Model comparison
GLM-5 vs Kimi K2.7 Code
GLM-5 is the stronger model overall, scoring 46.1 to 43.3 on the Noometry Index.
Last verified . 9 shared benchmarks.
Summary
- They share 9 benchmarks with published results for both. GLM-5 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 27.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 80% for GLM-5 and 95.6% for Kimi K2.7 Code.
- GLM-5 is cheaper at $1 / $3.20 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-5 | Kimi K2.7 Code | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Moonshot AI |
| Noometry Index | 46.1 | 43.3 |
| Released | 2026-02-11 | 2026-06-12 |
| Weights | Open | Open |
| Context window | 205K | 262K |
| Max output | 131K | 262K |
| Input $ / M tokens | $1 | $0.95 |
| Output $ / M tokens | $3.20 | $4 |
| Results tracked | 45 | 19 |
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Category by category
Coding GLM-5 leads
GLM-5: 49.0 (#52), Kimi K2.7 Code: 42.9 (#95)
| Benchmark | GLM-5 | Kimi K2.7 Code |
|---|---|---|
| LMArena WebDev | 1434 | 1473 |
| WeirdML | 48.2% | 54.1% |
| ALE-Bench | 765.62 | 886.23 |
| SWE-bench Verified | 72.1% | — |
| DeepSWE | — | 30.5% |
| FrontierCode | — | 30.1% |
| SWE-bench Verified (bash only) | 72.8% | — |
| SWE-bench Multilingual | 69.7% | — |
| SciCode | — | 47.5% |
| LMArena Coding | 1461 | — |
Agentic & Tool Use GLM-5 leads
GLM-5: 31.1 (#71), Kimi K2.7 Code: 24.0 (#122)
| Benchmark | GLM-5 | Kimi K2.7 Code |
|---|---|---|
| Vending-Bench 2 | 4,432 | 5,083 |
| Terminal-Bench | 52.4% | — |
| APEX-Agents | — | 37.6% |
| τ²-bench Airline | 82.5% | — |
| τ²-bench Banking | 9.8% | — |
| τ²-bench Retail | 73.7% | — |
| τ²-bench Telecom | 86.8% | — |
| GBAEval | — | 0.9% |
Reasoning Kimi K2.7 Code leads
GLM-5: 27.6 (#116), Kimi K2.7 Code: 39.0 (#61)
| Benchmark | GLM-5 | Kimi K2.7 Code |
|---|---|---|
| SimpleBench | 53.2% | 57.9% |
| Chess Puzzles | 10% | 21% |
| Epoch Capabilities Index | 145.83 | 149.97 |
| ARC-AGI-2 | 4.9% | — |
| Kagi LLM Benchmark | 75% | — |
| NYT Connections (extended) | 74.8% | — |
| ARC-AGI-1 | 44.7% | — |
| CritPt | — | 10% |
| LMArena Hard Prompts | 1452 | — |
| Surface Evolver Bench | — | 48.8% |
| ForecastBench | 61 | — |
Math Kimi K2.7 Code leads
GLM-5: 46.4 (#71), Kimi K2.7 Code: 52.9 (#48)
| Benchmark | GLM-5 | Kimi K2.7 Code |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 80% | 95.6% |
| FrontierMath (Tiers 1-3) | — | 54% |
| FrontierMath Tier 4 | — | 12.2% |
| MathArena Final-Answer Competitions | 65.7% | — |
| LMArena Math | 1440 | — |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Kimi K2.7 Code leads
GLM-5: 52.3 (#64), Kimi K2.7 Code: 53.5 (#57)
| Benchmark | GLM-5 | Kimi K2.7 Code |
|---|---|---|
| GPQA Diamond | 87.8% | 87.9% |
| SimpleQA Verified | — | 36.5% |
| Vectara Hallucination Rate | 10.1% | — |
| LMArena Expert | 1454 | — |
Multilingual Not comparable
GLM-5: 53.7 (#58), Kimi K2.7 Code: —
| Benchmark | GLM-5 | Kimi K2.7 Code |
|---|---|---|
| LMArena Non-English | 1430 | — |
| LMArena Chinese | 1511 | — |
| LMArena French | 1455 | — |
| LMArena German | 1445 | — |
| LMArena Japanese | 1416 | — |
| LMArena Korean | 1423 | — |
| LMArena Russian | 1436 | — |
| LMArena Spanish | 1454 | — |
Instruction Following Not comparable
GLM-5: 75.2 (#67), Kimi K2.7 Code: —
| Benchmark | GLM-5 | Kimi K2.7 Code |
|---|---|---|
| LMArena Instruction Following | 1428 | — |
Long Context Not comparable
GLM-5: 44.7 (#60), Kimi K2.7 Code: —
| Benchmark | GLM-5 | Kimi K2.7 Code |
|---|---|---|
| CL-bench | 18.7% | — |
| LMArena Longer Query | 1446 | — |
Writing & Preference Not comparable
GLM-5: 66.0 (#38), Kimi K2.7 Code: —
| Benchmark | GLM-5 | Kimi K2.7 Code |
|---|---|---|
| LMArena Text | 1446 | — |
| LMArena Creative Writing | 1439 | — |
| EQ-Bench Creative Writing | 1601 | — |
| LMArena Multi-Turn | 1456 | — |
Frequently asked questions
Is GLM-5 better than Kimi K2.7 Code?
GLM-5 is the stronger model overall, scoring 46.1 to 43.3 on the Noometry Index.
Which is cheaper, GLM-5 or Kimi K2.7 Code?
GLM-5 is cheaper. It lists at $1 per million input tokens and $3.20 per million output tokens; Kimi K2.7 Code lists at $0.95 and $4.
Is GLM-5 or Kimi K2.7 Code better for coding?
GLM-5 scores higher on coding benchmarks: 49.0 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-5 and Kimi K2.7 Code share?
9 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and Kimi K2.7 Code has 19.