Model comparison
Claude Opus 4.5 vs GLM-5.3
GLM-5.3 is the stronger model overall, scoring 54.8 to 50.5 on the Noometry Index.
Last verified . 34 shared benchmarks.
Summary
- They share 34 benchmarks with published results for both. Claude Opus 4.5 scores higher in 3 categories and GLM-5.3 in 6 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3 leads 62.3 to 38.6.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 34.4% for Claude Opus 4.5 and 68.8% for GLM-5.3.
- GLM-5.3 is cheaper at $1.40 / $4.40 per million input/output tokens, against $5 / $25 for Claude Opus 4.5.
- GLM-5.3 accepts more context: 1M tokens versus 200K.
- GLM-5.3 has downloadable open weights; the other is API-only.
Side by side
| Claude Opus 4.5 | GLM-5.3 | |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 50.5 | 54.8 |
| Released | 2025-11-01 | 2026-08-14 |
| Weights | Proprietary | Open |
| Context window | 200K | 1M |
| Max output | 64K | 131K |
| Input $ / M tokens | $5 | $1.40 |
| Output $ / M tokens | $25 | $4.40 |
| Results tracked | 69 | 42 |
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Category by category
Coding GLM-5.3 leads
Claude Opus 4.5: 54.8 (#27), GLM-5.3: 59.5 (#14)
| Benchmark | Claude Opus 4.5 | GLM-5.3 |
|---|---|---|
| LMArena WebDev | 1494 | 1622 |
| WeirdML | 63.7% | 75.4% |
| LMArena Coding | 1504 | 1496 |
| ALE-Bench | 1,025 | 1,317 |
| SWE-bench Verified | 76.7% | — |
| DeepSWE | — | 69% |
| FrontierCode | — | 40.1% |
| SWE-bench Verified (bash only) | 76.8% | — |
| CursorBench | — | 42.6% |
| SWE-bench Multilingual | 70.7% | — |
| FrontierSWE | — | 30.2% |
| SciCode | — | 59% |
| GSO | 26.5% | — |
| AlgoTune | 1.77 | — |
Agentic & Tool Use Claude Opus 4.5 leads
Claude Opus 4.5: 47.3 (#12), GLM-5.3: 36.4 (#38)
| Benchmark | Claude Opus 4.5 | GLM-5.3 |
|---|---|---|
| Vending-Bench 2 | 4,967 | 8,164 |
| Terminal-Bench | 63.1% | — |
| APEX-Agents | — | 56.6% |
| 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% | — |
| LMArena Search | 1180 | — |
| METR Time Horizons | 75% | — |
Reasoning GLM-5.3 leads
Claude Opus 4.5: 42.6 (#51), GLM-5.3: 46.1 (#46)
| Benchmark | Claude Opus 4.5 | GLM-5.3 |
|---|---|---|
| NYT Connections (extended) | 52.5% | 74.2% |
| Chess Puzzles | 12% | 21% |
| LMArena Hard Prompts | 1476 | 1489 |
| Mystery Game Puzzles | 22% | 33% |
| DTBench | 89.9% | 87.7% |
| LMCA | 44.5% | 55.5% |
| Epoch Capabilities Index | 150.09 | 155.61 |
| ARC-AGI-2 | 37.6% | — |
| SimpleBench | 62% | — |
| Kagi LLM Benchmark | 80.2% | — |
| ARC-AGI-1 | 80% | — |
| CritPt | — | 19.1% |
| EnigmaEval | 11.9% | — |
| EBR-Bench | 14.3% | — |
| Bench to the Future 3 | — | 0.15 |
| ForecastBench | 60.7 | — |
Math GLM-5.3 leads
Claude Opus 4.5: 38.6 (#132), GLM-5.3: 62.3 (#33)
| Benchmark | Claude Opus 4.5 | GLM-5.3 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 34.4% | 68.8% |
| FrontierMath Tier 4 | 4.9% | 29.3% |
| OTIS Mock AIME 2024-2025 | 86.1% | 91.1% |
| ProofBench | 36% | 49% |
| LMArena Math | 1463 | 1489 |
| FrontierMath (Feb 2025 set) | 20.7% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge GLM-5.3 leads
Claude Opus 4.5: 56.5 (#44), GLM-5.3: 58.3 (#37)
| Benchmark | Claude Opus 4.5 | GLM-5.3 |
|---|---|---|
| GPQA Diamond | 86% | 90.9% |
| SimpleQA Verified | 45.7% | 41% |
| LMArena Expert | 1487 | 1516 |
| Humanity's Last Exam | 25.2% | — |
| Vectara Hallucination Rate | 10.9% | — |
Multimodal Not comparable
Claude Opus 4.5: 31.4 (#107), GLM-5.3: —
| Benchmark | Claude Opus 4.5 | GLM-5.3 |
|---|---|---|
| GeoBench | 75% | — |
| VPCT | 40% | — |
| Furniture Assembly | 28.3% | — |
| LMArena Document | 1462 | — |
Multilingual GLM-5.3 leads
Claude Opus 4.5: 54.3 (#47), GLM-5.3: 55.7 (#28)
| Benchmark | Claude Opus 4.5 | GLM-5.3 |
|---|---|---|
| LMArena Non-English | 1438 | 1457 |
| LMArena Chinese | 1470 | 1528 |
| LMArena French | 1471 | 1499 |
| LMArena German | 1449 | 1499 |
| LMArena Japanese | 1416 | 1453 |
| LMArena Korean | 1424 | 1472 |
| LMArena Russian | 1447 | 1463 |
| LMArena Spanish | 1458 | 1460 |
Instruction Following Too close to call
Claude Opus 4.5: 77.5 (#19), GLM-5.3: 77.5 (#23)
| Benchmark | Claude Opus 4.5 | GLM-5.3 |
|---|---|---|
| LMArena Instruction Following | 1478 | 1477 |
Long Context Claude Opus 4.5 leads
Claude Opus 4.5: 46.5 (#22), GLM-5.3: 45.4 (#41)
| Benchmark | Claude Opus 4.5 | GLM-5.3 |
|---|---|---|
| LMArena Longer Query | 1480 | 1482 |
| CL-bench | 21.1% | — |
Writing & Preference GLM-5.3 leads
Claude Opus 4.5: 68.1 (#28), GLM-5.3: 75.7 (#6)
| Benchmark | Claude Opus 4.5 | GLM-5.3 |
|---|---|---|
| LMArena Text | 1451 | 1471 |
| LMArena Creative Writing | 1445 | 1457 |
| EQ-Bench Creative Writing | 1687 | 2075 |
| LMArena Multi-Turn | 1466 | 1472 |
Frequently asked questions
Is Claude Opus 4.5 better than GLM-5.3?
GLM-5.3 is the stronger model overall, scoring 54.8 to 50.5 on the Noometry Index.
Which is cheaper, Claude Opus 4.5 or GLM-5.3?
GLM-5.3 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; Claude Opus 4.5 lists at $5 and $25.
Is Claude Opus 4.5 or GLM-5.3 better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 54.8 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3 does, with 1M tokens against 200K.
How many benchmarks do Claude Opus 4.5 and GLM-5.3 share?
34 benchmarks have published results for both models. Claude Opus 4.5 has 69 scored results on Noometry and GLM-5.3 has 42.