Model comparison
GLM-4.6 vs Kimi K2 (Jul 2025)
GLM-4.6 and Kimi K2 (Jul 2025) score almost the same on the Noometry Index (41.4 vs 41.2), so choose on price, context window or the category you care about most.
Last verified . 26 shared benchmarks.
Summary
- They share 26 benchmarks with published results for both. GLM-4.6 scores higher in 5 categories and Kimi K2 (Jul 2025) in 4 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in multilingual, where GLM-4.6 leads 53.5 to 49.6.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 47.4% for GLM-4.6 and 64.4% for Kimi K2 (Jul 2025).
- Both cost about the same: $0.60 input and $2.20 output per million tokens.
- Kimi K2 (Jul 2025) accepts more context: 262K tokens versus 205K.
Side by side
| GLM-4.6 | Kimi K2 (Jul 2025) | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Moonshot AI |
| Noometry Index | 41.4 | 41.2 |
| Released | 2025-09-30 | 2025-07-12 |
| Weights | Open | Open |
| Context window | 205K | 262K |
| Max output | 131K | 262K |
| Input $ / M tokens | $0.60 | $0.57 |
| Output $ / M tokens | $2.20 | $2.30 |
| Results tracked | 29 | 42 |
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Category by category
Coding Kimi K2 (Jul 2025) leads
GLM-4.6: 40.1 (#148), Kimi K2 (Jul 2025): 42.4 (#102)
| Benchmark | GLM-4.6 | Kimi K2 (Jul 2025) |
|---|---|---|
| SWE-bench Verified (bash only) | 55.4% | 63.4% |
| LMArena Coding | 1449 | 1399 |
| ALE-Bench | 340.82 | 597.5 |
| Aider Polyglot | — | 59.1% |
| LMArena WebDev | 1340 | — |
| SciCode | 38.4% | — |
| GSO | — | 4.9% |
| WeirdML | — | 42.8% |
Agentic & Tool Use Too close to call
GLM-4.6: 32.3 (#66), Kimi K2 (Jul 2025): 32.4 (#64)
| Benchmark | GLM-4.6 | Kimi K2 (Jul 2025) |
|---|---|---|
| Terminal-Bench | 24.5% | 35.7% |
| Berkeley Function Calling Leaderboard | 72.4% | 59.1% |
| METR Time Horizons | — | 59.2% |
Reasoning Too close to call
GLM-4.6: 23.7 (#172), Kimi K2 (Jul 2025): 23.3 (#179)
| Benchmark | GLM-4.6 | Kimi K2 (Jul 2025) |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | 64.4% |
| LMArena Hard Prompts | 1440 | 1384 |
| SimpleBench | — | 26.3% |
| CritPt | 1.1% | — |
| Epoch Capabilities Index | — | 146.01 |
| ForecastBench | — | 60.2 |
Math Kimi K2 (Jul 2025) leads
GLM-4.6: 39.1 (#111), Kimi K2 (Jul 2025): 42.7 (#83)
| Benchmark | GLM-4.6 | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Math | 1432 | 1397 |
| FrontierMath (Feb 2025 set) | 3.8% | 21.4% |
| FrontierMath Tier 4 (v1) | 2.1% | 0% |
| Omni-MATH | — | 65.4% |
Knowledge GLM-4.6 leads
GLM-4.6: 40.2 (#124), Kimi K2 (Jul 2025): 37.3 (#157)
| Benchmark | GLM-4.6 | Kimi K2 (Jul 2025) |
|---|---|---|
| Vectara Hallucination Rate | 9.5% | 17.9% |
| LMArena Expert | 1431 | 1365 |
| MMLU-Pro | — | 81.9% |
| Confabulations | — | 20.4% |
| GPQA (HELM) | — | 65.3% |
Multilingual GLM-4.6 leads
GLM-4.6: 53.5 (#66), Kimi K2 (Jul 2025): 49.6 (#130)
| Benchmark | GLM-4.6 | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Non-English | 1426 | 1372 |
| LMArena Chinese | 1499 | 1415 |
| LMArena French | 1459 | 1379 |
| LMArena German | 1447 | 1387 |
| LMArena Japanese | 1393 | 1349 |
| LMArena Korean | 1400 | 1325 |
| LMArena Russian | 1419 | 1385 |
| LMArena Spanish | 1436 | 1386 |
Instruction Following GLM-4.6 leads
GLM-4.6: 74.3 (#98), Kimi K2 (Jul 2025): 71.1 (#156)
| Benchmark | GLM-4.6 | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Instruction Following | 1410 | 1348 |
| IFEval | — | 85% |
Long Context GLM-4.6 leads
GLM-4.6: 43.4 (#94), Kimi K2 (Jul 2025): 41.2 (#145)
| Benchmark | GLM-4.6 | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Longer Query | 1422 | 1353 |
| Fiction.LiveBench | — | 66.7% |
| CL-bench | — | 17.6% |
Writing & Preference Kimi K2 (Jul 2025) leads
GLM-4.6: 61.1 (#90), Kimi K2 (Jul 2025): 62.3 (#78)
| Benchmark | GLM-4.6 | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Text | 1440 | 1380 |
| LMArena Creative Writing | 1411 | 1350 |
| EQ-Bench Creative Writing | 1411 | 1666 |
| LMArena Multi-Turn | 1427 | 1371 |
| Short-Story Creative Writing | — | 85.6% |
| WildBench | — | 86.2% |
Frequently asked questions
Is GLM-4.6 better than Kimi K2 (Jul 2025)?
GLM-4.6 and Kimi K2 (Jul 2025) score almost the same on the Noometry Index (41.4 vs 41.2), so choose on price, context window or the category you care about most.
Which is cheaper, GLM-4.6 or Kimi K2 (Jul 2025)?
GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Kimi K2 (Jul 2025) lists at $0.57 and $2.30.
Is GLM-4.6 or Kimi K2 (Jul 2025) better for coding?
Kimi K2 (Jul 2025) scores higher on coding benchmarks: 42.4 versus 40.1 in the Noometry coding category.
Which has the bigger context window?
Kimi K2 (Jul 2025) does, with 262K tokens against 205K.
How many benchmarks do GLM-4.6 and Kimi K2 (Jul 2025) share?
26 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Kimi K2 (Jul 2025) has 42.