Model comparison
Claude Sonnet 4 vs GLM-5.3
GLM-5.3 is the stronger model overall, scoring 54.6 to 40.6 on the Noometry Index.
Last verified . 27 shared benchmarks.
Summary
- They share 27 benchmarks with published results for both. Claude Sonnet 4 scores higher in 1 category and GLM-5.3 in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3 leads 45.2 to 23.0.
- The biggest single-benchmark swing is WeirdML: 46.1% for Claude Sonnet 4 and 75.4% for GLM-5.3.
- GLM-5.3 is cheaper at $1.40 / $4.40 per million input/output tokens, against $3 / $15 for Claude Sonnet 4.
- GLM-5.3 accepts more context: 1M tokens versus 200K.
- GLM-5.3 has downloadable open weights; the other is API-only.
Side by side
| Claude Sonnet 4 | GLM-5.3 | |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 40.6 | 54.6 |
| Released | 2025-05-22 | 2026-08-14 |
| Weights | Proprietary | Open |
| Context window | 200K | 1M |
| Max output | 64K | 131K |
| Input $ / M tokens | $3 | $1.40 |
| Output $ / M tokens | $15 | $4.40 |
| Results tracked | 58 | 41 |
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Category by category
Coding GLM-5.3 leads
Claude Sonnet 4: 44.0 (#70), GLM-5.3: 59.0 (#12)
| Benchmark | Claude Sonnet 4 | GLM-5.3 |
|---|---|---|
| SciCode | 40% | 59% |
| WeirdML | 46.1% | 75.4% |
| LMArena Coding | 1414 | 1499 |
| ALE-Bench | 655.35 | 1,317 |
| DeepSWE | — | 69% |
| FrontierCode | — | 40.1% |
| SWE-bench Verified (bash only) | 64.9% | — |
| Aider Polyglot | 61.3% | — |
| LMArena WebDev | — | 1623 |
| FrontierSWE | — | 30.2% |
| GSO | 4.9% | — |
Agentic & Tool Use Claude Sonnet 4 leads
Claude Sonnet 4: 38.3 (#29), GLM-5.3: 36.3 (#34)
| Benchmark | Claude Sonnet 4 | GLM-5.3 |
|---|---|---|
| APEX-Agents | — | 56.6% |
| TheAgentCompany | 33.1% | — |
| Cybench | 35% | — |
| DeepResearch Bench | 46.6% | — |
| OSWorld | 43.9% | — |
| METR Time Horizons | 62% | — |
| Vending-Bench 2 | — | 8,164 |
Reasoning GLM-5.3 leads
Claude Sonnet 4: 23.0 (#169), GLM-5.3: 45.2 (#42)
| Benchmark | Claude Sonnet 4 | GLM-5.3 |
|---|---|---|
| CritPt | 0.3% | 19.1% |
| LMArena Hard Prompts | 1372 | 1491 |
| DTBench | 77.1% | 87.7% |
| LMCA | 29% | 55.5% |
| Epoch Capabilities Index | 141.69 | 155.56 |
| ARC-AGI-2 | 5.9% | — |
| SimpleBench | 45.5% | — |
| Kagi LLM Benchmark | 73% | — |
| NYT Connections (extended) | — | 74.2% |
| ARC-AGI-1 | 40% | — |
| Chess Puzzles | — | 21% |
| EnigmaEval | 3.1% | — |
| Mystery Game Puzzles | — | 33% |
| Bench to the Future 3 | — | 0.15 |
| ForecastBench | 60.2 | — |
Math GLM-5.3 leads
Claude Sonnet 4: 41.5 (#71), GLM-5.3: 62.1 (#27)
| Benchmark | Claude Sonnet 4 | GLM-5.3 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 71.1% | 91.1% |
| LMArena Math | 1375 | 1493 |
| FrontierMath (Tiers 1-3) | — | 68.8% |
| FrontierMath Tier 4 | — | 29.3% |
| ProofBench | — | 49% |
| Omni-MATH | 60.2% | — |
| MATH Level 5 | 84.4% | — |
| FrontierMath (Feb 2025 set) | 4.1% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge GLM-5.3 leads
Claude Sonnet 4: 41.6 (#99), GLM-5.3: 58.3 (#34)
| Benchmark | Claude Sonnet 4 | GLM-5.3 |
|---|---|---|
| GPQA Diamond | 79.2% | 90.9% |
| LMArena Expert | 1371 | 1512 |
| Humanity's Last Exam | 7.8% | — |
| SimpleQA Verified | — | 41% |
| MMLU-Pro | 84.3% | — |
| Confabulations | 13.2% | — |
| Vectara Hallucination Rate | 10.3% | — |
| GPQA (HELM) | 70.6% | — |
Multimodal Not comparable
Claude Sonnet 4: 25.6 (#116), GLM-5.3: —
| Benchmark | Claude Sonnet 4 | GLM-5.3 |
|---|---|---|
| LMArena Vision | 1191 | — |
| GeoBench | 37% | — |
| VPCT | 34% | — |
| MindCube | 44.8% | — |
Multilingual GLM-5.3 leads
Claude Sonnet 4: 46.9 (#151), GLM-5.3: 55.9 (#27)
| Benchmark | Claude Sonnet 4 | GLM-5.3 |
|---|---|---|
| LMArena Non-English | 1333 | 1457 |
| LMArena Chinese | 1351 | 1523 |
| LMArena French | 1364 | 1499 |
| LMArena German | 1331 | 1505 |
| LMArena Japanese | 1301 | 1438 |
| LMArena Korean | 1293 | 1478 |
| LMArena Russian | 1355 | 1460 |
| LMArena Spanish | 1356 | 1444 |
Instruction Following GLM-5.3 leads
Claude Sonnet 4: 71.8 (#141), GLM-5.3: 77.8 (#15)
| Benchmark | Claude Sonnet 4 | GLM-5.3 |
|---|---|---|
| LMArena Instruction Following | 1375 | 1481 |
| IFEval | 84% | — |
Long Context GLM-5.3 leads
Claude Sonnet 4: 33.6 (#254), GLM-5.3: 45.5 (#32)
| Benchmark | Claude Sonnet 4 | GLM-5.3 |
|---|---|---|
| LMArena Longer Query | 1398 | 1485 |
| Fiction.LiveBench | 46.9% | — |
Writing & Preference GLM-5.3 leads
Claude Sonnet 4: 57.3 (#127), GLM-5.3: 75.9 (#6)
| Benchmark | Claude Sonnet 4 | GLM-5.3 |
|---|---|---|
| LMArena Text | 1351 | 1473 |
| LMArena Creative Writing | 1345 | 1459 |
| EQ-Bench Creative Writing | 1483 | 2075 |
| LMArena Multi-Turn | 1376 | 1473 |
| Short-Story Creative Writing | 81.4% | — |
| WildBench | 83.8% | — |
Frequently asked questions
Is Claude Sonnet 4 better than GLM-5.3?
GLM-5.3 is the stronger model overall, scoring 54.6 to 40.6 on the Noometry Index.
Which is cheaper, Claude Sonnet 4 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 Sonnet 4 lists at $3 and $15.
Is Claude Sonnet 4 or GLM-5.3 better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.0 versus 44.0 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3 does, with 1M tokens against 200K.
How many benchmarks do Claude Sonnet 4 and GLM-5.3 share?
27 benchmarks have published results for both models. Claude Sonnet 4 has 58 scored results on Noometry and GLM-5.3 has 41.