Model comparison
Claude Opus 5 vs GLM-4.6
Claude Opus 5 is the stronger model overall, scoring 67.8 to 41.4 on the Noometry Index. GLM-4.6 costs 10× less per token, which makes it the better buy when Claude Opus 5's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
Summary
- They share 22 benchmarks with published results for both. Claude Opus 5 scores higher in 9 categories and GLM-4.6 in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Claude Opus 5 leads 77.2 to 23.7.
- The biggest single-benchmark swing is CritPt: 29.1% for Claude Opus 5 and 1.1% 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 5.
- Claude Opus 5 accepts more context: 1M tokens versus 205K.
- GLM-4.6 has downloadable open weights; the other is API-only.
Side by side
| Claude Opus 5 | GLM-4.6 | |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 67.8 | 41.4 |
| Released | 2026-07-24 | 2025-09-30 |
| Weights | Proprietary | Open |
| Context window | 1M | 205K |
| Max output | 128K | 131K |
| Input $ / M tokens | $5 | $0.60 |
| Output $ / M tokens | $25 | $2.20 |
| Results tracked | 57 | 29 |
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Category by category
Coding Claude Opus 5 leads
Claude Opus 5: 67.5 (#5), GLM-4.6: 40.1 (#148)
| Benchmark | Claude Opus 5 | GLM-4.6 |
|---|---|---|
| LMArena WebDev | 1691 | 1340 |
| SciCode | 56.4% | 38.4% |
| LMArena Coding | 1534 | 1449 |
| ALE-Bench | 2,165 | 340.82 |
| DeepSWE | 73.6% | — |
| FrontierCode | 53.4% | — |
| SWE-bench Verified (bash only) | — | 55.4% |
| CursorBench | 46.6% | — |
| FrontierSWE | 52% | — |
| WeirdML | 91.8% | — |
Agentic & Tool Use Claude Opus 5 leads
Claude Opus 5: 55.6 (#1), GLM-4.6: 32.3 (#66)
| Benchmark | Claude Opus 5 | GLM-4.6 |
|---|---|---|
| Terminal-Bench | — | 24.5% |
| APEX-Agents | 65.8% | — |
| Berkeley Function Calling Leaderboard | — | 72.4% |
| OSWorld 2.0 | 31.4% | — |
| τ²-bench Banking | 48.7% | — |
| PostTrainBench | 35% | — |
| BALROG | 63.4% | — |
| GBAEval | 79.6% | — |
| GDP.pdf | 24% | — |
| Vending-Bench 2 | 11,182 | — |
Reasoning Claude Opus 5 leads
Claude Opus 5: 77.2 (#4), GLM-4.6: 23.7 (#172)
| Benchmark | Claude Opus 5 | GLM-4.6 |
|---|---|---|
| CritPt | 29.1% | 1.1% |
| LMArena Hard Prompts | 1526 | 1440 |
| ARC-AGI-2 | 90.4% | — |
| SimpleBench | 80.6% | — |
| Kagi LLM Benchmark | — | 47.4% |
| NYT Connections (extended) | 94.3% | — |
| ARC-AGI-1 | 97.5% | — |
| Chess Puzzles | 42% | — |
| EBR-Bench | 45.7% | — |
| Mystery Game Puzzles | 59% | — |
| DTBench | 97.9% | — |
| LMCA | 64.5% | — |
| Bench to the Future 3 | 0.12 | — |
| Epoch Capabilities Index | 162.78 | — |
Math Claude Opus 5 leads
Claude Opus 5: 86.2 (#8), GLM-4.6: 39.1 (#111)
| Benchmark | Claude Opus 5 | GLM-4.6 |
|---|---|---|
| LMArena Math | 1531 | 1432 |
| FrontierMath (Tiers 1-3) | 85.6% | — |
| FrontierMath Tier 4 | 73.2% | — |
| OTIS Mock AIME 2024-2025 | 98.9% | — |
| ProofBench | 99% | — |
| FrontierMath (Feb 2025 set) | — | 3.8% |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge Claude Opus 5 leads
Claude Opus 5: 66.8 (#9), GLM-4.6: 40.2 (#124)
| Benchmark | Claude Opus 5 | GLM-4.6 |
|---|---|---|
| LMArena Expert | 1557 | 1431 |
| GPQA Diamond | 93.9% | — |
| SimpleQA Verified | 59.9% | — |
| Vectara Hallucination Rate | — | 9.5% |
Multimodal Not comparable
Claude Opus 5: 50.8 (#8), GLM-4.6: —
| Benchmark | Claude Opus 5 | GLM-4.6 |
|---|---|---|
| LMArena Vision | 1319 | — |
| Blueprint-Bench 2 | 30.4% | — |
| Furniture Assembly | 60.8% | — |
| LMArena Document | 1516 | — |
Multilingual Claude Opus 5 leads
Claude Opus 5: 58.8 (#4), GLM-4.6: 53.5 (#66)
| Benchmark | Claude Opus 5 | GLM-4.6 |
|---|---|---|
| LMArena Non-English | 1501 | 1426 |
| LMArena Chinese | 1574 | 1499 |
| LMArena French | 1519 | 1459 |
| LMArena German | 1524 | 1447 |
| LMArena Japanese | 1516 | 1393 |
| LMArena Korean | 1521 | 1400 |
| LMArena Russian | 1507 | 1419 |
| LMArena Spanish | 1519 | 1436 |
Instruction Following Claude Opus 5 leads
Claude Opus 5: 79.2 (#7), GLM-4.6: 74.3 (#98)
| Benchmark | Claude Opus 5 | GLM-4.6 |
|---|---|---|
| LMArena Instruction Following | 1517 | 1410 |
Long Context Claude Opus 5 leads
Claude Opus 5: 46.5 (#21), GLM-4.6: 43.4 (#94)
| Benchmark | Claude Opus 5 | GLM-4.6 |
|---|---|---|
| LMArena Longer Query | 1515 | 1422 |
Writing & Preference Claude Opus 5 leads
Claude Opus 5: 79.2 (#1), GLM-4.6: 61.1 (#90)
| Benchmark | Claude Opus 5 | GLM-4.6 |
|---|---|---|
| LMArena Text | 1507 | 1440 |
| LMArena Creative Writing | 1491 | 1411 |
| EQ-Bench Creative Writing | 2133 | 1411 |
| LMArena Multi-Turn | 1499 | 1427 |
| EQ-Bench 4 | 1385 | — |
Frequently asked questions
Is Claude Opus 5 better than GLM-4.6?
Claude Opus 5 is the stronger model overall, scoring 67.8 to 41.4 on the Noometry Index. GLM-4.6 costs 10× less per token, which makes it the better buy when Claude Opus 5's lead doesn't matter for your workload.
Which is cheaper, Claude Opus 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 5 lists at $5 and $25.
Is Claude Opus 5 or GLM-4.6 better for coding?
Claude Opus 5 scores higher on coding benchmarks: 67.5 versus 40.1 in the Noometry coding category.
Which has the bigger context window?
Claude Opus 5 does, with 1M tokens against 205K.
How many benchmarks do Claude Opus 5 and GLM-4.6 share?
22 benchmarks have published results for both models. Claude Opus 5 has 57 scored results on Noometry and GLM-4.6 has 29.