Model comparison
GLM-5.2 vs Kimi K2 (Jul 2025)
GLM-5.2 is the stronger model overall, scoring 51.1 to 41.2 on the Noometry Index. Kimi K2 (Jul 2025) costs 2.1× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Last verified . 23 shared benchmarks.
Summary
- They share 23 benchmarks with published results for both. GLM-5.2 scores higher in 9 categories and Kimi K2 (Jul 2025) in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.2 leads 57.1 to 37.3.
- The biggest single-benchmark swing is SimpleBench: 58.8% for GLM-5.2 and 26.3% for Kimi K2 (Jul 2025).
- Kimi K2 (Jul 2025) is cheaper at $0.57 / $2.30 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 262K.
Side by side
| GLM-5.2 | Kimi K2 (Jul 2025) | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Moonshot AI |
| Noometry Index | 51.1 | 41.2 |
| Released | 2026-06-13 | 2025-07-12 |
| Weights | Open | Open |
| Context window | 1M | 262K |
| Max output | 131K | 262K |
| Input $ / M tokens | $1.40 | $0.57 |
| Output $ / M tokens | $4.40 | $2.30 |
| Results tracked | 51 | 42 |
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Category by category
Coding GLM-5.2 leads
GLM-5.2: 51.3 (#41), Kimi K2 (Jul 2025): 42.4 (#102)
| Benchmark | GLM-5.2 | Kimi K2 (Jul 2025) |
|---|---|---|
| WeirdML | 70.1% | 42.8% |
| LMArena Coding | 1485 | 1399 |
| ALE-Bench | 1,047 | 597.5 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| SWE-bench Verified (bash only) | — | 63.4% |
| Aider Polyglot | — | 59.1% |
| LMArena WebDev | 1603 | — |
| SciCode | 50.5% | — |
| GSO | — | 4.9% |
Agentic & Tool Use Too close to call
GLM-5.2: 32.4 (#63), Kimi K2 (Jul 2025): 32.4 (#64)
| Benchmark | GLM-5.2 | Kimi K2 (Jul 2025) |
|---|---|---|
| Terminal-Bench | — | 35.7% |
| APEX-Agents | 45.2% | — |
| Berkeley Function Calling Leaderboard | — | 59.1% |
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |
| METR Time Horizons | — | 59.2% |
| Vending-Bench 2 | 8,314 | — |
Reasoning GLM-5.2 leads
GLM-5.2: 42.3 (#52), Kimi K2 (Jul 2025): 23.3 (#179)
| Benchmark | GLM-5.2 | Kimi K2 (Jul 2025) |
|---|---|---|
| SimpleBench | 58.8% | 26.3% |
| Kagi LLM Benchmark | 62.6% | 64.4% |
| LMArena Hard Prompts | 1480 | 1384 |
| Epoch Capabilities Index | 151.78 | 146.01 |
| ARC-AGI-2 | 22.8% | — |
| NYT Connections (extended) | 74.3% | — |
| ARC-AGI-1 | 77% | — |
| CritPt | 20.9% | — |
| Chess Puzzles | 21% | — |
| EBR-Bench | 9.5% | — |
| Mystery Game Puzzles | 19% | — |
| DTBench | 93.6% | — |
| LMCA | 45.8% | — |
| Surface Evolver Bench | 55.6% | — |
| ForecastBench | — | 60.2 |
Math GLM-5.2 leads
GLM-5.2: 55.7 (#43), Kimi K2 (Jul 2025): 42.7 (#83)
| Benchmark | GLM-5.2 | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Math | 1482 | 1397 |
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| OTIS Mock AIME 2024-2025 | 86.4% | — |
| ProofBench | 35% | — |
| Omni-MATH | — | 65.4% |
| FrontierMath (Feb 2025 set) | — | 21.4% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge GLM-5.2 leads
GLM-5.2: 57.1 (#40), Kimi K2 (Jul 2025): 37.3 (#157)
| Benchmark | GLM-5.2 | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Expert | 1486 | 1365 |
| GPQA Diamond | 91.9% | — |
| SimpleQA Verified | 34.2% | — |
| MMLU-Pro | — | 81.9% |
| Confabulations | — | 20.4% |
| Vectara Hallucination Rate | — | 17.9% |
| GPQA (HELM) | — | 65.3% |
Multilingual GLM-5.2 leads
GLM-5.2: 55.8 (#26), Kimi K2 (Jul 2025): 49.6 (#130)
| Benchmark | GLM-5.2 | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Non-English | 1459 | 1372 |
| LMArena Chinese | 1519 | 1415 |
| LMArena French | 1479 | 1379 |
| LMArena German | 1468 | 1387 |
| LMArena Japanese | 1451 | 1349 |
| LMArena Korean | 1445 | 1325 |
| LMArena Russian | 1466 | 1385 |
| LMArena Spanish | 1477 | 1386 |
Instruction Following GLM-5.2 leads
GLM-5.2: 76.9 (#34), Kimi K2 (Jul 2025): 71.1 (#156)
| Benchmark | GLM-5.2 | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Instruction Following | 1465 | 1348 |
| IFEval | — | 85% |
Long Context GLM-5.2 leads
GLM-5.2: 45.3 (#43), Kimi K2 (Jul 2025): 41.2 (#145)
| Benchmark | GLM-5.2 | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Longer Query | 1479 | 1353 |
| Fiction.LiveBench | — | 66.7% |
| CL-bench | — | 17.6% |
Writing & Preference GLM-5.2 leads
GLM-5.2: 70.4 (#21), Kimi K2 (Jul 2025): 62.3 (#78)
| Benchmark | GLM-5.2 | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Text | 1470 | 1380 |
| LMArena Creative Writing | 1462 | 1350 |
| EQ-Bench Creative Writing | 1757 | 1666 |
| LMArena Multi-Turn | 1469 | 1371 |
| Short-Story Creative Writing | — | 85.6% |
| WildBench | — | 86.2% |
| EQ-Bench 4 | 1222 | — |
Frequently asked questions
Is GLM-5.2 better than Kimi K2 (Jul 2025)?
GLM-5.2 is the stronger model overall, scoring 51.1 to 41.2 on the Noometry Index. Kimi K2 (Jul 2025) costs 2.1× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Which is cheaper, GLM-5.2 or Kimi K2 (Jul 2025)?
Kimi K2 (Jul 2025) is cheaper. It lists at $0.57 per million input tokens and $2.30 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.
Is GLM-5.2 or Kimi K2 (Jul 2025) better for coding?
GLM-5.2 scores higher on coding benchmarks: 51.3 versus 42.4 in the Noometry coding category.
Which has the bigger context window?
GLM-5.2 does, with 1M tokens against 262K.
How many benchmarks do GLM-5.2 and Kimi K2 (Jul 2025) share?
23 benchmarks have published results for both models. GLM-5.2 has 51 scored results on Noometry and Kimi K2 (Jul 2025) has 42.