Compare · ModelsLive · 2 picked · head to head
Claude Opus 4.5 vs Kimi K2 0711
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
Claude Opus 4.5 wins on 4/4 benchmarks
Claude Opus 4.5 wins 4 of 4 shared benchmarks. Leads in coding · reasoning.
Category leads
coding·Claude Opus 4.5reasoning·Claude Opus 4.5
Hype vs Reality
Attention vs performance
Claude Opus 4.5
#197 by perf·#5 by attention
Kimi K2 0711
#81 by perf·#15 by attention
Best value
Kimi K2 0711
14.4x better value than Claude Opus 4.5
Claude Opus 4.5
2.7 pts/$
$15.00/M
Kimi K2 0711
39.1 pts/$
$1.43/M
Vendor risk
Who is behind the model
Anthropic
$965.0B·Tier 1
Moonshot AI
$50.0B·Tier 1
Head to head
4 benchmarks · 2 models
Claude Opus 4.5Kimi K2 0711
GSO-Bench
Claude Opus 4.5 leads by +21.6
GSO-Bench · evaluates AI models on real-world open-source software engineering tasks, testing the ability to understand and resolve actual GitHub issues.
Claude Opus 4.5
26.5
Kimi K2 0711
4.9
SimpleBench
Claude Opus 4.5 leads by +42.8
SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking.
Claude Opus 4.5
54.4
Kimi K2 0711
11.6
Terminal Bench
Claude Opus 4.5 leads by +35.3
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 4.5
63.1
Kimi K2 0711
27.8
WeirdML
Claude Opus 4.5 leads by +24.4
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
Claude Opus 4.5
63.7
Kimi K2 0711
39.4
Full benchmark table
| Benchmark | Claude Opus 4.5 | 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. | 26.5 | 4.9 |
SimpleBench SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking. | 54.4 | 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. | 63.1 | 27.8 |
WeirdML WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns. | 63.7 | 39.4 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $5.00 | $25.00 | 200K tokens (~100 books) | $100.00 | |
| $0.57 | $2.30 | 131K tokens (~66 books) | $10.03 |