Model comparison
Claude Opus 4.5 vs GLM-4.6
Claude Opus 4.5 is the stronger model overall, scoring 50.5 to 41.4 on the Noometry Index. GLM-4.6 costs 10× less per token, which makes it the better buy when Claude Opus 4.5's lead doesn't matter for your workload.
Last verified . 27 shared benchmarks.
Summary
- They share 27 benchmarks with published results for both. Claude Opus 4.5 scores higher in 8 categories and GLM-4.6 in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Claude Opus 4.5 leads 42.6 to 23.7.
- The biggest single-benchmark swing is Terminal-Bench: 63.1% for Claude Opus 4.5 and 24.5% for GLM-4.6.
- GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $5 / $25 for Claude Opus 4.5.
- GLM-4.6 accepts more context: 205K tokens versus 200K.
- GLM-4.6 has downloadable open weights; the other is API-only.
Side by side
| Claude Opus 4.5 | GLM-4.6 | |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 50.5 | 41.4 |
| Released | 2025-11-01 | 2025-09-30 |
| Weights | Proprietary | Open |
| Context window | 200K | 205K |
| Max output | 64K | 131K |
| Input $ / M tokens | $5 | $0.60 |
| Output $ / M tokens | $25 | $2.20 |
| Results tracked | 69 | 29 |
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Category by category
Coding Claude Opus 4.5 leads
Claude Opus 4.5: 54.8 (#27), GLM-4.6: 40.1 (#148)
| Benchmark | Claude Opus 4.5 | GLM-4.6 |
|---|---|---|
| SWE-bench Verified (bash only) | 76.8% | 55.4% |
| LMArena WebDev | 1494 | 1340 |
| LMArena Coding | 1504 | 1449 |
| ALE-Bench | 1,025 | 340.82 |
| SWE-bench Verified | 76.7% | — |
| SWE-bench Multilingual | 70.7% | — |
| SciCode | — | 38.4% |
| 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-4.6: 32.3 (#66)
| Benchmark | Claude Opus 4.5 | GLM-4.6 |
|---|---|---|
| Terminal-Bench | 63.1% | 24.5% |
| Berkeley Function Calling Leaderboard | 77.5% | 72.4% |
| 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% | — |
| Vending-Bench 2 | 4,967 | — |
Reasoning Claude Opus 4.5 leads
Claude Opus 4.5: 42.6 (#51), GLM-4.6: 23.7 (#172)
| Benchmark | Claude Opus 4.5 | GLM-4.6 |
|---|---|---|
| Kagi LLM Benchmark | 80.2% | 47.4% |
| LMArena Hard Prompts | 1476 | 1440 |
| ARC-AGI-2 | 37.6% | — |
| SimpleBench | 62% | — |
| NYT Connections (extended) | 52.5% | — |
| ARC-AGI-1 | 80% | — |
| CritPt | — | 1.1% |
| Chess Puzzles | 12% | — |
| EnigmaEval | 11.9% | — |
| EBR-Bench | 14.3% | — |
| Mystery Game Puzzles | 22% | — |
| DTBench | 89.9% | — |
| LMCA | 44.5% | — |
| Epoch Capabilities Index | 150.09 | — |
| ForecastBench | 60.7 | — |
Math Too close to call
Claude Opus 4.5: 38.6 (#132), GLM-4.6: 39.1 (#111)
| Benchmark | Claude Opus 4.5 | GLM-4.6 |
|---|---|---|
| LMArena Math | 1463 | 1432 |
| FrontierMath (Feb 2025 set) | 20.7% | 3.8% |
| FrontierMath Tier 4 (v1) | 4.2% | 2.1% |
| FrontierMath (Tiers 1-3) | 34.4% | — |
| FrontierMath Tier 4 | 4.9% | — |
| OTIS Mock AIME 2024-2025 | 86.1% | — |
| ProofBench | 36% | — |
Knowledge Claude Opus 4.5 leads
Claude Opus 4.5: 56.5 (#44), GLM-4.6: 40.2 (#124)
| Benchmark | Claude Opus 4.5 | GLM-4.6 |
|---|---|---|
| Vectara Hallucination Rate | 10.9% | 9.5% |
| LMArena Expert | 1487 | 1431 |
| GPQA Diamond | 86% | — |
| Humanity's Last Exam | 25.2% | — |
| SimpleQA Verified | 45.7% | — |
Multimodal Not comparable
Claude Opus 4.5: 31.4 (#107), GLM-4.6: —
| Benchmark | Claude Opus 4.5 | GLM-4.6 |
|---|---|---|
| GeoBench | 75% | — |
| VPCT | 40% | — |
| Furniture Assembly | 28.3% | — |
| LMArena Document | 1462 | — |
Multilingual Too close to call
Claude Opus 4.5: 54.3 (#47), GLM-4.6: 53.5 (#66)
| Benchmark | Claude Opus 4.5 | GLM-4.6 |
|---|---|---|
| LMArena Non-English | 1438 | 1426 |
| LMArena Chinese | 1470 | 1499 |
| LMArena French | 1471 | 1459 |
| LMArena German | 1449 | 1447 |
| LMArena Japanese | 1416 | 1393 |
| LMArena Korean | 1424 | 1400 |
| LMArena Russian | 1447 | 1419 |
| LMArena Spanish | 1458 | 1436 |
Instruction Following Claude Opus 4.5 leads
Claude Opus 4.5: 77.5 (#19), GLM-4.6: 74.3 (#98)
| Benchmark | Claude Opus 4.5 | GLM-4.6 |
|---|---|---|
| LMArena Instruction Following | 1478 | 1410 |
Long Context Claude Opus 4.5 leads
Claude Opus 4.5: 46.5 (#22), GLM-4.6: 43.4 (#94)
| Benchmark | Claude Opus 4.5 | GLM-4.6 |
|---|---|---|
| LMArena Longer Query | 1480 | 1422 |
| CL-bench | 21.1% | — |
Writing & Preference Claude Opus 4.5 leads
Claude Opus 4.5: 68.1 (#28), GLM-4.6: 61.1 (#90)
| Benchmark | Claude Opus 4.5 | GLM-4.6 |
|---|---|---|
| LMArena Text | 1451 | 1440 |
| LMArena Creative Writing | 1445 | 1411 |
| EQ-Bench Creative Writing | 1687 | 1411 |
| LMArena Multi-Turn | 1466 | 1427 |
Frequently asked questions
Is Claude Opus 4.5 better than GLM-4.6?
Claude Opus 4.5 is the stronger model overall, scoring 50.5 to 41.4 on the Noometry Index. GLM-4.6 costs 10× less per token, which makes it the better buy when Claude Opus 4.5's lead doesn't matter for your workload.
Which is cheaper, Claude Opus 4.5 or GLM-4.6?
GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Claude Opus 4.5 lists at $5 and $25.
Is Claude Opus 4.5 or GLM-4.6 better for coding?
Claude Opus 4.5 scores higher on coding benchmarks: 54.8 versus 40.1 in the Noometry coding category.
Which has the bigger context window?
GLM-4.6 does, with 205K tokens against 200K.
How many benchmarks do Claude Opus 4.5 and GLM-4.6 share?
27 benchmarks have published results for both models. Claude Opus 4.5 has 69 scored results on Noometry and GLM-4.6 has 29.