Model comparison
Claude Opus 4.5 vs GLM-5.3-Flash
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 50.5 on the Noometry Index.
Last verified . 29 shared benchmarks.
Summary
- They share 29 benchmarks with published results for both. Claude Opus 4.5 scores higher in 5 categories and GLM-5.3-Flash in 5 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3-Flash leads 53.3 to 38.6.
- The biggest single-benchmark swing is ARC-AGI-2: 37.6% for Claude Opus 4.5 and 65.8% for GLM-5.3-Flash.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $5 / $25 for Claude Opus 4.5.
- GLM-5.3-Flash accepts more context: 1M tokens versus 200K.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.
Side by side
| Claude Opus 4.5 | GLM-5.3-Flash | |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 50.5 | 51.8 |
| Released | 2025-11-01 | 2026-08-20 |
| Weights | Proprietary | Open |
| Context window | 200K | 1M |
| Max output | 64K | 131K |
| Input $ / M tokens | $5 | $0.15 |
| Output $ / M tokens | $25 | $0.50 |
| Results tracked | 69 | 40 |
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Category by category
Coding Claude Opus 4.5 leads
Claude Opus 4.5: 54.8 (#27), GLM-5.3-Flash: 53.1 (#31)
| Benchmark | Claude Opus 4.5 | GLM-5.3-Flash |
|---|---|---|
| LMArena WebDev | 1494 | 1609 |
| LMArena Coding | 1504 | 1508 |
| ALE-Bench | 1,025 | 303.55 |
| SWE-bench Verified | 76.7% | — |
| DeepSWE | — | 63.4% |
| FrontierCode | — | 31.8% |
| SWE-bench Verified (bash only) | 76.8% | — |
| CursorBench | — | 36.8% |
| SWE-bench Multilingual | 70.7% | — |
| FrontierSWE | — | 18.1% |
| SciCode | — | 51.6% |
| GSO | 26.5% | — |
| WeirdML | 63.7% | — |
| AlgoTune | 1.77 | — |
Agentic & Tool Use Claude Opus 4.5 leads
Claude Opus 4.5: 47.3 (#12), GLM-5.3-Flash: 34.2 (#47)
| Benchmark | Claude Opus 4.5 | GLM-5.3-Flash |
|---|---|---|
| Terminal-Bench | 63.1% | — |
| APEX-Agents | — | 52.8% |
| Berkeley Function Calling Leaderboard | 77.5% | — |
| GDPval | 45.5% | — |
| Remote Labor Index | 3.8% | — |
| τ²-bench Airline | 84% | — |
| τ²-bench Banking | 24.7% | — |
| τ²-bench Retail | 79.6% | — |
| τ²-bench Telecom | 92.3% | — |
| Cybench | 82% | — |
| DeepResearch Bench | 54.8% | — |
| OSWorld | 66.3% | — |
| BALROG | 43.5% | — |
| GDP.pdf | — | 14% |
| LMArena Search | 1180 | — |
| METR Time Horizons | 75% | — |
| Vending-Bench 2 | 4,967 | — |
Reasoning GLM-5.3-Flash leads
Claude Opus 4.5: 42.6 (#51), GLM-5.3-Flash: 48.0 (#42)
| Benchmark | Claude Opus 4.5 | GLM-5.3-Flash |
|---|---|---|
| ARC-AGI-2 | 37.6% | 65.8% |
| ARC-AGI-1 | 80% | 91% |
| Chess Puzzles | 12% | 14% |
| LMArena Hard Prompts | 1476 | 1491 |
| Mystery Game Puzzles | 22% | 8% |
| Epoch Capabilities Index | 150.09 | 151.88 |
| SimpleBench | 62% | — |
| Kagi LLM Benchmark | 80.2% | — |
| NYT Connections (extended) | 52.5% | — |
| CritPt | — | 15.4% |
| EnigmaEval | 11.9% | — |
| EBR-Bench | 14.3% | — |
| DTBench | 89.9% | — |
| LMCA | 44.5% | — |
| Surface Evolver Bench | — | 52.5% |
| Bench to the Future 3 | — | 0.15 |
| ForecastBench | 60.7 | — |
Math GLM-5.3-Flash leads
Claude Opus 4.5: 38.6 (#132), GLM-5.3-Flash: 53.3 (#47)
| Benchmark | Claude Opus 4.5 | GLM-5.3-Flash |
|---|---|---|
| FrontierMath (Tiers 1-3) | 34.4% | 55.8% |
| FrontierMath Tier 4 | 4.9% | 17.1% |
| OTIS Mock AIME 2024-2025 | 86.1% | 93.9% |
| ProofBench | 36% | 21% |
| LMArena Math | 1463 | 1500 |
| FrontierMath (Feb 2025 set) | 20.7% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge GLM-5.3-Flash leads
Claude Opus 4.5: 56.5 (#44), GLM-5.3-Flash: 58.4 (#36)
| Benchmark | Claude Opus 4.5 | GLM-5.3-Flash |
|---|---|---|
| GPQA Diamond | 86% | 90.2% |
| LMArena Expert | 1487 | 1513 |
| Humanity's Last Exam | 25.2% | — |
| SimpleQA Verified | 45.7% | — |
| Vectara Hallucination Rate | 10.9% | — |
Multimodal GLM-5.3-Flash leads
Claude Opus 4.5: 31.4 (#107), GLM-5.3-Flash: 42.8 (#27)
| Benchmark | Claude Opus 4.5 | GLM-5.3-Flash |
|---|---|---|
| LMArena Vision | — | 1296 |
| GeoBench | 75% | — |
| VPCT | 40% | — |
| Furniture Assembly | 28.3% | — |
| LMArena Document | 1462 | — |
Multilingual GLM-5.3-Flash leads
Claude Opus 4.5: 54.3 (#47), GLM-5.3-Flash: 56.0 (#25)
| Benchmark | Claude Opus 4.5 | GLM-5.3-Flash |
|---|---|---|
| LMArena Non-English | 1438 | 1462 |
| LMArena Chinese | 1470 | 1527 |
| LMArena French | 1471 | 1496 |
| LMArena German | 1449 | 1470 |
| LMArena Japanese | 1416 | 1429 |
| LMArena Korean | 1424 | 1446 |
| LMArena Russian | 1447 | 1469 |
| LMArena Spanish | 1458 | 1471 |
Instruction Following Too close to call
Claude Opus 4.5: 77.5 (#19), GLM-5.3-Flash: 77.5 (#20)
| Benchmark | Claude Opus 4.5 | GLM-5.3-Flash |
|---|---|---|
| LMArena Instruction Following | 1478 | 1478 |
Long Context Claude Opus 4.5 leads
Claude Opus 4.5: 46.5 (#22), GLM-5.3-Flash: 45.4 (#39)
| Benchmark | Claude Opus 4.5 | GLM-5.3-Flash |
|---|---|---|
| LMArena Longer Query | 1480 | 1482 |
| CL-bench | 21.1% | — |
Writing & Preference Claude Opus 4.5 leads
Claude Opus 4.5: 68.1 (#28), GLM-5.3-Flash: 65.3 (#50)
| Benchmark | Claude Opus 4.5 | GLM-5.3-Flash |
|---|---|---|
| LMArena Text | 1451 | 1471 |
| LMArena Creative Writing | 1445 | 1442 |
| LMArena Multi-Turn | 1466 | 1467 |
| EQ-Bench Creative Writing | 1687 | — |
Frequently asked questions
Is Claude Opus 4.5 better than GLM-5.3-Flash?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 50.5 on the Noometry Index.
Which is cheaper, Claude Opus 4.5 or GLM-5.3-Flash?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Claude Opus 4.5 lists at $5 and $25.
Is Claude Opus 4.5 or GLM-5.3-Flash better for coding?
Claude Opus 4.5 scores higher on coding benchmarks: 54.8 versus 53.1 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3-Flash does, with 1M tokens against 200K.
How many benchmarks do Claude Opus 4.5 and GLM-5.3-Flash share?
29 benchmarks have published results for both models. Claude Opus 4.5 has 69 scored results on Noometry and GLM-5.3-Flash has 40.