Model comparison
Claude Sonnet 5 vs GLM-5.3-Flash
Claude Sonnet 5 is the stronger model overall, scoring 54.6 to 51.8 on the Noometry Index. GLM-5.3-Flash costs 17× less per token, which makes it the better buy when Claude Sonnet 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 Sonnet 5 scores higher in 5 categories and GLM-5.3-Flash in 5 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Claude Sonnet 5 leads 66.2 to 53.3.
- The biggest single-benchmark swing is ProofBench: 77% for Claude Sonnet 5 and 21% for GLM-5.3-Flash.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $2 / $10 for Claude Sonnet 5.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.
Side by side
| Claude Sonnet 5 | GLM-5.3-Flash | |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 54.6 | 51.8 |
| Released | 2026-06-29 | 2026-08-20 |
| Weights | Proprietary | Open |
| Context window | 1M | 1M |
| Max output | 128K | 131K |
| Input $ / M tokens | $2 | $0.15 |
| Output $ / M tokens | $10 | $0.50 |
| Results tracked | 51 | 40 |
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Category by category
Coding Claude Sonnet 5 leads
Claude Sonnet 5: 55.5 (#26), GLM-5.3-Flash: 53.1 (#31)
| Benchmark | Claude Sonnet 5 | GLM-5.3-Flash |
|---|---|---|
| DeepSWE | 53.8% | 63.4% |
| FrontierCode | 42.7% | 31.8% |
| CursorBench | 34.1% | 36.8% |
| LMArena WebDev | 1541 | 1609 |
| SciCode | 54.3% | 51.6% |
| LMArena Coding | 1483 | 1508 |
| ALE-Bench | 1,463 | 303.55 |
| FrontierSWE | — | 18.1% |
| GSO | 37.3% | — |
| WeirdML | 68.8% | — |
Agentic & Tool Use Claude Sonnet 5 leads
Claude Sonnet 5: 42.8 (#18), GLM-5.3-Flash: 34.2 (#47)
| Benchmark | Claude Sonnet 5 | GLM-5.3-Flash |
|---|---|---|
| APEX-Agents | 54.5% | 52.8% |
| GBAEval | 65.3% | — |
| GDP.pdf | — | 14% |
| LMArena Search | 1194 | — |
| Vending-Bench 2 | 6,378 | — |
Reasoning Claude Sonnet 5 leads
Claude Sonnet 5: 49.1 (#39), GLM-5.3-Flash: 48.0 (#42)
| Benchmark | Claude Sonnet 5 | GLM-5.3-Flash |
|---|---|---|
| CritPt | 16.9% | 15.4% |
| Chess Puzzles | 35% | 14% |
| LMArena Hard Prompts | 1461 | 1491 |
| Mystery Game Puzzles | 35% | 8% |
| Surface Evolver Bench | 60% | 52.5% |
| Bench to the Future 3 | 0.14 | 0.15 |
| Epoch Capabilities Index | 156.21 | 151.88 |
| ARC-AGI-2 | — | 65.8% |
| SimpleBench | 60.6% | — |
| NYT Connections (extended) | 75.1% | — |
| ARC-AGI-1 | — | 91% |
| DTBench | 92.5% | — |
| LMCA | 50% | — |
| ForecastBench | 61.1 | — |
Math Claude Sonnet 5 leads
Claude Sonnet 5: 66.2 (#27), GLM-5.3-Flash: 53.3 (#47)
| Benchmark | Claude Sonnet 5 | GLM-5.3-Flash |
|---|---|---|
| FrontierMath (Tiers 1-3) | 65.6% | 55.8% |
| FrontierMath Tier 4 | 29.3% | 17.1% |
| OTIS Mock AIME 2024-2025 | 94.7% | 93.9% |
| ProofBench | 77% | 21% |
| LMArena Math | 1467 | 1500 |
Knowledge GLM-5.3-Flash leads
Claude Sonnet 5: 55.6 (#47), GLM-5.3-Flash: 58.4 (#36)
| Benchmark | Claude Sonnet 5 | GLM-5.3-Flash |
|---|---|---|
| GPQA Diamond | 90.5% | 90.2% |
| LMArena Expert | 1490 | 1513 |
| SimpleQA Verified | 33.7% | — |
Multimodal Too close to call
Claude Sonnet 5: 42.4 (#31), GLM-5.3-Flash: 42.8 (#27)
| Benchmark | Claude Sonnet 5 | GLM-5.3-Flash |
|---|---|---|
| LMArena Vision | 1274 | 1296 |
| Blueprint-Bench 2 | 24.9% | — |
| LMArena Document | 1466 | — |
Multilingual GLM-5.3-Flash leads
Claude Sonnet 5: 53.8 (#55), GLM-5.3-Flash: 56.0 (#25)
| Benchmark | Claude Sonnet 5 | GLM-5.3-Flash |
|---|---|---|
| LMArena Non-English | 1431 | 1462 |
| LMArena Chinese | 1477 | 1527 |
| LMArena French | 1460 | 1496 |
| LMArena German | 1440 | 1470 |
| LMArena Japanese | 1422 | 1429 |
| LMArena Korean | 1411 | 1446 |
| LMArena Russian | 1451 | 1469 |
| LMArena Spanish | 1437 | 1471 |
Instruction Following GLM-5.3-Flash leads
Claude Sonnet 5: 76.3 (#41), GLM-5.3-Flash: 77.5 (#20)
| Benchmark | Claude Sonnet 5 | GLM-5.3-Flash |
|---|---|---|
| LMArena Instruction Following | 1452 | 1478 |
Long Context Too close to call
Claude Sonnet 5: 44.8 (#55), GLM-5.3-Flash: 45.4 (#39)
| Benchmark | Claude Sonnet 5 | GLM-5.3-Flash |
|---|---|---|
| LMArena Longer Query | 1463 | 1482 |
Writing & Preference Claude Sonnet 5 leads
Claude Sonnet 5: 69.2 (#25), GLM-5.3-Flash: 65.3 (#50)
| Benchmark | Claude Sonnet 5 | GLM-5.3-Flash |
|---|---|---|
| LMArena Text | 1442 | 1471 |
| LMArena Creative Writing | 1416 | 1442 |
| LMArena Multi-Turn | 1454 | 1467 |
| EQ-Bench Creative Writing | 1794 | — |
| EQ-Bench 4 | 1236 | — |
Frequently asked questions
Is Claude Sonnet 5 better than GLM-5.3-Flash?
Claude Sonnet 5 is the stronger model overall, scoring 54.6 to 51.8 on the Noometry Index. GLM-5.3-Flash costs 17× less per token, which makes it the better buy when Claude Sonnet 5's lead doesn't matter for your workload.
Which is cheaper, Claude Sonnet 5 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 Sonnet 5 lists at $2 and $10.
Is Claude Sonnet 5 or GLM-5.3-Flash better for coding?
Claude Sonnet 5 scores higher on coding benchmarks: 55.5 versus 53.1 in the Noometry coding category.
Which has the bigger context window?
Both accept 1M tokens.
How many benchmarks do Claude Sonnet 5 and GLM-5.3-Flash share?
36 benchmarks have published results for both models. Claude Sonnet 5 has 51 scored results on Noometry and GLM-5.3-Flash has 40.