Compare · ModelsLive · 2 picked · head to head
GPT-5.2 vs Kimi K2 0711
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
GPT-5.2 wins on 4/4 benchmarks
GPT-5.2 wins 4 of 4 shared benchmarks. Leads in coding · reasoning.
Category leads
coding·GPT-5.2reasoning·GPT-5.2
Hype vs Reality
Attention vs performance
GPT-5.2
#154 by perf·#10 by attention
Kimi K2 0711
#74 by perf·#18 by attention
Best value
Kimi K2 0711
6.6x better value than GPT-5.2
GPT-5.2
6.0 pts/$
$7.88/M
Kimi K2 0711
39.1 pts/$
$1.43/M
Vendor risk
Who is behind the model
OpenAI
$840.0B·Tier 1
Moonshot AI
$50.0B·Tier 1
Head to head
4 benchmarks · 2 models
GPT-5.2Kimi K2 0711
GSO-Bench
GPT-5.2 leads by +22.5
GSO-Bench · evaluates AI models on real-world open-source software engineering tasks, testing the ability to understand and resolve actual GitHub issues.
GPT-5.2
27.4
Kimi K2 0711
4.9
SimpleBench
GPT-5.2 leads by +23.4
SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking.
GPT-5.2
35.0
Kimi K2 0711
11.6
Terminal Bench
GPT-5.2 leads by +37.1
Terminal-Bench 2.0 · evaluates AI agents on real terminal-based coding tasks · writing scripts, debugging, running tests, and managing projects entirely through command-line interaction. Tests both code quality and terminal fluency. Claude Opus 4.7 scores 69.4%, demonstrating significant agentic terminal competence.
GPT-5.2
64.9
Kimi K2 0711
27.8
WeirdML
GPT-5.2 leads by +32.8
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
GPT-5.2
72.2
Kimi K2 0711
39.4
Full benchmark table
| Benchmark | GPT-5.2 | Kimi K2 0711 |
|---|---|---|
GSO-Bench GSO-Bench · evaluates AI models on real-world open-source software engineering tasks, testing the ability to understand and resolve actual GitHub issues. | 27.4 | 4.9 |
SimpleBench SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking. | 35.0 | 11.6 |
Terminal Bench Terminal-Bench 2.0 · evaluates AI agents on real terminal-based coding tasks · writing scripts, debugging, running tests, and managing projects entirely through command-line interaction. Tests both code quality and terminal fluency. Claude Opus 4.7 scores 69.4%, demonstrating significant agentic terminal competence. | 64.9 | 27.8 |
WeirdML WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns. | 72.2 | 39.4 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $1.75 | $14.00 | 400K tokens (~200 books) | $48.13 | |
| $0.57 | $2.30 | 131K tokens (~66 books) | $10.03 |