Model comparison
Claude Sonnet 4.6 vs GLM-4.5V
Claude Sonnet 4.6 is the stronger model overall, scoring 50.3 to 39.8 on the Noometry Index. GLM-4.5V costs 6.7× less per token, which makes it the better buy when Claude Sonnet 4.6's lead doesn't matter for your workload.
Last verified . 14 shared benchmarks.
Summary
- They share 14 benchmarks with published results for both. Claude Sonnet 4.6 scores higher in 9 categories and GLM-4.5V in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Claude Sonnet 4.6 leads 46.1 to 27.4.
- GLM-4.5V is cheaper at $0.60 / $1.80 per million input/output tokens, against $3 / $15 for Claude Sonnet 4.6.
- Claude Sonnet 4.6 accepts more context: 1M tokens versus 64K.
- GLM-4.5V has downloadable open weights; the other is API-only.
Side by side
| Claude Sonnet 4.6 | GLM-4.5V | |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 50.3 | 39.8 |
| Released | 2026-02-17 | 2025-08-11 |
| Weights | Proprietary | Open |
| Context window | 1M | 64K |
| Max output | 128K | 16K |
| Input $ / M tokens | $3 | $0.60 |
| Output $ / M tokens | $15 | $1.80 |
| Results tracked | 57 | 15 |
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Category by category
Coding Claude Sonnet 4.6 leads
Claude Sonnet 4.6: 46.3 (#67), GLM-4.5V: 39.5 (#155)
| Benchmark | Claude Sonnet 4.6 | GLM-4.5V |
|---|---|---|
| LMArena Coding | 1504 | 1347 |
| SWE-bench Verified | 75.2% | — |
| DeepSWE | 29.9% | — |
| FrontierCode | 24.3% | — |
| LMArena WebDev | 1522 | — |
| SciCode | 46.8% | — |
| WeirdML | 66.1% | — |
| ALE-Bench | 1,327 | — |
Agentic & Tool Use Not comparable
Claude Sonnet 4.6: 39.1 (#28), GLM-4.5V: —
| Benchmark | Claude Sonnet 4.6 | GLM-4.5V |
|---|---|---|
| Terminal-Bench | 53.4% | — |
| APEX-Agents | 43% | — |
| OSWorld 2.0 | 9.3% | — |
| DeepResearch Bench | 54.9% | — |
| OSWorld | 72.1% | — |
| ExploitBench | 23.6% | — |
| GBAEval | 48.8% | — |
| GDP.pdf | 18% | — |
| LMArena Search | 1221 | — |
| Vending-Bench 2 | 7,204 | — |
Reasoning Claude Sonnet 4.6 leads
Claude Sonnet 4.6: 46.1 (#45), GLM-4.5V: 27.4 (#119)
| Benchmark | Claude Sonnet 4.6 | GLM-4.5V |
|---|---|---|
| LMArena Hard Prompts | 1484 | 1334 |
| ARC-AGI-2 | 60.4% | — |
| Kagi LLM Benchmark | — | 59.8% |
| NYT Connections (extended) | 80.9% | — |
| ARC-AGI-1 | 86.5% | — |
| CritPt | 3.1% | — |
| Chess Puzzles | 13% | — |
| Thematic Generalization | 76.3% | — |
| Mystery Game Puzzles | 16% | — |
| DTBench | 89.9% | — |
| LMCA | 46.5% | — |
| Epoch Capabilities Index | 152.24 | — |
| ForecastBench | 62 | — |
Math Claude Sonnet 4.6 leads
Claude Sonnet 4.6: 52.9 (#49), GLM-4.5V: 37.4 (#159)
| Benchmark | Claude Sonnet 4.6 | GLM-4.5V |
|---|---|---|
| LMArena Math | 1462 | 1354 |
| OTIS Mock AIME 2024-2025 | 85.8% | — |
| ProofBench | 45% | — |
| FrontierMath (Feb 2025 set) | 32.4% | — |
| FrontierMath Tier 4 (v1) | 8.3% | — |
Knowledge Claude Sonnet 4.6 leads
Claude Sonnet 4.6: 51.7 (#65), GLM-4.5V: 37.5 (#156)
| Benchmark | Claude Sonnet 4.6 | GLM-4.5V |
|---|---|---|
| LMArena Expert | 1500 | 1353 |
| GPQA Diamond | 87.4% | — |
| SimpleQA Verified | 35.5% | — |
| Vectara Hallucination Rate | 10.6% | — |
Multimodal Claude Sonnet 4.6 leads
Claude Sonnet 4.6: 38.0 (#68), GLM-4.5V: 34.3 (#92)
| Benchmark | Claude Sonnet 4.6 | GLM-4.5V |
|---|---|---|
| LMArena Vision | 1283 | 1154 |
| Blueprint-Bench 2 | 6.7% | — |
| LMArena Document | 1482 | — |
Multilingual Claude Sonnet 4.6 leads
Claude Sonnet 4.6: 54.4 (#41), GLM-4.5V: 44.6 (#177)
| Benchmark | Claude Sonnet 4.6 | GLM-4.5V |
|---|---|---|
| LMArena Non-English | 1440 | 1303 |
| LMArena Chinese | 1491 | 1337 |
| LMArena Russian | 1440 | 1298 |
| LMArena Spanish | 1464 | 1336 |
| LMArena French | 1465 | — |
| LMArena German | 1428 | — |
| LMArena Japanese | 1420 | — |
| LMArena Korean | 1411 | — |
Instruction Following Claude Sonnet 4.6 leads
Claude Sonnet 4.6: 77.4 (#25), GLM-4.5V: 69.2 (#175)
| Benchmark | Claude Sonnet 4.6 | GLM-4.5V |
|---|---|---|
| LMArena Instruction Following | 1475 | 1311 |
Long Context Claude Sonnet 4.6 leads
Claude Sonnet 4.6: 45.3 (#44), GLM-4.5V: 39.6 (#171)
| Benchmark | Claude Sonnet 4.6 | GLM-4.5V |
|---|---|---|
| LMArena Longer Query | 1479 | 1304 |
Writing & Preference Claude Sonnet 4.6 leads
Claude Sonnet 4.6: 70.2 (#22), GLM-4.5V: 52.5 (#170)
| Benchmark | Claude Sonnet 4.6 | GLM-4.5V |
|---|---|---|
| LMArena Text | 1458 | 1333 |
| LMArena Creative Writing | 1435 | 1295 |
| LMArena Multi-Turn | 1464 | 1332 |
| EQ-Bench Creative Writing | 1810 | — |
| EQ-Bench 4 | 1207 | — |
Frequently asked questions
Is Claude Sonnet 4.6 better than GLM-4.5V?
Claude Sonnet 4.6 is the stronger model overall, scoring 50.3 to 39.8 on the Noometry Index. GLM-4.5V costs 6.7× less per token, which makes it the better buy when Claude Sonnet 4.6's lead doesn't matter for your workload.
Which is cheaper, Claude Sonnet 4.6 or GLM-4.5V?
GLM-4.5V is cheaper. It lists at $0.60 per million input tokens and $1.80 per million output tokens; Claude Sonnet 4.6 lists at $3 and $15.
Is Claude Sonnet 4.6 or GLM-4.5V better for coding?
Claude Sonnet 4.6 scores higher on coding benchmarks: 46.3 versus 39.5 in the Noometry coding category.
Which has the bigger context window?
Claude Sonnet 4.6 does, with 1M tokens against 64K.
How many benchmarks do Claude Sonnet 4.6 and GLM-4.5V share?
14 benchmarks have published results for both models. Claude Sonnet 4.6 has 57 scored results on Noometry and GLM-4.5V has 15.