Model comparison
GLM-4.6V vs Kimi K2 (Jul 2025)
GLM-4.6V and Kimi K2 (Jul 2025) score almost the same on the Noometry Index (41.3 vs 41.2), so choose on price, context window or the category you care about most.
Last verified . 11 shared benchmarks.
Summary
- They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 4 categories and Kimi K2 (Jul 2025) in 3 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Kimi K2 (Jul 2025) leads 62.3 to 56.6.
- GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.57 / $2.30 for Kimi K2 (Jul 2025).
- Kimi K2 (Jul 2025) accepts more context: 262K tokens versus 128K.
Side by side
| GLM-4.6V | Kimi K2 (Jul 2025) | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Moonshot AI |
| Noometry Index | 41.3 | 41.2 |
| Released | 2025-12-08 | 2025-07-12 |
| Weights | Open | Open |
| Context window | 128K | 262K |
| Max output | 33K | 262K |
| Input $ / M tokens | $0.30 | $0.57 |
| Output $ / M tokens | $0.90 | $2.30 |
| Results tracked | 12 | 42 |
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Category by category
Coding Kimi K2 (Jul 2025) leads
GLM-4.6V: 40.9 (#128), Kimi K2 (Jul 2025): 42.4 (#102)
| Benchmark | GLM-4.6V | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Coding | 1390 | 1399 |
| SWE-bench Verified (bash only) | — | 63.4% |
| Aider Polyglot | — | 59.1% |
| GSO | — | 4.9% |
| WeirdML | — | 42.8% |
| ALE-Bench | — | 597.5 |
Agentic & Tool Use Not comparable
GLM-4.6V: —, Kimi K2 (Jul 2025): 32.4 (#64)
| Benchmark | GLM-4.6V | Kimi K2 (Jul 2025) |
|---|---|---|
| Terminal-Bench | — | 35.7% |
| Berkeley Function Calling Leaderboard | — | 59.1% |
| METR Time Horizons | — | 59.2% |
Reasoning GLM-4.6V leads
GLM-4.6V: 27.6 (#115), Kimi K2 (Jul 2025): 23.3 (#179)
| Benchmark | GLM-4.6V | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Hard Prompts | 1368 | 1384 |
| SimpleBench | — | 26.3% |
| Kagi LLM Benchmark | — | 64.4% |
| Epoch Capabilities Index | — | 146.01 |
| ForecastBench | — | 60.2 |
Math Not comparable
GLM-4.6V: —, Kimi K2 (Jul 2025): 42.7 (#83)
| Benchmark | GLM-4.6V | Kimi K2 (Jul 2025) |
|---|---|---|
| Omni-MATH | — | 65.4% |
| LMArena Math | — | 1397 |
| FrontierMath (Feb 2025 set) | — | 21.4% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Too close to call
GLM-4.6V: 38.0 (#149), Kimi K2 (Jul 2025): 37.3 (#157)
| Benchmark | GLM-4.6V | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Expert | 1371 | 1365 |
| MMLU-Pro | — | 81.9% |
| Confabulations | — | 20.4% |
| Vectara Hallucination Rate | — | 17.9% |
| GPQA (HELM) | — | 65.3% |
Multimodal Not comparable
GLM-4.6V: 34.8 (#90), Kimi K2 (Jul 2025): —
| Benchmark | GLM-4.6V | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Vision | 1164 | — |
Multilingual Too close to call
GLM-4.6V: 48.6 (#141), Kimi K2 (Jul 2025): 49.6 (#130)
| Benchmark | GLM-4.6V | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Non-English | 1359 | 1372 |
| LMArena Chinese | 1425 | 1415 |
| LMArena Russian | 1340 | 1385 |
| LMArena French | — | 1379 |
| LMArena German | — | 1387 |
| LMArena Japanese | — | 1349 |
| LMArena Korean | — | 1325 |
| LMArena Spanish | — | 1386 |
Instruction Following Too close to call
GLM-4.6V: 71.4 (#151), Kimi K2 (Jul 2025): 71.1 (#156)
| Benchmark | GLM-4.6V | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Instruction Following | 1352 | 1348 |
| IFEval | — | 85% |
Long Context Too close to call
GLM-4.6V: 41.3 (#143), Kimi K2 (Jul 2025): 41.2 (#145)
| Benchmark | GLM-4.6V | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Longer Query | 1358 | 1353 |
| Fiction.LiveBench | — | 66.7% |
| CL-bench | — | 17.6% |
Writing & Preference Kimi K2 (Jul 2025) leads
GLM-4.6V: 56.6 (#137), Kimi K2 (Jul 2025): 62.3 (#78)
| Benchmark | GLM-4.6V | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Text | 1377 | 1380 |
| LMArena Creative Writing | 1347 | 1350 |
| LMArena Multi-Turn | 1360 | 1371 |
| Short-Story Creative Writing | — | 85.6% |
| EQ-Bench Creative Writing | — | 1666 |
| WildBench | — | 86.2% |
Frequently asked questions
Is GLM-4.6V better than Kimi K2 (Jul 2025)?
GLM-4.6V and Kimi K2 (Jul 2025) score almost the same on the Noometry Index (41.3 vs 41.2), so choose on price, context window or the category you care about most.
Which is cheaper, GLM-4.6V or Kimi K2 (Jul 2025)?
GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Kimi K2 (Jul 2025) lists at $0.57 and $2.30.
Is GLM-4.6V or Kimi K2 (Jul 2025) better for coding?
Kimi K2 (Jul 2025) scores higher on coding benchmarks: 42.4 versus 40.9 in the Noometry coding category.
Which has the bigger context window?
Kimi K2 (Jul 2025) does, with 262K tokens against 128K.
How many benchmarks do GLM-4.6V and Kimi K2 (Jul 2025) share?
11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Kimi K2 (Jul 2025) has 42.