Model comparison
GLM-4.6 vs Kimi K2.7 Code
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 41.4 on the Noometry Index. GLM-4.6 costs 1.7× less per token, which makes it the better buy when Kimi K2.7 Code's lead doesn't matter for your workload.
Last verified . 4 shared benchmarks.
Summary
- They share 4 benchmarks with published results for both. GLM-4.6 scores higher in 1 category and Kimi K2.7 Code in 4 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Kimi K2.7 Code leads 39.0 to 23.7.
- The biggest single-benchmark swing is SciCode: 38.4% for GLM-4.6 and 47.5% for Kimi K2.7 Code.
- GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $0.95 / $4 for Kimi K2.7 Code.
- Kimi K2.7 Code accepts more context: 262K tokens versus 205K.
Side by side
| GLM-4.6 | Kimi K2.7 Code | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Moonshot AI |
| Noometry Index | 41.4 | 43.3 |
| Released | 2025-09-30 | 2026-06-12 |
| Weights | Open | Open |
| Context window | 205K | 262K |
| Max output | 131K | 262K |
| Input $ / M tokens | $0.60 | $0.95 |
| Output $ / M tokens | $2.20 | $4 |
| Results tracked | 29 | 19 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Kimi K2.7 Code leads
GLM-4.6: 40.1 (#148), Kimi K2.7 Code: 42.9 (#95)
| Benchmark | GLM-4.6 | Kimi K2.7 Code |
|---|---|---|
| LMArena WebDev | 1340 | 1473 |
| SciCode | 38.4% | 47.5% |
| ALE-Bench | 340.82 | 886.23 |
| DeepSWE | — | 30.5% |
| FrontierCode | — | 30.1% |
| SWE-bench Verified (bash only) | 55.4% | — |
| WeirdML | — | 54.1% |
| LMArena Coding | 1449 | — |
Agentic & Tool Use GLM-4.6 leads
GLM-4.6: 32.3 (#66), Kimi K2.7 Code: 24.0 (#122)
| Benchmark | GLM-4.6 | Kimi K2.7 Code |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| APEX-Agents | — | 37.6% |
| Berkeley Function Calling Leaderboard | 72.4% | — |
| GBAEval | — | 0.9% |
| Vending-Bench 2 | — | 5,083 |
Reasoning Kimi K2.7 Code leads
GLM-4.6: 23.7 (#172), Kimi K2.7 Code: 39.0 (#61)
| Benchmark | GLM-4.6 | Kimi K2.7 Code |
|---|---|---|
| CritPt | 1.1% | 10% |
| SimpleBench | — | 57.9% |
| Kagi LLM Benchmark | 47.4% | — |
| Chess Puzzles | — | 21% |
| LMArena Hard Prompts | 1440 | — |
| Surface Evolver Bench | — | 48.8% |
| Epoch Capabilities Index | — | 149.97 |
Math Kimi K2.7 Code leads
GLM-4.6: 39.1 (#111), Kimi K2.7 Code: 52.9 (#48)
| Benchmark | GLM-4.6 | Kimi K2.7 Code |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 54% |
| FrontierMath Tier 4 | — | 12.2% |
| OTIS Mock AIME 2024-2025 | — | 95.6% |
| LMArena Math | 1432 | — |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Kimi K2.7 Code leads
GLM-4.6: 40.2 (#124), Kimi K2.7 Code: 53.5 (#57)
| Benchmark | GLM-4.6 | Kimi K2.7 Code |
|---|---|---|
| GPQA Diamond | — | 87.9% |
| SimpleQA Verified | — | 36.5% |
| Vectara Hallucination Rate | 9.5% | — |
| LMArena Expert | 1431 | — |
Multilingual Not comparable
GLM-4.6: 53.5 (#66), Kimi K2.7 Code: —
| Benchmark | GLM-4.6 | Kimi K2.7 Code |
|---|---|---|
| LMArena Non-English | 1426 | — |
| LMArena Chinese | 1499 | — |
| LMArena French | 1459 | — |
| LMArena German | 1447 | — |
| LMArena Japanese | 1393 | — |
| LMArena Korean | 1400 | — |
| LMArena Russian | 1419 | — |
| LMArena Spanish | 1436 | — |
Instruction Following Not comparable
GLM-4.6: 74.3 (#98), Kimi K2.7 Code: —
| Benchmark | GLM-4.6 | Kimi K2.7 Code |
|---|---|---|
| LMArena Instruction Following | 1410 | — |
Long Context Not comparable
GLM-4.6: 43.4 (#94), Kimi K2.7 Code: —
| Benchmark | GLM-4.6 | Kimi K2.7 Code |
|---|---|---|
| LMArena Longer Query | 1422 | — |
Writing & Preference Not comparable
GLM-4.6: 61.1 (#90), Kimi K2.7 Code: —
| Benchmark | GLM-4.6 | Kimi K2.7 Code |
|---|---|---|
| LMArena Text | 1440 | — |
| LMArena Creative Writing | 1411 | — |
| EQ-Bench Creative Writing | 1411 | — |
| LMArena Multi-Turn | 1427 | — |
Frequently asked questions
Is GLM-4.6 better than Kimi K2.7 Code?
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 41.4 on the Noometry Index. GLM-4.6 costs 1.7× less per token, which makes it the better buy when Kimi K2.7 Code's lead doesn't matter for your workload.
Which is cheaper, GLM-4.6 or Kimi K2.7 Code?
GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Kimi K2.7 Code lists at $0.95 and $4.
Is GLM-4.6 or Kimi K2.7 Code better for coding?
Kimi K2.7 Code scores higher on coding benchmarks: 42.9 versus 40.1 in the Noometry coding category.
Which has the bigger context window?
Kimi K2.7 Code does, with 262K tokens against 205K.
How many benchmarks do GLM-4.6 and Kimi K2.7 Code share?
4 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Kimi K2.7 Code has 19.