 17.1% | 24.4% |
| OTIS Mock AIME 2024-2025 | 93.9% | 97.8% |
| ProofBench | 21% | 31% |
| LMArena Math | 1499 | 1457 |
Knowledge Grok 4.5 leads
GLM-5.3-Flash: 58.4 (#33), Grok 4.5: 62.3 (#21)
| Benchmark | GLM-5.3-Flash | Grok 4.5 |
|---|---|---|
| GPQA Diamond | 90.2% | 93.4% |
| LMArena Expert | 1512 | 1466 |
| SimpleQA Verified | — | 48.3% |
Multimodal GLM-5.3-Flash leads
GLM-5.3-Flash: 42.6 (#25), Grok 4.5: 37.8 (#63)
| Benchmark | GLM-5.3-Flash | Grok 4.5 |
|---|---|---|
| LMArena Vision | 1304 | 1287 |
| Blueprint-Bench 2 | — | 27.3% |
| Furniture Assembly | — | 22.5% |
| LMArena Document | — | 1452 |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.2 (#24), Grok 4.5: 54.5 (#43)
| Benchmark | GLM-5.3-Flash | Grok 4.5 |
|---|---|---|
| LMArena Non-English | 1462 | 1439 |
| LMArena Chinese | 1529 | 1497 |
| LMArena French | 1495 | 1458 |
| LMArena German | 1457 | 1448 |
| LMArena Japanese | 1420 | 1425 |
| LMArena Korean | 1443 | 1402 |
| LMArena Russian | 1472 | 1447 |
| LMArena Spanish | 1471 | 1452 |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.8 (#17), Grok 4.5: 76.3 (#45)
| Benchmark | GLM-5.3-Flash | Grok 4.5 |
|---|---|---|
| LMArena Instruction Following | 1480 | 1446 |
Long Context Too close to call
GLM-5.3-Flash: 45.3 (#41), Grok 4.5: 44.8 (#54)
| Benchmark | GLM-5.3-Flash | Grok 4.5 |
|---|---|---|
| LMArena Longer Query | 1479 | 1463 |
Writing & Preference Too close to call
GLM-5.3-Flash: 65.4 (#49), Grok 4.5: 65.9 (#40)
| Benchmark | GLM-5.3-Flash | Grok 4.5 |
|---|---|---|
| LMArena Text | 1471 | 1448 |
| LMArena Creative Writing | 1440 | 1442 |
| LMArena Multi-Turn | 1471 | 1457 |
| EQ-Bench Creative Writing | — | 1579 |
Frequently asked questions
Is GLM-5.3-Flash better than Grok 4.5?
Grok 4.5 is the stronger model overall, scoring 55.0 to 49.6 on the Noometry Index. GLM-5.3-Flash costs 13× 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-5.3-Flash or Grok 4.5?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Grok 4.5 lists at $2 and $6.
Is GLM-5.3-Flash or Grok 4.5 better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 51.7 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3-Flash does, with 1M tokens against 500K.
How many benchmarks do GLM-5.3-Flash and Grok 4.5 share?
34 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and Grok 4.5 has 52.