Model comparison
GLM-4.7 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-4.7 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 . 11 shared benchmarks.
Summary
- They share 11 benchmarks with published results for both. GLM-4.7 scores higher in 2 categories and Kimi K2.7 Code in 3 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Kimi K2.7 Code leads 39.0 to 24.3.
- The biggest single-benchmark swing is Chess Puzzles: 6% for GLM-4.7 and 21% for Kimi K2.7 Code.
- GLM-4.7 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-4.7 | Kimi K2.7 Code | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Moonshot AI |
| Noometry Index | 42.0 | 43.3 |
| Released | 2025-12-22 | 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 | 36 | 19 |
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Category by category
Coding GLM-4.7 leads
GLM-4.7: 44.0 (#79), Kimi K2.7 Code: 42.9 (#95)
| Benchmark | GLM-4.7 | Kimi K2.7 Code |
|---|---|---|
| LMArena WebDev | 1435 | 1473 |
| SciCode | 45.1% | 47.5% |
| ALE-Bench | 399.48 | 886.23 |
| DeepSWE | — | 30.5% |
| FrontierCode | — | 30.1% |
| WeirdML | — | 54.1% |
| LMArena Coding | 1454 | — |
Agentic & Tool Use GLM-4.7 leads
GLM-4.7: 26.5 (#103), Kimi K2.7 Code: 24.0 (#122)
| Benchmark | GLM-4.7 | Kimi K2.7 Code |
|---|---|---|
| Vending-Bench 2 | 2,377 | 5,083 |
| Terminal-Bench | 33.4% | — |
| APEX-Agents | — | 37.6% |
| GBAEval | — | 0.9% |
Reasoning Kimi K2.7 Code leads
GLM-4.7: 24.3 (#164), Kimi K2.7 Code: 39.0 (#61)
| Benchmark | GLM-4.7 | Kimi K2.7 Code |
|---|---|---|
| SimpleBench | 47.7% | 57.9% |
| CritPt | 1.7% | 10% |
| Chess Puzzles | 6% | 21% |
| Epoch Capabilities Index | 143.51 | 149.97 |
| LMArena Hard Prompts | 1443 | — |
| Surface Evolver Bench | — | 48.8% |
Math Kimi K2.7 Code leads
GLM-4.7: 38.6 (#135), Kimi K2.7 Code: 52.9 (#48)
| Benchmark | GLM-4.7 | Kimi K2.7 Code |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 95.6% |
| FrontierMath (Tiers 1-3) | — | 54% |
| FrontierMath Tier 4 | — | 12.2% |
| ProofBench | 6% | — |
| LMArena Math | 1423 | — |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge Kimi K2.7 Code leads
GLM-4.7: 47.0 (#80), Kimi K2.7 Code: 53.5 (#57)
| Benchmark | GLM-4.7 | Kimi K2.7 Code |
|---|---|---|
| GPQA Diamond | 83.3% | 87.9% |
| SimpleQA Verified | 32.2% | 36.5% |
| Vectara Hallucination Rate | 11.7% | — |
| LMArena Expert | 1424 | — |
Multilingual Not comparable
GLM-4.7: 52.8 (#79), Kimi K2.7 Code: —
| Benchmark | GLM-4.7 | Kimi K2.7 Code |
|---|---|---|
| LMArena Non-English | 1417 | — |
| LMArena Chinese | 1495 | — |
| LMArena French | 1432 | — |
| LMArena German | 1424 | — |
| LMArena Japanese | 1439 | — |
| LMArena Korean | 1399 | — |
| LMArena Russian | 1423 | — |
| LMArena Spanish | 1434 | — |
Instruction Following Not comparable
GLM-4.7: 74.4 (#95), Kimi K2.7 Code: —
| Benchmark | GLM-4.7 | Kimi K2.7 Code |
|---|---|---|
| LMArena Instruction Following | 1411 | — |
Long Context Not comparable
GLM-4.7: 42.8 (#116), Kimi K2.7 Code: —
| Benchmark | GLM-4.7 | Kimi K2.7 Code |
|---|---|---|
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
| LMArena Longer Query | 1432 | — |
Writing & Preference Not comparable
GLM-4.7: 60.9 (#93), Kimi K2.7 Code: —
| Benchmark | GLM-4.7 | Kimi K2.7 Code |
|---|---|---|
| LMArena Text | 1435 | — |
| LMArena Creative Writing | 1401 | — |
| EQ-Bench Creative Writing | 1413 | — |
| LMArena Multi-Turn | 1446 | — |
Frequently asked questions
Is GLM-4.7 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-4.7 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-4.7 or Kimi K2.7 Code?
GLM-4.7 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-4.7 or Kimi K2.7 Code better for coding?
GLM-4.7 scores higher on coding benchmarks: 44.0 versus 42.9 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-4.7 and Kimi K2.7 Code share?
11 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Kimi K2.7 Code has 19.