Kimi K2.5
Moonshot AI
Open-source multimodal agentic model for coding, visual understanding, documents, research, and multi-step developer workflows.
Scorecard
- Coding & agentic86
- Multimodal & I/O84
- Technical capability83
- Cost effectiveness82
- Developer experience82
- Speed & availability80
- Risk & evidence80
- Reasoning & knowledge78
- Pricing clarity76
- Adoption signal73
Kainotomic evaluation
Kimi K2.5 has strong evidence for coding and agentic developer work. Official Moonshot sources describe an open-source, multimodal, agentic model for coding, visual understanding, documents, research, and multi-step workflows. Public coding evidence is also substantive: SWE-bench Verified lists Kimi K2.5 high reasoning at 70.80% resolved, while Moonshot’s Hugging Face card reports Kimi K2.5 Thinking at 76.8 on SWE-Bench Verified using an internal 5-run framework. LiveCodeBench evidence reports 85.0, ranked 9th among 31 models shown. The model’s broader capability profile is good but not uniformly frontier. Artificial Analysis lists a 47 Intelligence Index, 256k context, 44.2 output tokens/s for the reasoning model, and prices of $0.58/M input and $3.00/M output tokens. Separate provider benchmarking shows much faster non-reasoning hosted options, including Fireworks at 337.8 tokens/s and a lowest blended price of $0.39/M tokens. This supports favorable cost-effectiveness, though availability and performance depend heavily on provider and reasoning mode. Evidence quality is reasonably high because official docs, model pages, GitHub, SWE-bench, LiveCodeBench, Artificial Analysis, Terminal-Bench, and LMArena were checked. Caveats remain: DeepSWE/DataCurve had no Kimi K2.5 row, CodeSOTA only indicates no DeepSWE result, and Terminal-Bench lists “Terminus 2 Kimi K2.5” at 43.2% ± 2.9, which is a weaker agentic terminal result. LMArena ranks are useful public preference signals, not controlled capability measurements.
Strengths
- Strong public coding evidence from SWE-bench Verified and LiveCodeBench.
- Official positioning covers multimodal, document, research, coding, and agentic workflows.
- Open-source project presence through Moonshot AI GitHub and Hugging Face model card.
- Competitive token pricing and provider options according to Artificial Analysis.
- Large 256k-token context window reported by Artificial Analysis.
Caveats
- SWE-bench results differ by source: official leaderboard reports 70.80%, while Moonshot Hugging Face reports 76.8 from an internal 5-run framework.
- DeepSWE/DataCurve was checked but has no Kimi K2.5 row or score.
- Terminal-Bench result is modest and appears under a specific “Terminus 2 Kimi K2.5” setup.
- LMArena evidence is preference/product-feel signal, not a controlled benchmark.
- Speed and price vary materially between reasoning and non-reasoning provider configurations.