Model comparison
Claude Opus 4.8 vs GLM-5.3
Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 54.6 on the Noometry Index. GLM-5.3 costs 4.7× less per token, which makes it the better buy when Claude Opus 4.8's lead doesn't matter for your workload.
Last verified . 40 shared benchmarks.
Summary
- They share 40 benchmarks with published results for both. Claude Opus 4.8 scores higher in 5 categories and GLM-5.3 in 4 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Claude Opus 4.8 leads 64.6 to 45.2.
- The biggest single-benchmark swing is FrontierMath Tier 4: 56.1% for Claude Opus 4.8 and 29.3% for GLM-5.3.
- GLM-5.3 is cheaper at $1.40 / $4.40 per million input/output tokens, against $5 / $25 for Claude Opus 4.8.
- GLM-5.3 has downloadable open weights; the other is API-only.
Side by side
| Claude Opus 4.8 | GLM-5.3 | |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 60.7 | 54.6 |
| Released | 2026-05-28 | 2026-08-14 |
| 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 | 65 | 41 |
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Category by category
Coding Claude Opus 4.8 leads
Claude Opus 4.8: 60.2 (#9), GLM-5.3: 59.0 (#12)
| Benchmark | Claude Opus 4.8 | GLM-5.3 |
|---|---|---|
| DeepSWE | 59% | 69% |
| FrontierCode | 46.5% | 40.1% |
| LMArena WebDev | 1556 | 1623 |
| SciCode | 53.5% | 59% |
| WeirdML | 82.9% | 75.4% |
| LMArena Coding | 1491 | 1499 |
| ALE-Bench | 1,564 | 1,317 |
| FrontierSWE | — | 30.2% |
| GSO | 47.1% | — |
Agentic & Tool Use Claude Opus 4.8 leads
Claude Opus 4.8: 47.6 (#10), GLM-5.3: 36.3 (#34)
| Benchmark | Claude Opus 4.8 | GLM-5.3 |
|---|---|---|
| APEX-Agents | 48.9% | 56.6% |
| Vending-Bench 2 | 5,787 | 8,164 |
| OSWorld 2.0 | 20.6% | — |
| Remote Labor Index | 8.3% | — |
| τ²-bench Banking | 39.7% | — |
| DeepResearch Bench | 50.2% | — |
| PostTrainBench | 33.8% | — |
| GBAEval | 70.9% | — |
| GDP.pdf | 24% | — |
| LMArena Search | 1204 | — |
Reasoning Claude Opus 4.8 leads
Claude Opus 4.8: 64.6 (#14), GLM-5.3: 45.2 (#42)
| Benchmark | Claude Opus 4.8 | GLM-5.3 |
|---|---|---|
| NYT Connections (extended) | 91.1% | 74.2% |
| CritPt | 20.9% | 19.1% |
| Chess Puzzles | 34% | 21% |
| LMArena Hard Prompts | 1481 | 1491 |
| Mystery Game Puzzles | 36% | 33% |
| DTBench | 94.9% | 87.7% |
| LMCA | 57.5% | 55.5% |
| Bench to the Future 3 | 0.14 | 0.15 |
| Epoch Capabilities Index | 158.3 | 155.56 |
| ARC-AGI-2 | 72.1% | — |
| SimpleBench | 64.8% | — |
| Kagi LLM Benchmark | 88.8% | — |
| ARC-AGI-1 | 92.5% | — |
| EnigmaEval | 23.5% | — |
| EBR-Bench | 28.6% | — |
| Surface Evolver Bench | 87.5% | — |
| ForecastBench | 59.9 | — |
Math Claude Opus 4.8 leads
Claude Opus 4.8: 78.2 (#11), GLM-5.3: 62.1 (#27)
| Benchmark | Claude Opus 4.8 | GLM-5.3 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 80% | 68.8% |
| FrontierMath Tier 4 | 56.1% | 29.3% |
| OTIS Mock AIME 2024-2025 | 98.3% | 91.1% |
| ProofBench | 69% | 49% |
| LMArena Math | 1487 | 1493 |
| MathArena Final-Answer Competitions | 91.8% | — |
| FrontierMath (Feb 2025 set) | 47.2% | — |
| FrontierMath Tier 4 (v1) | 31.3% | — |
Knowledge Claude Opus 4.8 leads
Claude Opus 4.8: 61.3 (#26), GLM-5.3: 58.3 (#34)
| Benchmark | Claude Opus 4.8 | GLM-5.3 |
|---|---|---|
| GPQA Diamond | 91% | 90.9% |
| SimpleQA Verified | 53% | 41% |
| LMArena Expert | 1501 | 1512 |
Multimodal Not comparable
Claude Opus 4.8: 43.2 (#20), GLM-5.3: —
| Benchmark | Claude Opus 4.8 | GLM-5.3 |
|---|---|---|
| LMArena Vision | 1295 | — |
| Blueprint-Bench 2 | 14.5% | — |
| Furniture Assembly | 42.5% | — |
| LMArena Document | 1475 | — |
Multilingual Too close to call
Claude Opus 4.8: 55.4 (#31), GLM-5.3: 55.9 (#27)
| Benchmark | Claude Opus 4.8 | GLM-5.3 |
|---|---|---|
| LMArena Non-English | 1450 | 1457 |
| LMArena Chinese | 1509 | 1523 |
| LMArena French | 1482 | 1499 |
| LMArena German | 1470 | 1505 |
| LMArena Japanese | 1440 | 1438 |
| LMArena Korean | 1432 | 1478 |
| LMArena Russian | 1476 | 1460 |
| LMArena Spanish | 1470 | 1444 |
Instruction Following Too close to call
Claude Opus 4.8: 77.6 (#22), GLM-5.3: 77.8 (#15)
| Benchmark | Claude Opus 4.8 | GLM-5.3 |
|---|---|---|
| LMArena Instruction Following | 1476 | 1481 |
Long Context Too close to call
Claude Opus 4.8: 45.4 (#37), GLM-5.3: 45.5 (#32)
| Benchmark | Claude Opus 4.8 | GLM-5.3 |
|---|---|---|
| LMArena Longer Query | 1482 | 1485 |
Writing & Preference GLM-5.3 leads
Claude Opus 4.8: 72.1 (#17), GLM-5.3: 75.9 (#6)
| Benchmark | Claude Opus 4.8 | GLM-5.3 |
|---|---|---|
| LMArena Text | 1460 | 1473 |
| LMArena Creative Writing | 1453 | 1459 |
| EQ-Bench Creative Writing | 1840 | 2075 |
| LMArena Multi-Turn | 1476 | 1473 |
| EQ-Bench 4 | 1281 | — |
Frequently asked questions
Is Claude Opus 4.8 better than GLM-5.3?
Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 54.6 on the Noometry Index. GLM-5.3 costs 4.7× less per token, which makes it the better buy when Claude Opus 4.8's lead doesn't matter for your workload.
Which is cheaper, Claude Opus 4.8 or GLM-5.3?
GLM-5.3 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; Claude Opus 4.8 lists at $5 and $25.
Is Claude Opus 4.8 or GLM-5.3 better for coding?
Claude Opus 4.8 scores higher on coding benchmarks: 60.2 versus 59.0 in the Noometry coding category.
Which has the bigger context window?
Both accept 1M tokens.
How many benchmarks do Claude Opus 4.8 and GLM-5.3 share?
40 benchmarks have published results for both models. Claude Opus 4.8 has 65 scored results on Noometry and GLM-5.3 has 41.