Model comparison
Claude Sonnet 4.5 vs GLM-4.6V
Claude Sonnet 4.5 is the stronger model overall, scoring 44.1 to 41.3 on the Noometry Index. GLM-4.6V costs 13× less per token, which makes it the better buy when Claude Sonnet 4.5's lead doesn't matter for your workload.
Last verified . 11 shared benchmarks.
Summary
- They share 11 benchmarks with published results for both. Claude Sonnet 4.5 scores higher in 6 categories and GLM-4.6V in 2 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Claude Sonnet 4.5 leads 48.4 to 38.0.
- GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $3 / $15 for Claude Sonnet 4.5.
- Claude Sonnet 4.5 accepts more context: 200K tokens versus 128K.
- GLM-4.6V has downloadable open weights; the other is API-only.
Side by side
| Claude Sonnet 4.5 | GLM-4.6V | |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 44.1 | 41.3 |
| Released | 2025-09-29 | 2025-12-08 |
| Weights | Proprietary | Open |
| Context window | 200K | 128K |
| Max output | 64K | 33K |
| Input $ / M tokens | $3 | $0.30 |
| Output $ / M tokens | $15 | $0.90 |
| Results tracked | 73 | 12 |
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Category by category
Coding Claude Sonnet 4.5 leads
Claude Sonnet 4.5: 47.3 (#61), GLM-4.6V: 40.9 (#128)
| Benchmark | Claude Sonnet 4.5 | GLM-4.6V |
|---|---|---|
| LMArena Coding | 1489 | 1390 |
| SWE-bench Verified | 71.3% | — |
| SWE-bench Verified (bash only) | 71.4% | — |
| LMArena WebDev | 1393 | — |
| SWE-bench Multilingual | 67% | — |
| SciCode | 44.7% | — |
| GSO | 14.7% | — |
| WeirdML | 47.7% | — |
| ALE-Bench | 796.15 | — |
| AlgoTune | 1.52 | — |
Agentic & Tool Use Not comparable
Claude Sonnet 4.5: 38.3 (#32), GLM-4.6V: —
| Benchmark | Claude Sonnet 4.5 | GLM-4.6V |
|---|---|---|
| Terminal-Bench | 46.5% | — |
| Berkeley Function Calling Leaderboard | 73.2% | — |
| GDPval | 42.5% | — |
| Remote Labor Index | 2.1% | — |
| τ²-bench Airline | 72% | — |
| τ²-bench Banking | 25.3% | — |
| τ²-bench Retail | 72.4% | — |
| τ²-bench Telecom | 84.9% | — |
| Cybench | 60% | — |
| DeepResearch Bench | 52.6% | — |
| OSWorld | 62.9% | — |
| LMArena Search | 1159 | — |
| METR Time Horizons | 67.4% | — |
| Vending-Bench 2 | 3,839 | — |
Reasoning Too close to call
Claude Sonnet 4.5: 26.9 (#125), GLM-4.6V: 27.6 (#115)
| Benchmark | Claude Sonnet 4.5 | GLM-4.6V |
|---|---|---|
| LMArena Hard Prompts | 1462 | 1368 |
| ARC-AGI-2 | 13.6% | — |
| SimpleBench | 54.3% | — |
| Kagi LLM Benchmark | 57.9% | — |
| NYT Connections (extended) | 37.3% | — |
| ARC-AGI-1 | 63.7% | — |
| CritPt | 1.1% | — |
| Chess Puzzles | 12% | — |
| EnigmaEval | 6% | — |
| EBR-Bench | 2.4% | — |
| Mystery Game Puzzles | 17% | — |
| DTBench | 83.2% | — |
| LMCA | 38.8% | — |
| Epoch Capabilities Index | 146.84 | — |
| ForecastBench | 61.9 | — |
Math Not comparable
Claude Sonnet 4.5: 32.3 (#216), GLM-4.6V: —
| Benchmark | Claude Sonnet 4.5 | GLM-4.6V |
|---|---|---|
| FrontierMath (Tiers 1-3) | 23.9% | — |
| FrontierMath Tier 4 | 2.4% | — |
| OTIS Mock AIME 2024-2025 | 77.8% | — |
| ProofBench | 19% | — |
| Omni-MATH | 55.3% | — |
| LMArena Math | 1449 | — |
| MATH Level 5 | 97.7% | — |
| FrontierMath (Feb 2025 set) | 15.2% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge Claude Sonnet 4.5 leads
Claude Sonnet 4.5: 48.4 (#76), GLM-4.6V: 38.0 (#149)
| Benchmark | Claude Sonnet 4.5 | GLM-4.6V |
|---|---|---|
| LMArena Expert | 1482 | 1371 |
| GPQA Diamond | 82.3% | — |
| Humanity's Last Exam | 13.7% | — |
| SimpleQA Verified | 30.7% | — |
| MMLU-Pro | 86.9% | — |
| Vectara Hallucination Rate | 12% | — |
| GPQA (HELM) | 68.6% | — |
Multimodal Too close to call
Claude Sonnet 4.5: 34.8 (#89), GLM-4.6V: 34.8 (#90)
| Benchmark | Claude Sonnet 4.5 | GLM-4.6V |
|---|---|---|
| LMArena Vision | — | 1164 |
| VPCT | 39.8% | — |
| LMArena Document | 1450 | — |
Multilingual Claude Sonnet 4.5 leads
Claude Sonnet 4.5: 53.4 (#69), GLM-4.6V: 48.6 (#141)
| Benchmark | Claude Sonnet 4.5 | GLM-4.6V |
|---|---|---|
| LMArena Non-English | 1425 | 1359 |
| LMArena Chinese | 1459 | 1425 |
| LMArena Russian | 1437 | 1340 |
| LMArena French | 1458 | — |
| LMArena German | 1427 | — |
| LMArena Japanese | 1390 | — |
| LMArena Korean | 1403 | — |
| LMArena Spanish | 1457 | — |
Instruction Following Claude Sonnet 4.5 leads
Claude Sonnet 4.5: 75.0 (#78), GLM-4.6V: 71.4 (#151)
| Benchmark | Claude Sonnet 4.5 | GLM-4.6V |
|---|---|---|
| LMArena Instruction Following | 1459 | 1352 |
| IFEval | 85% | — |
Long Context Claude Sonnet 4.5 leads
Claude Sonnet 4.5: 45.2 (#46), GLM-4.6V: 41.3 (#143)
| Benchmark | Claude Sonnet 4.5 | GLM-4.6V |
|---|---|---|
| LMArena Longer Query | 1476 | 1358 |
Writing & Preference Claude Sonnet 4.5 leads
Claude Sonnet 4.5: 66.5 (#34), GLM-4.6V: 56.6 (#137)
| Benchmark | Claude Sonnet 4.5 | GLM-4.6V |
|---|---|---|
| LMArena Text | 1439 | 1377 |
| LMArena Creative Writing | 1442 | 1347 |
| LMArena Multi-Turn | 1465 | 1360 |
| EQ-Bench Creative Writing | 1678 | — |
| WildBench | 85.4% | — |
Frequently asked questions
Is Claude Sonnet 4.5 better than GLM-4.6V?
Claude Sonnet 4.5 is the stronger model overall, scoring 44.1 to 41.3 on the Noometry Index. GLM-4.6V costs 13× less per token, which makes it the better buy when Claude Sonnet 4.5's lead doesn't matter for your workload.
Which is cheaper, Claude Sonnet 4.5 or GLM-4.6V?
GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Claude Sonnet 4.5 lists at $3 and $15.
Is Claude Sonnet 4.5 or GLM-4.6V better for coding?
Claude Sonnet 4.5 scores higher on coding benchmarks: 47.3 versus 40.9 in the Noometry coding category.
Which has the bigger context window?
Claude Sonnet 4.5 does, with 200K tokens against 128K.
How many benchmarks do Claude Sonnet 4.5 and GLM-4.6V share?
11 benchmarks have published results for both models. Claude Sonnet 4.5 has 73 scored results on Noometry and GLM-4.6V has 12.