Model comparison
Claude 3.7 Sonnet vs GLM-5
GLM-5 is the stronger model overall, scoring 46.1 to 39.5 on the Noometry Index.
Last verified . 28 shared benchmarks.
Summary
- They share 28 benchmarks with published results for both. Claude 3.7 Sonnet scores higher in 2 categories and GLM-5 in 7 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5 leads 52.3 to 39.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 57.8% for Claude 3.7 Sonnet and 80% for GLM-5.
- GLM-5 has downloadable open weights; the other is API-only.
Side by side
| Claude 3.7 Sonnet | GLM-5 | |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 39.5 | 46.1 |
| Released | 2025-02-24 | 2026-02-11 |
| Weights | Proprietary | Open |
| Context window | — | 205K |
| Max output | — | 131K |
| Input $ / M tokens | — | $1 |
| Output $ / M tokens | — | $3.20 |
| Results tracked | 58 | 45 |
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Category by category
Coding GLM-5 leads
Claude 3.7 Sonnet: 40.6 (#136), GLM-5: 49.0 (#52)
| Benchmark | Claude 3.7 Sonnet | GLM-5 |
|---|---|---|
| SWE-bench Verified | 61% | 72.1% |
| SWE-bench Verified (bash only) | 52.8% | 72.8% |
| LMArena Coding | 1361 | 1461 |
| Aider Polyglot | 64.9% | — |
| LMArena WebDev | — | 1434 |
| SWE-bench Multilingual | — | 69.7% |
| GSO | 3.8% | — |
| WeirdML | — | 48.2% |
| LiveBench Coding | 74.5% | — |
| CadEval | 54% | — |
| ALE-Bench | — | 765.62 |
Agentic & Tool Use Claude 3.7 Sonnet leads
Claude 3.7 Sonnet: 34.1 (#50), GLM-5: 31.1 (#71)
| Benchmark | Claude 3.7 Sonnet | GLM-5 |
|---|---|---|
| Terminal-Bench | — | 52.4% |
| TheAgentCompany | 30.9% | — |
| τ²-bench Airline | — | 82.5% |
| τ²-bench Banking | — | 9.8% |
| τ²-bench Retail | — | 73.7% |
| τ²-bench Telecom | — | 86.8% |
| Cybench | 20% | — |
| DeepResearch Bench | 43.6% | — |
| OSWorld | 35.8% | — |
| METR Time Horizons | 60% | — |
| Vending-Bench 2 | — | 4,432 |
Reasoning GLM-5 leads
Claude 3.7 Sonnet: 18.6 (#277), GLM-5: 27.6 (#116)
| Benchmark | Claude 3.7 Sonnet | GLM-5 |
|---|---|---|
| ARC-AGI-2 | 0.9% | 4.9% |
| SimpleBench | 46.4% | 53.2% |
| ARC-AGI-1 | 28.6% | 44.7% |
| LMArena Hard Prompts | 1333 | 1452 |
| Epoch Capabilities Index | 141.16 | 145.83 |
| ForecastBench | 61.8 | 61 |
| Kagi LLM Benchmark | — | 75% |
| NYT Connections (extended) | — | 74.8% |
| Chess Puzzles | — | 10% |
| EnigmaEval | 4.2% | — |
| LiveBench Reasoning | 87.8% | — |
| LiveBench Data Analysis | 74% | — |
| LiveBench | 76.1% | — |
Math GLM-5 leads
Claude 3.7 Sonnet: 37.5 (#153), GLM-5: 46.4 (#71)
| Benchmark | Claude 3.7 Sonnet | GLM-5 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 57.8% | 80% |
| LMArena Math | 1337 | 1440 |
| FrontierMath (Feb 2025 set) | 4.1% | 16.4% |
| MathArena Final-Answer Competitions | — | 65.7% |
| Omni-MATH | 33% | — |
| LiveBench Math | 79% | — |
| MATH Level 5 | 91.2% | — |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge GLM-5 leads
Claude 3.7 Sonnet: 39.8 (#130), GLM-5: 52.3 (#64)
| Benchmark | Claude 3.7 Sonnet | GLM-5 |
|---|---|---|
| GPQA Diamond | 79.7% | 87.8% |
| LMArena Expert | 1321 | 1454 |
| Humanity's Last Exam | 8% | — |
| MMLU-Pro | 78.4% | — |
| Confabulations | 14.7% | — |
| Vectara Hallucination Rate | — | 10.1% |
| GPQA (HELM) | 60.8% | — |
Multimodal Not comparable
Claude 3.7 Sonnet: 33.7 (#95), GLM-5: —
| Benchmark | Claude 3.7 Sonnet | GLM-5 |
|---|---|---|
| LMArena Vision | 1169 | — |
| GeoBench | 68% | — |
| VPCT | 39% | — |
| SpatialViz-Bench | 33.9% | — |
Multilingual GLM-5 leads
Claude 3.7 Sonnet: 44.1 (#179), GLM-5: 53.7 (#58)
| Benchmark | Claude 3.7 Sonnet | GLM-5 |
|---|---|---|
| LMArena Non-English | 1296 | 1430 |
| LMArena Chinese | 1299 | 1511 |
| LMArena French | 1303 | 1455 |
| LMArena German | 1301 | 1445 |
| LMArena Japanese | 1267 | 1416 |
| LMArena Korean | 1249 | 1423 |
| LMArena Russian | 1311 | 1436 |
| LMArena Spanish | 1298 | 1454 |
Instruction Following GLM-5 leads
Claude 3.7 Sonnet: 72.9 (#125), GLM-5: 75.2 (#67)
| Benchmark | Claude 3.7 Sonnet | GLM-5 |
|---|---|---|
| LMArena Instruction Following | 1352 | 1428 |
| LiveBench Instruction Following | 81.3% | — |
| IFEval | 83.4% | — |
Long Context Claude 3.7 Sonnet leads
Claude 3.7 Sonnet: 50.3 (#10), GLM-5: 44.7 (#60)
| Benchmark | Claude 3.7 Sonnet | GLM-5 |
|---|---|---|
| LMArena Longer Query | 1373 | 1446 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | — | 18.7% |
Writing & Preference GLM-5 leads
Claude 3.7 Sonnet: 54.4 (#150), GLM-5: 66.0 (#38)
| Benchmark | Claude 3.7 Sonnet | GLM-5 |
|---|---|---|
| LMArena Text | 1314 | 1446 |
| LMArena Creative Writing | 1332 | 1439 |
| EQ-Bench Creative Writing | 1412 | 1601 |
| LMArena Multi-Turn | 1339 | 1456 |
| Short-Story Creative Writing | 81.1% | — |
| WildBench | 81.4% | — |
| LiveBench Language | 59.9% | — |
Frequently asked questions
Is Claude 3.7 Sonnet better than GLM-5?
GLM-5 is the stronger model overall, scoring 46.1 to 39.5 on the Noometry Index.
Is Claude 3.7 Sonnet or GLM-5 better for coding?
GLM-5 scores higher on coding benchmarks: 49.0 versus 40.6 in the Noometry coding category.
How many benchmarks do Claude 3.7 Sonnet and GLM-5 share?
28 benchmarks have published results for both models. Claude 3.7 Sonnet has 58 scored results on Noometry and GLM-5 has 45.