Model comparison
Claude Opus 5.5 vs GLM-5.2
Claude Opus 5.5 is the stronger model overall, scoring 68.6 to 51.1 on the Noometry Index. GLM-5.2 costs 3.7× less per token, which makes it the better buy when Claude Opus 5.5's lead doesn't matter for your workload.
Last verified . 36 shared benchmarks.
Summary
- They share 36 benchmarks with published results for both. Claude Opus 5.5 scores higher in 9 categories and GLM-5.2 in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Claude Opus 5.5 leads 80.2 to 42.3.
- The biggest single-benchmark swing is ARC-AGI-2: 93.3% for Claude Opus 5.5 and 22.8% for GLM-5.2.
- GLM-5.2 is cheaper at $1.40 / $4.40 per million input/output tokens, against $4 / $20 for Claude Opus 5.5.
- GLM-5.2 has downloadable open weights; the other is API-only.
Side by side
| Claude Opus 5.5 | GLM-5.2 | |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 68.6 | 51.1 |
| Released | 2026-09-22 | 2026-06-13 |
| Weights | Proprietary | Open |
| Context window | 1M | 1M |
| Max output | 128K | 131K |
| Input $ / M tokens | $4 | $1.40 |
| Output $ / M tokens | $20 | $4.40 |
| Results tracked | 44 | 51 |
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Category by category
Coding Claude Opus 5.5 leads
Claude Opus 5.5: 71.9 (#3), GLM-5.2: 51.3 (#41)
| Benchmark | Claude Opus 5.5 | GLM-5.2 |
|---|---|---|
| FrontierCode | 54.6% | 24.5% |
| LMArena WebDev | 1813 | 1603 |
| SciCode | 66.9% | 50.5% |
| LMArena Coding | 1547 | 1485 |
| ALE-Bench | 2,147 | 1,047 |
| SWE-bench Verified | — | 78.7% |
| DeepSWE | — | 43.8% |
| CursorBench | 57.8% | — |
| FrontierSWE | 62.3% | — |
| WeirdML | — | 70.1% |
| MirrorCode | 77.4% | — |
Agentic & Tool Use Claude Opus 5.5 leads
Claude Opus 5.5: 45.3 (#15), GLM-5.2: 32.4 (#63)
| Benchmark | Claude Opus 5.5 | GLM-5.2 |
|---|---|---|
| APEX-Agents | 73.5% | 45.2% |
| Vending-Bench 2 | 9,235 | 8,314 |
| τ²-bench Banking | — | 37.1% |
| PostTrainBench | — | 31.7% |
| GBAEval | — | 0% |
| GDP.pdf | 30.6% | — |
Reasoning Claude Opus 5.5 leads
Claude Opus 5.5: 80.2 (#3), GLM-5.2: 42.3 (#52)
| Benchmark | Claude Opus 5.5 | GLM-5.2 |
|---|---|---|
| ARC-AGI-2 | 93.3% | 22.8% |
| NYT Connections (extended) | 88.5% | 74.3% |
| ARC-AGI-1 | 98.5% | 77% |
| CritPt | 31.7% | 20.9% |
| EBR-Bench | 71.4% | 9.5% |
| LMArena Hard Prompts | 1535 | 1480 |
| Mystery Game Puzzles | 71% | 19% |
| DTBench | 98.9% | 93.6% |
| LMCA | 68.2% | 45.8% |
| Epoch Capabilities Index | 167.33 | 151.78 |
| SimpleBench | — | 58.8% |
| Kagi LLM Benchmark | — | 62.6% |
| Chess Puzzles | — | 21% |
| Surface Evolver Bench | — | 55.6% |
Math Claude Opus 5.5 leads
Claude Opus 5.5: 91.8 (#3), GLM-5.2: 55.7 (#43)
| Benchmark | Claude Opus 5.5 | GLM-5.2 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 91.2% | 59.2% |
| FrontierMath Tier 4 | 95% | 29.3% |
| OTIS Mock AIME 2024-2025 | 100% | 86.4% |
| ProofBench | 100% | 35% |
| LMArena Math | 1506 | 1482 |
| MathArena Final-Answer Competitions | — | 67.6% |
| FrontierMath Erdős | 2.9% | — |
Knowledge Claude Opus 5.5 leads
Claude Opus 5.5: 66.4 (#10), GLM-5.2: 57.1 (#40)
| Benchmark | Claude Opus 5.5 | GLM-5.2 |
|---|---|---|
| GPQA Diamond | 90.6% | 91.9% |
| SimpleQA Verified | 72.2% | 34.2% |
| LMArena Expert | 1547 | 1486 |
Multimodal Not comparable
Claude Opus 5.5: 57.8 (#1), GLM-5.2: —
| Benchmark | Claude Opus 5.5 | GLM-5.2 |
|---|---|---|
| LMArena Vision | 1321 | — |
| Blueprint-Bench 2 | 51.2% | — |
| Furniture Assembly | 83.3% | — |
Multilingual Claude Opus 5.5 leads
Claude Opus 5.5: 59.1 (#2), GLM-5.2: 55.8 (#26)
| Benchmark | Claude Opus 5.5 | GLM-5.2 |
|---|---|---|
| LMArena Non-English | 1507 | 1459 |
| LMArena Chinese | 1588 | 1519 |
| LMArena French | 1514 | 1479 |
| LMArena Russian | 1520 | 1466 |
| LMArena Spanish | 1507 | 1477 |
| LMArena German | — | 1468 |
| LMArena Japanese | — | 1451 |
| LMArena Korean | — | 1445 |
Instruction Following Claude Opus 5.5 leads
Claude Opus 5.5: 80.0 (#3), GLM-5.2: 76.9 (#34)
| Benchmark | Claude Opus 5.5 | GLM-5.2 |
|---|---|---|
| LMArena Instruction Following | 1537 | 1465 |
Long Context Claude Opus 5.5 leads
Claude Opus 5.5: 47.1 (#19), GLM-5.2: 45.3 (#43)
| Benchmark | Claude Opus 5.5 | GLM-5.2 |
|---|---|---|
| LMArena Longer Query | 1532 | 1479 |
Writing & Preference Claude Opus 5.5 leads
Claude Opus 5.5: 78.2 (#3), GLM-5.2: 70.4 (#21)
| Benchmark | Claude Opus 5.5 | GLM-5.2 |
|---|---|---|
| LMArena Text | 1515 | 1470 |
| LMArena Creative Writing | 1533 | 1462 |
| EQ-Bench Creative Writing | 2050 | 1757 |
| LMArena Multi-Turn | 1499 | 1469 |
| EQ-Bench 4 | — | 1222 |
Frequently asked questions
Is Claude Opus 5.5 better than GLM-5.2?
Claude Opus 5.5 is the stronger model overall, scoring 68.6 to 51.1 on the Noometry Index. GLM-5.2 costs 3.7× less per token, which makes it the better buy when Claude Opus 5.5's lead doesn't matter for your workload.
Which is cheaper, Claude Opus 5.5 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 5.5 lists at $4 and $20.
Is Claude Opus 5.5 or GLM-5.2 better for coding?
Claude Opus 5.5 scores higher on coding benchmarks: 71.9 versus 51.3 in the Noometry coding category.
Which has the bigger context window?
Both accept 1M tokens.
How many benchmarks do Claude Opus 5.5 and GLM-5.2 share?
36 benchmarks have published results for both models. Claude Opus 5.5 has 44 scored results on Noometry and GLM-5.2 has 51.