Model comparison
Claude 3.7 Sonnet vs GLM-5.3-Flash
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 39.5 on the Noometry Index.
Last verified . 23 shared benchmarks.
Summary
- They share 23 benchmarks with published results for both. Claude 3.7 Sonnet scores higher in 1 category and GLM-5.3-Flash in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 18.6.
- The biggest single-benchmark swing is ARC-AGI-2: 0.9% for Claude 3.7 Sonnet and 65.8% for GLM-5.3-Flash.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.
Side by side
| Claude 3.7 Sonnet | GLM-5.3-Flash | |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 39.5 | 51.8 |
| Released | 2025-02-24 | 2026-08-20 |
| Weights | Proprietary | Open |
| Context window | — | 1M |
| Max output | — | 131K |
| Input $ / M tokens | — | $0.15 |
| Output $ / M tokens | — | $0.50 |
| Results tracked | 58 | 40 |
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Category by category
Coding GLM-5.3-Flash leads
Claude 3.7 Sonnet: 40.6 (#136), GLM-5.3-Flash: 53.1 (#31)
| Benchmark | Claude 3.7 Sonnet | GLM-5.3-Flash |
|---|---|---|
| LMArena Coding | 1361 | 1508 |
| SWE-bench Verified | 61% | — |
| DeepSWE | — | 63.4% |
| FrontierCode | — | 31.8% |
| SWE-bench Verified (bash only) | 52.8% | — |
| Aider Polyglot | 64.9% | — |
| CursorBench | — | 36.8% |
| LMArena WebDev | — | 1609 |
| FrontierSWE | — | 18.1% |
| SciCode | — | 51.6% |
| GSO | 3.8% | — |
| LiveBench Coding | 74.5% | — |
| CadEval | 54% | — |
| ALE-Bench | — | 303.55 |
Agentic & Tool Use Too close to call
Claude 3.7 Sonnet: 34.1 (#50), GLM-5.3-Flash: 34.2 (#47)
| Benchmark | Claude 3.7 Sonnet | GLM-5.3-Flash |
|---|---|---|
| APEX-Agents | — | 52.8% |
| TheAgentCompany | 30.9% | — |
| Cybench | 20% | — |
| DeepResearch Bench | 43.6% | — |
| OSWorld | 35.8% | — |
| GDP.pdf | — | 14% |
| METR Time Horizons | 60% | — |
Reasoning GLM-5.3-Flash leads
Claude 3.7 Sonnet: 18.6 (#277), GLM-5.3-Flash: 48.0 (#42)
| Benchmark | Claude 3.7 Sonnet | GLM-5.3-Flash |
|---|---|---|
| ARC-AGI-2 | 0.9% | 65.8% |
| ARC-AGI-1 | 28.6% | 91% |
| LMArena Hard Prompts | 1333 | 1491 |
| Epoch Capabilities Index | 141.16 | 151.88 |
| SimpleBench | 46.4% | — |
| CritPt | — | 15.4% |
| Chess Puzzles | — | 14% |
| EnigmaEval | 4.2% | — |
| LiveBench Reasoning | 87.8% | — |
| Mystery Game Puzzles | — | 8% |
| LiveBench Data Analysis | 74% | — |
| Surface Evolver Bench | — | 52.5% |
| Bench to the Future 3 | — | 0.15 |
| ForecastBench | 61.8 | — |
| LiveBench | 76.1% | — |
Math GLM-5.3-Flash leads
Claude 3.7 Sonnet: 37.5 (#153), GLM-5.3-Flash: 53.3 (#47)
| Benchmark | Claude 3.7 Sonnet | GLM-5.3-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 57.8% | 93.9% |
| LMArena Math | 1337 | 1500 |
| FrontierMath (Tiers 1-3) | — | 55.8% |
| FrontierMath Tier 4 | — | 17.1% |
| ProofBench | — | 21% |
| Omni-MATH | 33% | — |
| LiveBench Math | 79% | — |
| MATH Level 5 | 91.2% | — |
| FrontierMath (Feb 2025 set) | 4.1% | — |
Knowledge GLM-5.3-Flash leads
Claude 3.7 Sonnet: 39.8 (#130), GLM-5.3-Flash: 58.4 (#36)
| Benchmark | Claude 3.7 Sonnet | GLM-5.3-Flash |
|---|---|---|
| GPQA Diamond | 79.7% | 90.2% |
| LMArena Expert | 1321 | 1513 |
| Humanity's Last Exam | 8% | — |
| MMLU-Pro | 78.4% | — |
| Confabulations | 14.7% | — |
| GPQA (HELM) | 60.8% | — |
Multimodal GLM-5.3-Flash leads
Claude 3.7 Sonnet: 33.7 (#95), GLM-5.3-Flash: 42.8 (#27)
| Benchmark | Claude 3.7 Sonnet | GLM-5.3-Flash |
|---|---|---|
| LMArena Vision | 1169 | 1296 |
| GeoBench | 68% | — |
| VPCT | 39% | — |
| SpatialViz-Bench | 33.9% | — |
Multilingual GLM-5.3-Flash leads
Claude 3.7 Sonnet: 44.1 (#179), GLM-5.3-Flash: 56.0 (#25)
| Benchmark | Claude 3.7 Sonnet | GLM-5.3-Flash |
|---|---|---|
| LMArena Non-English | 1296 | 1462 |
| LMArena Chinese | 1299 | 1527 |
| LMArena French | 1303 | 1496 |
| LMArena German | 1301 | 1470 |
| LMArena Japanese | 1267 | 1429 |
| LMArena Korean | 1249 | 1446 |
| LMArena Russian | 1311 | 1469 |
| LMArena Spanish | 1298 | 1471 |
Instruction Following GLM-5.3-Flash leads
Claude 3.7 Sonnet: 72.9 (#125), GLM-5.3-Flash: 77.5 (#20)
| Benchmark | Claude 3.7 Sonnet | GLM-5.3-Flash |
|---|---|---|
| LMArena Instruction Following | 1352 | 1478 |
| LiveBench Instruction Following | 81.3% | — |
| IFEval | 83.4% | — |
Long Context Claude 3.7 Sonnet leads
Claude 3.7 Sonnet: 50.3 (#10), GLM-5.3-Flash: 45.4 (#39)
| Benchmark | Claude 3.7 Sonnet | GLM-5.3-Flash |
|---|---|---|
| LMArena Longer Query | 1373 | 1482 |
| Fiction.LiveBench | 83.3% | — |
Writing & Preference GLM-5.3-Flash leads
Claude 3.7 Sonnet: 54.4 (#150), GLM-5.3-Flash: 65.3 (#50)
| Benchmark | Claude 3.7 Sonnet | GLM-5.3-Flash |
|---|---|---|
| LMArena Text | 1314 | 1471 |
| LMArena Creative Writing | 1332 | 1442 |
| LMArena Multi-Turn | 1339 | 1467 |
| Short-Story Creative Writing | 81.1% | — |
| EQ-Bench Creative Writing | 1412 | — |
| WildBench | 81.4% | — |
| LiveBench Language | 59.9% | — |
Frequently asked questions
Is Claude 3.7 Sonnet better than GLM-5.3-Flash?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 39.5 on the Noometry Index.
Is Claude 3.7 Sonnet or GLM-5.3-Flash better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 40.6 in the Noometry coding category.
How many benchmarks do Claude 3.7 Sonnet and GLM-5.3-Flash share?
23 benchmarks have published results for both models. Claude 3.7 Sonnet has 58 scored results on Noometry and GLM-5.3-Flash has 40.