Compare · ModelsLive · 2 picked · head to head
GLM 5.2 vs Kimi K2.7 Code
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
GLM 5.2 wins on 17/22 benchmarks
GLM 5.2 wins 17 of 22 shared benchmarks. Leads in speed · knowledge · coding.
Category leads
speed·GLM 5.2knowledge·GLM 5.2coding·GLM 5.2math·GLM 5.2reasoning·GLM 5.2language·GLM 5.2general·GLM 5.2
Hype vs Reality
Attention vs performance
GLM 5.2
#67 by perf·#3 by attention
Kimi K2.7 Code
#69 by perf·#17 by attention
Best value
Kimi K2.7 Code
1.4x better value than GLM 5.2
GLM 5.2
22.6 pts/$
$2.50/M
Kimi K2.7 Code
30.5 pts/$
$1.84/M
Vendor risk
Who is behind the model
z-ai
private · undisclosed
Moonshot AI
$18.0B·Tier 1
Head to head
22 benchmarks · 2 models
GLM 5.2Kimi K2.7 Code
Artificial Analysis · Agentic Index
GLM 5.2 leads by +13.5
Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?"
GLM 5.2
43.1
Kimi K2.7 Code
29.6
Artificial Analysis · Coding Index
GLM 5.2 leads by +8.0
Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads.
GLM 5.2
68.8
Kimi K2.7 Code
60.8
Artificial Analysis · Quality Index
GLM 5.2 leads by +25.3
GLM 5.2
51.1
Kimi K2.7 Code
25.8
Chess Puzzles
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
GLM 5.2
16.9
Kimi K2.7 Code
16.9
Deepswe
GLM 5.2 leads by +13.3
GLM 5.2
43.8
Kimi K2.7 Code
30.5
Frontiercode
Kimi K2.7 Code leads by +5.6
GLM 5.2
24.5
Kimi K2.7 Code
30.1
FrontierMath-Tier-4-v2-Private
GLM 5.2 leads by +17.1
GLM 5.2
29.3
Kimi K2.7 Code
12.2
FrontierMath-Tiers-1-3-v2-Private
GLM 5.2 leads by +5.2
GLM 5.2
59.2
Kimi K2.7 Code
54.0
GPQA diamond
GLM 5.2 leads by +5.3
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
GLM 5.2
89.1
Kimi K2.7 Code
83.8
LiveBench · Agentic Coding
GLM 5.2 leads by +3.3
GLM 5.2
73.3
Kimi K2.7 Code
70.0
LiveBench · Coding
GLM 5.2 leads by +5.7
GLM 5.2
79.7
Kimi K2.7 Code
74.0
LiveBench · Data Analysis
GLM 5.2 leads by +11.1
GLM 5.2
73.7
Kimi K2.7 Code
62.7
LiveBench · If
GLM 5.2 leads by +6.0
GLM 5.2
62.3
Kimi K2.7 Code
56.3
LiveBench · Language
Kimi K2.7 Code leads by +1.7
GLM 5.2
76.2
Kimi K2.7 Code
77.9
LiveBench · Mathematics
GLM 5.2 leads by +10.2
GLM 5.2
89.8
Kimi K2.7 Code
79.6
LiveBench · Overall
GLM 5.2 leads by +4.3
GLM 5.2
76.2
Kimi K2.7 Code
71.9
LiveBench · Reasoning
Kimi K2.7 Code leads by +4.2
GLM 5.2
78.6
Kimi K2.7 Code
82.8
OTIS Mock AIME 2024-2025
Kimi K2.7 Code leads by +9.2
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
GLM 5.2
86.4
Kimi K2.7 Code
95.5
SimpleBench
GLM 5.2 leads by +1.1
SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking.
GLM 5.2
50.6
Kimi K2.7 Code
49.5
SimpleQA Verified
Kimi K2.7 Code leads by +2.3
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
GLM 5.2
34.2
Kimi K2.7 Code
36.5
Surface Evolver Bench
GLM 5.2 leads by +6.9
GLM 5.2
55.6
Kimi K2.7 Code
48.8
WeirdML
GLM 5.2 leads by +16.0
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
GLM 5.2
70.1
Kimi K2.7 Code
54.1
Full benchmark table
| Benchmark | GLM 5.2 | Kimi K2.7 Code |
|---|---|---|
Artificial Analysis · Agentic Index Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?" | 43.1 | 29.6 |
Artificial Analysis · Coding Index Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads. | 68.8 | 60.8 |
Artificial Analysis · Quality Index | 51.1 | 25.8 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 16.9 | 16.9 |
Deepswe | 43.8 | 30.5 |
Frontiercode | 24.5 | 30.1 |
FrontierMath-Tier-4-v2-Private | 29.3 | 12.2 |
FrontierMath-Tiers-1-3-v2-Private | 59.2 | 54.0 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 89.1 | 83.8 |
LiveBench · Agentic Coding | 73.3 | 70.0 |
LiveBench · Coding | 79.7 | 74.0 |
LiveBench · Data Analysis | 73.7 | 62.7 |
LiveBench · If | 62.3 | 56.3 |
LiveBench · Language | 76.2 | 77.9 |
LiveBench · Mathematics | 89.8 | 79.6 |
LiveBench · Overall | 76.2 | 71.9 |
LiveBench · Reasoning | 78.6 | 82.8 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 86.4 | 95.5 |
SimpleBench SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking. | 50.6 | 49.5 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 34.2 | 36.5 |
Surface Evolver Bench | 55.6 | 48.8 |
WeirdML WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns. | 70.1 | 54.1 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $1.00 | $4.00 | 1.0M tokens (~524 books) | $17.50 | |
| $0.61 | $3.07 | 262K tokens (~131 books) | $12.26 |