Model comparison
Claude Sonnet 4.5 vs GLM-5
GLM-5 is the stronger model overall, scoring 46.1 to 44.1 on the Noometry Index.
Last verified . 43 shared benchmarks.
Summary
- They share 43 benchmarks with published results for both. Claude Sonnet 4.5 scores higher in 3 categories and GLM-5 in 6 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5 leads 46.4 to 32.3.
- The biggest single-benchmark swing is NYT Connections (extended): 37.3% for Claude Sonnet 4.5 and 74.8% for GLM-5.
- GLM-5 is cheaper at $1 / $3.20 per million input/output tokens, against $3 / $15 for Claude Sonnet 4.5.
- GLM-5 accepts more context: 205K tokens versus 200K.
- GLM-5 has downloadable open weights; the other is API-only.
Side by side
| Claude Sonnet 4.5 | GLM-5 | |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 44.1 | 46.1 |
| Released | 2025-09-29 | 2026-02-11 |
| Weights | Proprietary | Open |
| Context window | 200K | 205K |
| Max output | 64K | 131K |
| Input $ / M tokens | $3 | $1 |
| Output $ / M tokens | $15 | $3.20 |
| Results tracked | 73 | 45 |
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Category by category
Coding GLM-5 leads
Claude Sonnet 4.5: 47.3 (#61), GLM-5: 49.0 (#52)
| Benchmark | Claude Sonnet 4.5 | GLM-5 |
|---|---|---|
| SWE-bench Verified | 71.3% | 72.1% |
| SWE-bench Verified (bash only) | 71.4% | 72.8% |
| LMArena WebDev | 1393 | 1434 |
| SWE-bench Multilingual | 67% | 69.7% |
| WeirdML | 47.7% | 48.2% |
| LMArena Coding | 1489 | 1461 |
| ALE-Bench | 796.15 | 765.62 |
| SciCode | 44.7% | — |
| GSO | 14.7% | — |
| AlgoTune | 1.52 | — |
Agentic & Tool Use Claude Sonnet 4.5 leads
Claude Sonnet 4.5: 38.3 (#32), GLM-5: 31.1 (#71)
| Benchmark | Claude Sonnet 4.5 | GLM-5 |
|---|---|---|
| Terminal-Bench | 46.5% | 52.4% |
| τ²-bench Airline | 72% | 82.5% |
| τ²-bench Banking | 25.3% | 9.8% |
| τ²-bench Retail | 72.4% | 73.7% |
| τ²-bench Telecom | 84.9% | 86.8% |
| Vending-Bench 2 | 3,839 | 4,432 |
| Berkeley Function Calling Leaderboard | 73.2% | — |
| GDPval | 42.5% | — |
| Remote Labor Index | 2.1% | — |
| Cybench | 60% | — |
| DeepResearch Bench | 52.6% | — |
| OSWorld | 62.9% | — |
| LMArena Search | 1159 | — |
| METR Time Horizons | 67.4% | — |
Reasoning Too close to call
Claude Sonnet 4.5: 26.9 (#125), GLM-5: 27.6 (#116)
| Benchmark | Claude Sonnet 4.5 | GLM-5 |
|---|---|---|
| ARC-AGI-2 | 13.6% | 4.9% |
| SimpleBench | 54.3% | 53.2% |
| Kagi LLM Benchmark | 57.9% | 75% |
| NYT Connections (extended) | 37.3% | 74.8% |
| ARC-AGI-1 | 63.7% | 44.7% |
| Chess Puzzles | 12% | 10% |
| LMArena Hard Prompts | 1462 | 1452 |
| Epoch Capabilities Index | 146.84 | 145.83 |
| ForecastBench | 61.9 | 61 |
| CritPt | 1.1% | — |
| EnigmaEval | 6% | — |
| EBR-Bench | 2.4% | — |
| Mystery Game Puzzles | 17% | — |
| DTBench | 83.2% | — |
| LMCA | 38.8% | — |
Math GLM-5 leads
Claude Sonnet 4.5: 32.3 (#216), GLM-5: 46.4 (#71)
| Benchmark | Claude Sonnet 4.5 | GLM-5 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 77.8% | 80% |
| LMArena Math | 1449 | 1440 |
| FrontierMath (Feb 2025 set) | 15.2% | 16.4% |
| FrontierMath Tier 4 (v1) | 4.2% | 2.1% |
| FrontierMath (Tiers 1-3) | 23.9% | — |
| FrontierMath Tier 4 | 2.4% | — |
| MathArena Final-Answer Competitions | — | 65.7% |
| ProofBench | 19% | — |
| Omni-MATH | 55.3% | — |
| MATH Level 5 | 97.7% | — |
Knowledge GLM-5 leads
Claude Sonnet 4.5: 48.4 (#76), GLM-5: 52.3 (#64)
| Benchmark | Claude Sonnet 4.5 | GLM-5 |
|---|---|---|
| GPQA Diamond | 82.3% | 87.8% |
| Vectara Hallucination Rate | 12% | 10.1% |
| LMArena Expert | 1482 | 1454 |
| Humanity's Last Exam | 13.7% | — |
| SimpleQA Verified | 30.7% | — |
| MMLU-Pro | 86.9% | — |
| GPQA (HELM) | 68.6% | — |
Multimodal Not comparable
Claude Sonnet 4.5: 34.8 (#89), GLM-5: —
| Benchmark | Claude Sonnet 4.5 | GLM-5 |
|---|---|---|
| VPCT | 39.8% | — |
| LMArena Document | 1450 | — |
Multilingual Too close to call
Claude Sonnet 4.5: 53.4 (#69), GLM-5: 53.7 (#58)
| Benchmark | Claude Sonnet 4.5 | GLM-5 |
|---|---|---|
| LMArena Non-English | 1425 | 1430 |
| LMArena Chinese | 1459 | 1511 |
| LMArena French | 1458 | 1455 |
| LMArena German | 1427 | 1445 |
| LMArena Japanese | 1390 | 1416 |
| LMArena Korean | 1403 | 1423 |
| LMArena Russian | 1437 | 1436 |
| LMArena Spanish | 1457 | 1454 |
Instruction Following Too close to call
Claude Sonnet 4.5: 75.0 (#78), GLM-5: 75.2 (#67)
| Benchmark | Claude Sonnet 4.5 | GLM-5 |
|---|---|---|
| LMArena Instruction Following | 1459 | 1428 |
| IFEval | 85% | — |
Long Context Too close to call
Claude Sonnet 4.5: 45.2 (#46), GLM-5: 44.7 (#60)
| Benchmark | Claude Sonnet 4.5 | GLM-5 |
|---|---|---|
| LMArena Longer Query | 1476 | 1446 |
| CL-bench | — | 18.7% |
Writing & Preference Too close to call
Claude Sonnet 4.5: 66.5 (#34), GLM-5: 66.0 (#38)
| Benchmark | Claude Sonnet 4.5 | GLM-5 |
|---|---|---|
| LMArena Text | 1439 | 1446 |
| LMArena Creative Writing | 1442 | 1439 |
| EQ-Bench Creative Writing | 1678 | 1601 |
| LMArena Multi-Turn | 1465 | 1456 |
| WildBench | 85.4% | — |
Frequently asked questions
Is Claude Sonnet 4.5 better than GLM-5?
GLM-5 is the stronger model overall, scoring 46.1 to 44.1 on the Noometry Index.
Which is cheaper, Claude Sonnet 4.5 or GLM-5?
GLM-5 is cheaper. It lists at $1 per million input tokens and $3.20 per million output tokens; Claude Sonnet 4.5 lists at $3 and $15.
Is Claude Sonnet 4.5 or GLM-5 better for coding?
GLM-5 scores higher on coding benchmarks: 49.0 versus 47.3 in the Noometry coding category.
Which has the bigger context window?
GLM-5 does, with 205K tokens against 200K.
How many benchmarks do Claude Sonnet 4.5 and GLM-5 share?
43 benchmarks have published results for both models. Claude Sonnet 4.5 has 73 scored results on Noometry and GLM-5 has 45.