Model comparison
Claude Opus 4.8 vs GLM-5.3-Flash
Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 51.8 on the Noometry Index. GLM-5.3-Flash costs 42× less per token, which makes it the better buy when Claude Opus 4.8's lead doesn't matter for your workload.
Last verified . 38 shared benchmarks.
Summary
- They share 38 benchmarks with published results for both. Claude Opus 4.8 scores higher in 8 categories and GLM-5.3-Flash in 2 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in math, where Claude Opus 4.8 leads 78.4 to 53.3.
- The biggest single-benchmark swing is ProofBench: 69% for Claude Opus 4.8 and 21% for GLM-5.3-Flash.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $5 / $25 for Claude Opus 4.8.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.
Side by side
| Claude Opus 4.8 | GLM-5.3-Flash | |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 60.7 | 51.8 |
| Released | 2026-05-28 | 2026-08-20 |
| Weights | Proprietary | Open |
| Context window | 1M | 1M |
| Max output | 128K | 131K |
| Input $ / M tokens | $5 | $0.15 |
| Output $ / M tokens | $25 | $0.50 |
| Results tracked | 65 | 40 |
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Category by category
Coding Claude Opus 4.8 leads
Claude Opus 4.8: 59.9 (#12), GLM-5.3-Flash: 53.1 (#31)
| Benchmark | Claude Opus 4.8 | GLM-5.3-Flash |
|---|---|---|
| DeepSWE | 59% | 63.4% |
| FrontierCode | 46.5% | 31.8% |
| LMArena WebDev | 1556 | 1609 |
| SciCode | 53.5% | 51.6% |
| LMArena Coding | 1490 | 1508 |
| ALE-Bench | 1,564 | 303.55 |
| CursorBench | — | 36.8% |
| FrontierSWE | — | 18.1% |
| GSO | 47.1% | — |
| WeirdML | 82.9% | — |
Agentic & Tool Use Claude Opus 4.8 leads
Claude Opus 4.8: 47.6 (#11), GLM-5.3-Flash: 34.2 (#47)
| Benchmark | Claude Opus 4.8 | GLM-5.3-Flash |
|---|---|---|
| APEX-Agents | 48.9% | 52.8% |
| GDP.pdf | 24% | 14% |
| 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% | — |
| LMArena Search | 1204 | — |
| Vending-Bench 2 | 5,787 | — |
Reasoning Claude Opus 4.8 leads
Claude Opus 4.8: 64.7 (#16), GLM-5.3-Flash: 48.0 (#42)
| Benchmark | Claude Opus 4.8 | GLM-5.3-Flash |
|---|---|---|
| ARC-AGI-2 | 72.1% | 65.8% |
| ARC-AGI-1 | 92.5% | 91% |
| CritPt | 20.9% | 15.4% |
| Chess Puzzles | 34% | 14% |
| LMArena Hard Prompts | 1482 | 1491 |
| Mystery Game Puzzles | 36% | 8% |
| Surface Evolver Bench | 87.5% | 52.5% |
| Bench to the Future 3 | 0.14 | 0.15 |
| Epoch Capabilities Index | 158.21 | 151.88 |
| SimpleBench | 64.8% | — |
| Kagi LLM Benchmark | 88.8% | — |
| NYT Connections (extended) | 91.1% | — |
| EnigmaEval | 23.5% | — |
| EBR-Bench | 28.6% | — |
| DTBench | 94.9% | — |
| LMCA | 57.5% | — |
| ForecastBench | 59.9 | — |
Math Claude Opus 4.8 leads
Claude Opus 4.8: 78.4 (#13), GLM-5.3-Flash: 53.3 (#47)
| Benchmark | Claude Opus 4.8 | GLM-5.3-Flash |
|---|---|---|
| FrontierMath (Tiers 1-3) | 80% | 55.8% |
| FrontierMath Tier 4 | 56.1% | 17.1% |
| OTIS Mock AIME 2024-2025 | 98.3% | 93.9% |
| ProofBench | 69% | 21% |
| LMArena Math | 1487 | 1500 |
| 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 (#29), GLM-5.3-Flash: 58.4 (#36)
| Benchmark | Claude Opus 4.8 | GLM-5.3-Flash |
|---|---|---|
| GPQA Diamond | 91% | 90.2% |
| LMArena Expert | 1502 | 1513 |
| SimpleQA Verified | 53% | — |
Multimodal Too close to call
Claude Opus 4.8: 42.9 (#26), GLM-5.3-Flash: 42.8 (#27)
| Benchmark | Claude Opus 4.8 | GLM-5.3-Flash |
|---|---|---|
| LMArena Vision | 1294 | 1296 |
| Blueprint-Bench 2 | 14.5% | — |
| Furniture Assembly | 42.5% | — |
| LMArena Document | 1475 | — |
Multilingual Too close to call
Claude Opus 4.8: 55.2 (#33), GLM-5.3-Flash: 56.0 (#25)
| Benchmark | Claude Opus 4.8 | GLM-5.3-Flash |
|---|---|---|
| LMArena Non-English | 1450 | 1462 |
| LMArena Chinese | 1507 | 1527 |
| LMArena French | 1481 | 1496 |
| LMArena German | 1472 | 1470 |
| LMArena Japanese | 1440 | 1429 |
| LMArena Korean | 1432 | 1446 |
| LMArena Russian | 1474 | 1469 |
| LMArena Spanish | 1466 | 1471 |
Instruction Following Too close to call
Claude Opus 4.8: 77.4 (#24), GLM-5.3-Flash: 77.5 (#20)
| Benchmark | Claude Opus 4.8 | GLM-5.3-Flash |
|---|---|---|
| LMArena Instruction Following | 1476 | 1478 |
Long Context Too close to call
Claude Opus 4.8: 45.4 (#35), GLM-5.3-Flash: 45.4 (#39)
| Benchmark | Claude Opus 4.8 | GLM-5.3-Flash |
|---|---|---|
| LMArena Longer Query | 1483 | 1482 |
Writing & Preference Claude Opus 4.8 leads
Claude Opus 4.8: 72.0 (#16), GLM-5.3-Flash: 65.3 (#50)
| Benchmark | Claude Opus 4.8 | GLM-5.3-Flash |
|---|---|---|
| LMArena Text | 1461 | 1471 |
| LMArena Creative Writing | 1454 | 1442 |
| LMArena Multi-Turn | 1476 | 1467 |
| EQ-Bench Creative Writing | 1840 | — |
| EQ-Bench 4 | 1281 | — |
Frequently asked questions
Is Claude Opus 4.8 better than GLM-5.3-Flash?
Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 51.8 on the Noometry Index. GLM-5.3-Flash costs 42× 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-Flash?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Claude Opus 4.8 lists at $5 and $25.
Is Claude Opus 4.8 or GLM-5.3-Flash better for coding?
Claude Opus 4.8 scores higher on coding benchmarks: 59.9 versus 53.1 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-Flash share?
38 benchmarks have published results for both models. Claude Opus 4.8 has 65 scored results on Noometry and GLM-5.3-Flash has 40.