Bedrock Mantle/moonshotai Kimi K2 Thinking

Kimi K2 Thinking

Openrouter · LLM · #64 Rank Active
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75.0
Score
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Benchmark Scores

Code Based on benchmarks: SWE-Bench Verified 71.3% · SWE-Bench (mini-SWE-agent) 63.4%
67.1
#134
SWE-Bench, Hugging Face, Benchmark composite
SWE-Bench (mini-SWE-agent) — 63.4% (confidence 82%); SWE-Bench Verified — 71.3% (confidence 82%); SWE-Bench Verified 71.3% · SWE-Bench (mini-SWE-agent) 63.4% — 67.1 (confidence 84%)
Reasoning Based on benchmarks: GPQA Diamond 84.5%
84.5
#79
Hugging Face, Benchmark composite
GPQA Diamond — 84.5% (confidence 82%); GPQA Diamond 84.5% — 84.5 (confidence 78%)
Speed Output throughput (tokens/sec) from modelgrep live probes, or catalog estimate as fallback.
3.4
#388
modelgrep · 10 hours ago
Price Normalized API cost (input + output per 1M tokens) from OpenRouter and models.dev.
92.1
#374
models.dev · 10 hours ago

What the rating is based on

75.0
  • Code 20% 67.1 SWE-Bench, Hugging Face, Benchmark composite
    • SWE-Bench (mini-SWE-agent) 63.4% confidence 82%
    • SWE-Bench Verified 71.3% confidence 82%
    • SWE-Bench Verified 71.3% · SWE-Bench (mini-SWE-agent) 63.4% 67.1 confidence 84%
  • Text 24% No data
  • Image 12% No data
  • Math 5% No data
  • Reasoning 39% 84.5 Hugging Face, Benchmark composite
    • GPQA Diamond 84.5% confidence 82%
    • GPQA Diamond 84.5% 84.5 confidence 78%
Benchmark breakdown
  • GPQA Diamond Reasoning · 84.5% · Hugging Face
  • SWE-Bench (mini-SWE-agent) Code · 63.4% · SWE-Bench
  • SWE-Bench Verified Code · 71.3% · Hugging Face

No data for: Text, Image, Math — excluded from the overall score.

How we calculate scores

Description

The Bedrock/moonshotai Kimi K2 Thinking model is specifically designed for complex reasoning and planning tasks. With its expansive context window of 262,144 tokens, users can tackle intricate problems that require detailed analysis and deep reasoning. This model shines in areas like software engineering, where it demonstrates strong capabilities on coding benchmarks and reasoning tasks, making it suitable for developers and data scientists needing to generate or refine complex code and algorithms. However, potential users should be aware of the trade-offs involved. Kimi K2 Thinking is a paid API, costing $0.6 for input and $2.5 for output per 1M tokens, which may deter those seeking budget-friendly options. Additionally, it may not be the best choice for casual conversations or light tasks, as its strengths lie firmly in analytical and technical domains, requiring more focus and investment.

Strengths

  • Extensive context window allows for in-depth reasoning.
  • Strong performance on coding benchmarks, suitable for developers.
  • Optimized for complex planning and problem-solving tasks.
  • Reasoning capabilities excel in specific benchmarks like GPQA Diamond.

Limitations

  • Not suited for casual or conversational use.
  • Paid API may impose cost constraints for some users.
  • Latency may be higher due to the depth of reasoning required.

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