Model comparison
Claude Opus 4.6 vs GLM-5.2
Claude Opus 4.6 is the stronger model overall, scoring 58.2 to 51.1 on the Noometry Index. GLM-5.2 costs 4.7× less per token, which makes it the better buy when Claude Opus 4.6's lead doesn't matter for your workload.
Last verified . 46 shared benchmarks.
Summary
- They share 46 benchmarks with published results for both. Claude Opus 4.6 scores higher in 9 categories and GLM-5.2 in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where Claude Opus 4.6 leads 51.1 to 32.4.
- The biggest single-benchmark swing is ARC-AGI-2: 69.2% for Claude Opus 4.6 and 22.8% for GLM-5.2.
- GLM-5.2 is cheaper at $1.40 / $4.40 per million input/output tokens, against $5 / $25 for Claude Opus 4.6.
- GLM-5.2 has downloadable open weights; the other is API-only.
Side by side
| Claude Opus 4.6 | GLM-5.2 | |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 58.2 | 51.1 |
| Released | 2026-02-04 | 2026-06-13 |
| Weights | Proprietary | Open |
| Context window | 1M | 1M |
| Max output | 128K | 131K |
| Input $ / M tokens | $5 | $1.40 |
| Output $ / M tokens | $25 | $4.40 |
| Results tracked | 68 | 51 |
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Category by category
Coding Claude Opus 4.6 leads
Claude Opus 4.6: 57.2 (#20), GLM-5.2: 51.3 (#41)
| Benchmark | Claude Opus 4.6 | GLM-5.2 |
|---|---|---|
| SWE-bench Verified | 78.7% | 78.7% |
| FrontierCode | 26.6% | 24.5% |
| LMArena WebDev | 1547 | 1603 |
| WeirdML | 78% | 70.1% |
| LMArena Coding | 1536 | 1485 |
| ALE-Bench | 996.5 | 1,047 |
| DeepSWE | — | 43.8% |
| SWE-bench Verified (bash only) | 75.6% | — |
| SWE-bench Multilingual | 72% | — |
| SciCode | — | 50.5% |
| GSO | 41.2% | — |
| AlgoTune | 1.47 | — |
Agentic & Tool Use Claude Opus 4.6 leads
Claude Opus 4.6: 51.1 (#4), GLM-5.2: 32.4 (#63)
| Benchmark | Claude Opus 4.6 | GLM-5.2 |
|---|---|---|
| APEX-Agents | 46.3% | 45.2% |
| τ²-bench Banking | 27.3% | 37.1% |
| GBAEval | 44.1% | 0% |
| Vending-Bench 2 | 8,018 | 8,314 |
| Terminal-Bench | 79.8% | — |
| Remote Labor Index | 4.2% | — |
| Cybench | 93% | — |
| DeepResearch Bench | 55.3% | — |
| PostTrainBench | — | 31.7% |
| LMArena Search | 1253 | — |
| METR Time Horizons | 78.9% | — |
Reasoning Claude Opus 4.6 leads
Claude Opus 4.6: 57.8 (#23), GLM-5.2: 42.3 (#52)
| Benchmark | Claude Opus 4.6 | GLM-5.2 |
|---|---|---|
| ARC-AGI-2 | 69.2% | 22.8% |
| SimpleBench | 67.6% | 58.8% |
| Kagi LLM Benchmark | 83.6% | 62.6% |
| NYT Connections (extended) | 92.1% | 74.3% |
| ARC-AGI-1 | 94% | 77% |
| Chess Puzzles | 17% | 21% |
| EBR-Bench | 12.7% | 9.5% |
| LMArena Hard Prompts | 1527 | 1480 |
| Mystery Game Puzzles | 25% | 19% |
| DTBench | 91.2% | 93.6% |
| LMCA | 55.8% | 45.8% |
| Epoch Capabilities Index | 155.24 | 151.78 |
| CritPt | — | 20.9% |
| EnigmaEval | 7.6% | — |
| Thematic Generalization | 80.6% | — |
| Surface Evolver Bench | — | 55.6% |
| ForecastBench | 60 | — |
Math Claude Opus 4.6 leads
Claude Opus 4.6: 63.0 (#31), GLM-5.2: 55.7 (#43)
| Benchmark | Claude Opus 4.6 | GLM-5.2 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 66% | 59.2% |
| FrontierMath Tier 4 | 26.8% | 29.3% |
| MathArena Final-Answer Competitions | 78.5% | 67.6% |
| OTIS Mock AIME 2024-2025 | 94.4% | 86.4% |
| ProofBench | 50% | 35% |
| LMArena Math | 1519 | 1482 |
| FrontierMath (Feb 2025 set) | 40.7% | — |
| FrontierMath Tier 4 (v1) | 22.9% | — |
Knowledge Claude Opus 4.6 leads
Claude Opus 4.6: 61.9 (#26), GLM-5.2: 57.1 (#40)
| Benchmark | Claude Opus 4.6 | GLM-5.2 |
|---|---|---|
| GPQA Diamond | 90.5% | 91.9% |
| SimpleQA Verified | 47% | 34.2% |
| LMArena Expert | 1546 | 1486 |
| Humanity's Last Exam | 34.4% | — |
| Vectara Hallucination Rate | 12.2% | — |
Multimodal Not comparable
Claude Opus 4.6: 37.3 (#74), GLM-5.2: —
| Benchmark | Claude Opus 4.6 | GLM-5.2 |
|---|---|---|
| LMArena Vision | 1316 | — |
| Furniture Assembly | 28.3% | — |
| LMArena Document | 1507 | — |
Multilingual Claude Opus 4.6 leads
Claude Opus 4.6: 57.9 (#6), GLM-5.2: 55.8 (#26)
| Benchmark | Claude Opus 4.6 | GLM-5.2 |
|---|---|---|
| LMArena Non-English | 1489 | 1459 |
| LMArena Chinese | 1551 | 1519 |
| LMArena French | 1513 | 1479 |
| LMArena German | 1502 | 1468 |
| LMArena Japanese | 1484 | 1451 |
| LMArena Korean | 1464 | 1445 |
| LMArena Russian | 1497 | 1466 |
| LMArena Spanish | 1510 | 1477 |
Instruction Following Claude Opus 4.6 leads
Claude Opus 4.6: 79.5 (#4), GLM-5.2: 76.9 (#34)
| Benchmark | Claude Opus 4.6 | GLM-5.2 |
|---|---|---|
| LMArena Instruction Following | 1523 | 1465 |
Long Context Claude Opus 4.6 leads
Claude Opus 4.6: 48.1 (#13), GLM-5.2: 45.3 (#43)
| Benchmark | Claude Opus 4.6 | GLM-5.2 |
|---|---|---|
| LMArena Longer Query | 1520 | 1479 |
| CL-bench | 20.7% | — |
| CL-bench Life | 17% | — |
Writing & Preference Claude Opus 4.6 leads
Claude Opus 4.6: 73.5 (#10), GLM-5.2: 70.4 (#21)
| Benchmark | Claude Opus 4.6 | GLM-5.2 |
|---|---|---|
| LMArena Text | 1503 | 1470 |
| LMArena Creative Writing | 1505 | 1462 |
| EQ-Bench Creative Writing | 1809 | 1757 |
| EQ-Bench 4 | 1223 | 1222 |
| LMArena Multi-Turn | 1513 | 1469 |
Frequently asked questions
Is Claude Opus 4.6 better than GLM-5.2?
Claude Opus 4.6 is the stronger model overall, scoring 58.2 to 51.1 on the Noometry Index. GLM-5.2 costs 4.7× less per token, which makes it the better buy when Claude Opus 4.6's lead doesn't matter for your workload.
Which is cheaper, Claude Opus 4.6 or GLM-5.2?
GLM-5.2 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; Claude Opus 4.6 lists at $5 and $25.
Is Claude Opus 4.6 or GLM-5.2 better for coding?
Claude Opus 4.6 scores higher on coding benchmarks: 57.2 versus 51.3 in the Noometry coding category.
Which has the bigger context window?
Both accept 1M tokens.
How many benchmarks do Claude Opus 4.6 and GLM-5.2 share?
46 benchmarks have published results for both models. Claude Opus 4.6 has 68 scored results on Noometry and GLM-5.2 has 51.