Model comparison
Claude Sonnet 5 vs GLM-4.6
Claude Sonnet 5 is the stronger model overall, scoring 54.6 to 41.4 on the Noometry Index. GLM-4.6 costs 4.0× less per token, which makes it the better buy when Claude Sonnet 5's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
Summary
- They share 22 benchmarks with published results for both. Claude Sonnet 5 scores higher in 9 categories and GLM-4.6 in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Claude Sonnet 5 leads 66.2 to 39.1.
- The biggest single-benchmark swing is SciCode: 54.3% for Claude Sonnet 5 and 38.4% for GLM-4.6.
- GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $2 / $10 for Claude Sonnet 5.
- Claude Sonnet 5 accepts more context: 1M tokens versus 205K.
- GLM-4.6 has downloadable open weights; the other is API-only.
Side by side
| Claude Sonnet 5 | GLM-4.6 | |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 54.6 | 41.4 |
| Released | 2026-06-29 | 2025-09-30 |
| Weights | Proprietary | Open |
| Context window | 1M | 205K |
| Max output | 128K | 131K |
| Input $ / M tokens | $2 | $0.60 |
| Output $ / M tokens | $10 | $2.20 |
| Results tracked | 51 | 29 |
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Category by category
Coding Claude Sonnet 5 leads
Claude Sonnet 5: 55.5 (#26), GLM-4.6: 40.1 (#148)
| Benchmark | Claude Sonnet 5 | GLM-4.6 |
|---|---|---|
| LMArena WebDev | 1541 | 1340 |
| SciCode | 54.3% | 38.4% |
| LMArena Coding | 1483 | 1449 |
| ALE-Bench | 1,463 | 340.82 |
| DeepSWE | 53.8% | — |
| FrontierCode | 42.7% | — |
| SWE-bench Verified (bash only) | — | 55.4% |
| CursorBench | 34.1% | — |
| GSO | 37.3% | — |
| WeirdML | 68.8% | — |
Agentic & Tool Use Claude Sonnet 5 leads
Claude Sonnet 5: 42.8 (#18), GLM-4.6: 32.3 (#66)
| Benchmark | Claude Sonnet 5 | GLM-4.6 |
|---|---|---|
| Terminal-Bench | — | 24.5% |
| APEX-Agents | 54.5% | — |
| Berkeley Function Calling Leaderboard | — | 72.4% |
| GBAEval | 65.3% | — |
| LMArena Search | 1194 | — |
| Vending-Bench 2 | 6,378 | — |
Reasoning Claude Sonnet 5 leads
Claude Sonnet 5: 49.1 (#39), GLM-4.6: 23.7 (#172)
| Benchmark | Claude Sonnet 5 | GLM-4.6 |
|---|---|---|
| CritPt | 16.9% | 1.1% |
| LMArena Hard Prompts | 1461 | 1440 |
| SimpleBench | 60.6% | — |
| Kagi LLM Benchmark | — | 47.4% |
| NYT Connections (extended) | 75.1% | — |
| Chess Puzzles | 35% | — |
| Mystery Game Puzzles | 35% | — |
| DTBench | 92.5% | — |
| LMCA | 50% | — |
| Surface Evolver Bench | 60% | — |
| Bench to the Future 3 | 0.14 | — |
| Epoch Capabilities Index | 156.21 | — |
| ForecastBench | 61.1 | — |
Math Claude Sonnet 5 leads
Claude Sonnet 5: 66.2 (#27), GLM-4.6: 39.1 (#111)
| Benchmark | Claude Sonnet 5 | GLM-4.6 |
|---|---|---|
| LMArena Math | 1467 | 1432 |
| FrontierMath (Tiers 1-3) | 65.6% | — |
| FrontierMath Tier 4 | 29.3% | — |
| OTIS Mock AIME 2024-2025 | 94.7% | — |
| ProofBench | 77% | — |
| FrontierMath (Feb 2025 set) | — | 3.8% |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge Claude Sonnet 5 leads
Claude Sonnet 5: 55.6 (#47), GLM-4.6: 40.2 (#124)
| Benchmark | Claude Sonnet 5 | GLM-4.6 |
|---|---|---|
| LMArena Expert | 1490 | 1431 |
| GPQA Diamond | 90.5% | — |
| SimpleQA Verified | 33.7% | — |
| Vectara Hallucination Rate | — | 9.5% |
Multimodal Not comparable
Claude Sonnet 5: 42.4 (#31), GLM-4.6: —
| Benchmark | Claude Sonnet 5 | GLM-4.6 |
|---|---|---|
| LMArena Vision | 1274 | — |
| Blueprint-Bench 2 | 24.9% | — |
| LMArena Document | 1466 | — |
Multilingual Too close to call
Claude Sonnet 5: 53.8 (#55), GLM-4.6: 53.5 (#66)
| Benchmark | Claude Sonnet 5 | GLM-4.6 |
|---|---|---|
| LMArena Non-English | 1431 | 1426 |
| LMArena Chinese | 1477 | 1499 |
| LMArena French | 1460 | 1459 |
| LMArena German | 1440 | 1447 |
| LMArena Japanese | 1422 | 1393 |
| LMArena Korean | 1411 | 1400 |
| LMArena Russian | 1451 | 1419 |
| LMArena Spanish | 1437 | 1436 |
Instruction Following Claude Sonnet 5 leads
Claude Sonnet 5: 76.3 (#41), GLM-4.6: 74.3 (#98)
| Benchmark | Claude Sonnet 5 | GLM-4.6 |
|---|---|---|
| LMArena Instruction Following | 1452 | 1410 |
Long Context Claude Sonnet 5 leads
Claude Sonnet 5: 44.8 (#55), GLM-4.6: 43.4 (#94)
| Benchmark | Claude Sonnet 5 | GLM-4.6 |
|---|---|---|
| LMArena Longer Query | 1463 | 1422 |
Writing & Preference Claude Sonnet 5 leads
Claude Sonnet 5: 69.2 (#25), GLM-4.6: 61.1 (#90)
| Benchmark | Claude Sonnet 5 | GLM-4.6 |
|---|---|---|
| LMArena Text | 1442 | 1440 |
| LMArena Creative Writing | 1416 | 1411 |
| EQ-Bench Creative Writing | 1794 | 1411 |
| LMArena Multi-Turn | 1454 | 1427 |
| EQ-Bench 4 | 1236 | — |
Frequently asked questions
Is Claude Sonnet 5 better than GLM-4.6?
Claude Sonnet 5 is the stronger model overall, scoring 54.6 to 41.4 on the Noometry Index. GLM-4.6 costs 4.0× less per token, which makes it the better buy when Claude Sonnet 5's lead doesn't matter for your workload.
Which is cheaper, Claude Sonnet 5 or GLM-4.6?
GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Claude Sonnet 5 lists at $2 and $10.
Is Claude Sonnet 5 or GLM-4.6 better for coding?
Claude Sonnet 5 scores higher on coding benchmarks: 55.5 versus 40.1 in the Noometry coding category.
Which has the bigger context window?
Claude Sonnet 5 does, with 1M tokens against 205K.
How many benchmarks do Claude Sonnet 5 and GLM-4.6 share?
22 benchmarks have published results for both models. Claude Sonnet 5 has 51 scored results on Noometry and GLM-4.6 has 29.