Model comparison
GLM-4.5 vs Kimi K2.7 Code
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 42.0 on the Noometry Index. GLM-4.5 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 . 2 shared benchmarks.
Summary
- They share 2 benchmarks with published results for both. GLM-4.5 scores higher in 0 categories and Kimi K2.7 Code in 4 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Kimi K2.7 Code leads 53.5 to 35.9.
- The biggest single-benchmark swing is WeirdML: 40.6% for GLM-4.5 and 54.1% for Kimi K2.7 Code.
- GLM-4.5 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 131K.
Side by side
| GLM-4.5 | Kimi K2.7 Code | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Moonshot AI |
| Noometry Index | 42.0 | 43.3 |
| Released | 2025-07-27 | 2026-06-12 |
| Weights | Open | Open |
| Context window | 131K | 262K |
| Max output | 98K | 262K |
| Input $ / M tokens | $0.60 | $0.95 |
| Output $ / M tokens | $2.20 | $4 |
| Results tracked | 27 | 19 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Kimi K2.7 Code leads
GLM-4.5: 41.4 (#125), Kimi K2.7 Code: 42.9 (#95)
| Benchmark | GLM-4.5 | Kimi K2.7 Code |
|---|---|---|
| WeirdML | 40.6% | 54.1% |
| ALE-Bench | 344.82 | 886.23 |
| DeepSWE | — | 30.5% |
| FrontierCode | — | 30.1% |
| SWE-bench Verified (bash only) | 54.2% | — |
| LMArena WebDev | — | 1473 |
| SciCode | — | 47.5% |
| LMArena Coding | 1434 | — |
| AlgoTune | 1.52 | — |
Agentic & Tool Use Not comparable
GLM-4.5: —, Kimi K2.7 Code: 24.0 (#122)
| Benchmark | GLM-4.5 | Kimi K2.7 Code |
|---|---|---|
| APEX-Agents | — | 37.6% |
| GBAEval | — | 0.9% |
| Vending-Bench 2 | — | 5,083 |
Reasoning Kimi K2.7 Code leads
GLM-4.5: 28.6 (#100), Kimi K2.7 Code: 39.0 (#61)
| Benchmark | GLM-4.5 | Kimi K2.7 Code |
|---|---|---|
| SimpleBench | — | 57.9% |
| Kagi LLM Benchmark | 57.9% | — |
| CritPt | — | 10% |
| Chess Puzzles | — | 21% |
| LMArena Hard Prompts | 1429 | — |
| Surface Evolver Bench | — | 48.8% |
| Epoch Capabilities Index | — | 149.97 |
Math Kimi K2.7 Code leads
GLM-4.5: 39.0 (#116), Kimi K2.7 Code: 52.9 (#48)
| Benchmark | GLM-4.5 | Kimi K2.7 Code |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 54% |
| FrontierMath Tier 4 | — | 12.2% |
| OTIS Mock AIME 2024-2025 | — | 95.6% |
| LMArena Math | 1427 | — |
Knowledge Kimi K2.7 Code leads
GLM-4.5: 35.9 (#179), Kimi K2.7 Code: 53.5 (#57)
| Benchmark | GLM-4.5 | Kimi K2.7 Code |
|---|---|---|
| GPQA Diamond | — | 87.9% |
| Humanity's Last Exam | 8.3% | — |
| SimpleQA Verified | — | 36.5% |
| Confabulations | 11.3% | — |
| LMArena Expert | 1433 | — |
Multilingual Not comparable
GLM-4.5: 52.8 (#77), Kimi K2.7 Code: —
| Benchmark | GLM-4.5 | Kimi K2.7 Code |
|---|---|---|
| LMArena Non-English | 1417 | — |
| LMArena Chinese | 1465 | — |
| LMArena French | 1418 | — |
| LMArena German | 1407 | — |
| LMArena Japanese | 1415 | — |
| LMArena Korean | 1380 | — |
| LMArena Russian | 1414 | — |
| LMArena Spanish | 1454 | — |
Instruction Following Not comparable
GLM-4.5: 74.1 (#104), Kimi K2.7 Code: —
| Benchmark | GLM-4.5 | Kimi K2.7 Code |
|---|---|---|
| LMArena Instruction Following | 1404 | — |
Long Context Not comparable
GLM-4.5: 38.2 (#201), Kimi K2.7 Code: —
| Benchmark | GLM-4.5 | Kimi K2.7 Code |
|---|---|---|
| Fiction.LiveBench | 58.3% | — |
| LMArena Longer Query | 1412 | — |
Writing & Preference Not comparable
GLM-4.5: 57.5 (#127), Kimi K2.7 Code: —
| Benchmark | GLM-4.5 | Kimi K2.7 Code |
|---|---|---|
| LMArena Text | 1430 | — |
| LMArena Creative Writing | 1395 | — |
| Short-Story Creative Writing | 73.4% | — |
| EQ-Bench Creative Writing | 1343 | — |
| LMArena Multi-Turn | 1415 | — |
Frequently asked questions
Is GLM-4.5 better than Kimi K2.7 Code?
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 42.0 on the Noometry Index. GLM-4.5 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-4.5 or Kimi K2.7 Code?
GLM-4.5 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-4.5 or Kimi K2.7 Code better for coding?
Kimi K2.7 Code scores higher on coding benchmarks: 42.9 versus 41.4 in the Noometry coding category.
Which has the bigger context window?
Kimi K2.7 Code does, with 262K tokens against 131K.
How many benchmarks do GLM-4.5 and Kimi K2.7 Code share?
2 benchmarks have published results for both models. GLM-4.5 has 27 scored results on Noometry and Kimi K2.7 Code has 19.