Model comparison
Claude Opus 4.5 vs GLM-4.6V
Claude Opus 4.5 is the stronger model overall, scoring 50.5 to 41.3 on the Noometry Index. GLM-4.6V costs 22× less per token, which makes it the better buy when Claude Opus 4.5's lead doesn't matter for your workload.
Last verified . 11 shared benchmarks.
Summary
- They share 11 benchmarks with published results for both. Claude Opus 4.5 scores higher in 7 categories and GLM-4.6V in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Claude Opus 4.5 leads 56.5 to 38.0.
- GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $5 / $25 for Claude Opus 4.5.
- Claude Opus 4.5 accepts more context: 200K tokens versus 128K.
- GLM-4.6V has downloadable open weights; the other is API-only.
Side by side
| Claude Opus 4.5 | GLM-4.6V | |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 50.5 | 41.3 |
| Released | 2025-11-01 | 2025-12-08 |
| Weights | Proprietary | Open |
| Context window | 200K | 128K |
| Max output | 64K | 33K |
| Input $ / M tokens | $5 | $0.30 |
| Output $ / M tokens | $25 | $0.90 |
| Results tracked | 69 | 12 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Claude Opus 4.5 leads
Claude Opus 4.5: 54.8 (#27), GLM-4.6V: 40.9 (#128)
| Benchmark | Claude Opus 4.5 | GLM-4.6V |
|---|---|---|
| LMArena Coding | 1504 | 1390 |
| SWE-bench Verified | 76.7% | — |
| SWE-bench Verified (bash only) | 76.8% | — |
| LMArena WebDev | 1494 | — |
| SWE-bench Multilingual | 70.7% | — |
| GSO | 26.5% | — |
| WeirdML | 63.7% | — |
| ALE-Bench | 1,025 | — |
| AlgoTune | 1.77 | — |
Agentic & Tool Use Not comparable
Claude Opus 4.5: 47.3 (#12), GLM-4.6V: —
| Benchmark | Claude Opus 4.5 | GLM-4.6V |
|---|---|---|
| Terminal-Bench | 63.1% | — |
| 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% | — |
| Vending-Bench 2 | 4,967 | — |
Reasoning Claude Opus 4.5 leads
Claude Opus 4.5: 42.6 (#51), GLM-4.6V: 27.6 (#115)
| Benchmark | Claude Opus 4.5 | GLM-4.6V |
|---|---|---|
| LMArena Hard Prompts | 1476 | 1368 |
| ARC-AGI-2 | 37.6% | — |
| SimpleBench | 62% | — |
| Kagi LLM Benchmark | 80.2% | — |
| NYT Connections (extended) | 52.5% | — |
| ARC-AGI-1 | 80% | — |
| 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 Not comparable
Claude Opus 4.5: 38.6 (#132), GLM-4.6V: —
| Benchmark | Claude Opus 4.5 | GLM-4.6V |
|---|---|---|
| FrontierMath (Tiers 1-3) | 34.4% | — |
| FrontierMath Tier 4 | 4.9% | — |
| OTIS Mock AIME 2024-2025 | 86.1% | — |
| ProofBench | 36% | — |
| LMArena Math | 1463 | — |
| FrontierMath (Feb 2025 set) | 20.7% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge Claude Opus 4.5 leads
Claude Opus 4.5: 56.5 (#44), GLM-4.6V: 38.0 (#149)
| Benchmark | Claude Opus 4.5 | GLM-4.6V |
|---|---|---|
| LMArena Expert | 1487 | 1371 |
| GPQA Diamond | 86% | — |
| Humanity's Last Exam | 25.2% | — |
| SimpleQA Verified | 45.7% | — |
| Vectara Hallucination Rate | 10.9% | — |
Multimodal GLM-4.6V leads
Claude Opus 4.5: 31.4 (#107), GLM-4.6V: 34.8 (#90)
| Benchmark | Claude Opus 4.5 | GLM-4.6V |
|---|---|---|
| LMArena Vision | — | 1164 |
| GeoBench | 75% | — |
| VPCT | 40% | — |
| Furniture Assembly | 28.3% | — |
| LMArena Document | 1462 | — |
Multilingual Claude Opus 4.5 leads
Claude Opus 4.5: 54.3 (#47), GLM-4.6V: 48.6 (#141)
| Benchmark | Claude Opus 4.5 | GLM-4.6V |
|---|---|---|
| LMArena Non-English | 1438 | 1359 |
| LMArena Chinese | 1470 | 1425 |
| LMArena Russian | 1447 | 1340 |
| LMArena French | 1471 | — |
| LMArena German | 1449 | — |
| LMArena Japanese | 1416 | — |
| LMArena Korean | 1424 | — |
| LMArena Spanish | 1458 | — |
Instruction Following Claude Opus 4.5 leads
Claude Opus 4.5: 77.5 (#19), GLM-4.6V: 71.4 (#151)
| Benchmark | Claude Opus 4.5 | GLM-4.6V |
|---|---|---|
| LMArena Instruction Following | 1478 | 1352 |
Long Context Claude Opus 4.5 leads
Claude Opus 4.5: 46.5 (#22), GLM-4.6V: 41.3 (#143)
| Benchmark | Claude Opus 4.5 | GLM-4.6V |
|---|---|---|
| LMArena Longer Query | 1480 | 1358 |
| CL-bench | 21.1% | — |
Writing & Preference Claude Opus 4.5 leads
Claude Opus 4.5: 68.1 (#28), GLM-4.6V: 56.6 (#137)
| Benchmark | Claude Opus 4.5 | GLM-4.6V |
|---|---|---|
| LMArena Text | 1451 | 1377 |
| LMArena Creative Writing | 1445 | 1347 |
| LMArena Multi-Turn | 1466 | 1360 |
| EQ-Bench Creative Writing | 1687 | — |
Frequently asked questions
Is Claude Opus 4.5 better than GLM-4.6V?
Claude Opus 4.5 is the stronger model overall, scoring 50.5 to 41.3 on the Noometry Index. GLM-4.6V costs 22× 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-4.6V?
GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Claude Opus 4.5 lists at $5 and $25.
Is Claude Opus 4.5 or GLM-4.6V better for coding?
Claude Opus 4.5 scores higher on coding benchmarks: 54.8 versus 40.9 in the Noometry coding category.
Which has the bigger context window?
Claude Opus 4.5 does, with 200K tokens against 128K.
How many benchmarks do Claude Opus 4.5 and GLM-4.6V share?
11 benchmarks have published results for both models. Claude Opus 4.5 has 69 scored results on Noometry and GLM-4.6V has 12.