Model comparison
GLM-5.3 vs Kimi K2.6
GLM-5.3 is the stronger model overall, scoring 54.6 to 47.5 on the Noometry Index.
Last verified . 36 shared benchmarks.
Summary
- They share 36 benchmarks with published results for both. GLM-5.3 scores higher in 9 categories and Kimi K2.6 in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where GLM-5.3 leads 36.3 to 21.8.
- The biggest single-benchmark swing is ProofBench: 49% for GLM-5.3 and 16% for Kimi K2.6.
- Kimi K2.6 is cheaper at $0.95 / $4 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.
- GLM-5.3 accepts more context: 1M tokens versus 262K.
Side by side
| GLM-5.3 | Kimi K2.6 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Moonshot AI |
| Noometry Index | 54.6 | 47.5 |
| Released | 2026-08-14 | 2026-04-20 |
| Weights | Open | Open |
| Context window | 1M | 262K |
| Max output | 131K | 262K |
| Input $ / M tokens | $1.40 | $0.95 |
| Output $ / M tokens | $4.40 | $4 |
| Results tracked | 41 | 51 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.0 (#12), Kimi K2.6: 50.5 (#41)
| Benchmark | GLM-5.3 | Kimi K2.6 |
|---|---|---|
| LMArena WebDev | 1623 | 1509 |
| SciCode | 59% | 53.5% |
| WeirdML | 75.4% | 55.9% |
| LMArena Coding | 1499 | 1488 |
| ALE-Bench | 1,317 | 1,093 |
| SWE-bench Verified | — | 76.7% |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| FrontierSWE | 30.2% | — |
Agentic & Tool Use GLM-5.3 leads
GLM-5.3: 36.3 (#34), Kimi K2.6: 21.8 (#128)
| Benchmark | GLM-5.3 | Kimi K2.6 |
|---|---|---|
| Vending-Bench 2 | 8,164 | 6,205 |
| APEX-Agents | 56.6% | — |
| OSWorld 2.0 | — | 4.6% |
| ExploitBench | — | 18.4% |
| GBAEval | — | 0.9% |
| GDP.pdf | — | 12% |
Reasoning GLM-5.3 leads
GLM-5.3: 45.2 (#42), Kimi K2.6: 39.6 (#51)
| Benchmark | GLM-5.3 | Kimi K2.6 |
|---|---|---|
| NYT Connections (extended) | 74.2% | 87.2% |
| CritPt | 19.1% | 8% |
| Chess Puzzles | 21% | 26% |
| LMArena Hard Prompts | 1491 | 1470 |
| Mystery Game Puzzles | 33% | 18% |
| DTBench | 87.7% | 90.9% |
| LMCA | 55.5% | 37.3% |
| Epoch Capabilities Index | 155.56 | 151.03 |
| EBR-Bench | — | 2.4% |
| Bench to the Future 3 | 0.15 | — |
Math GLM-5.3 leads
GLM-5.3: 62.1 (#27), Kimi K2.6: 56.8 (#34)
| Benchmark | GLM-5.3 | Kimi K2.6 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 68.8% | 57.2% |
| FrontierMath Tier 4 | 29.3% | 25.6% |
| OTIS Mock AIME 2024-2025 | 91.1% | 96.1% |
| ProofBench | 49% | 16% |
| LMArena Math | 1493 | 1475 |
| MathArena Final-Answer Competitions | — | 72.9% |
| FrontierMath (Feb 2025 set) | — | 39% |
| FrontierMath Tier 4 (v1) | — | 14.6% |
Knowledge GLM-5.3 leads
GLM-5.3: 58.3 (#34), Kimi K2.6: 53.9 (#48)
| Benchmark | GLM-5.3 | Kimi K2.6 |
|---|---|---|
| GPQA Diamond | 90.9% | 90.8% |
| SimpleQA Verified | 41% | 34.9% |
| LMArena Expert | 1512 | 1491 |
| Vectara Hallucination Rate | — | 10.8% |
Multimodal Not comparable
GLM-5.3: —, Kimi K2.6: 31.8 (#95)
| Benchmark | GLM-5.3 | Kimi K2.6 |
|---|---|---|
| LMArena Vision | — | 1282 |
| Blueprint-Bench 2 | — | 3.9% |
| Furniture Assembly | — | 21.7% |
| LMArena Document | — | 1451 |
Multilingual Too close to call
GLM-5.3: 55.9 (#27), Kimi K2.6: 55.1 (#35)
| Benchmark | GLM-5.3 | Kimi K2.6 |
|---|---|---|
| LMArena Non-English | 1457 | 1446 |
| LMArena Chinese | 1523 | 1521 |
| LMArena French | 1499 | 1471 |
| LMArena German | 1505 | 1449 |
| LMArena Japanese | 1438 | 1442 |
| LMArena Korean | 1478 | 1428 |
| LMArena Russian | 1460 | 1446 |
| LMArena Spanish | 1444 | 1463 |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.8 (#15), Kimi K2.6: 76.5 (#40)
| Benchmark | GLM-5.3 | Kimi K2.6 |
|---|---|---|
| LMArena Instruction Following | 1481 | 1451 |
Long Context Too close to call
GLM-5.3: 45.5 (#32), Kimi K2.6: 44.9 (#50)
| Benchmark | GLM-5.3 | Kimi K2.6 |
|---|---|---|
| LMArena Longer Query | 1485 | 1468 |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.9 (#6), Kimi K2.6: 68.7 (#25)
| Benchmark | GLM-5.3 | Kimi K2.6 |
|---|---|---|
| LMArena Text | 1473 | 1455 |
| LMArena Creative Writing | 1459 | 1434 |
| EQ-Bench Creative Writing | 2075 | 1725 |
| LMArena Multi-Turn | 1473 | 1453 |
| EQ-Bench 4 | — | 1202 |
Frequently asked questions
Is GLM-5.3 better than Kimi K2.6?
GLM-5.3 is the stronger model overall, scoring 54.6 to 47.5 on the Noometry Index.
Which is cheaper, GLM-5.3 or Kimi K2.6?
Kimi K2.6 is cheaper. It lists at $0.95 per million input tokens and $4 per million output tokens; GLM-5.3 lists at $1.40 and $4.40.
Is GLM-5.3 or Kimi K2.6 better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.0 versus 50.5 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3 does, with 1M tokens against 262K.
How many benchmarks do GLM-5.3 and Kimi K2.6 share?
36 benchmarks have published results for both models. GLM-5.3 has 41 scored results on Noometry and Kimi K2.6 has 51.