DeepSeek-V3 vs Kimi K2.5
Verdict
Kimi K2.5 leads 4–0 across 4 shared benchmarks.
DeepSeek-V3 0 · Kimi K2.5 4 · higher isn't always better — see caveats.
Per-benchmark head-to-head
Reported scores — protocols may differ. How we compare
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DeepSeek-V3Kimi K2.5
Capabilities
Math
Math: Kimi K2.5 leads 2–0 across 2 shared math benchmarks (largest gap: OTIS Mock AIME 2024-2025, 92.19 vs 15.75 — single evaluator).
4 reproduced
Reasoning
Reasoning: Kimi K2.5 leads 2–0 across 2 shared reasoning benchmarks (largest gap: GPQA diamond, 83.47 vs 42.05 — single evaluator).
2 reproduced2 unverified
What the labs claim
Vendor claims — compare against the measured scores above. A claim is what a developer says about its own model, not an independent measurement.
DeepSeek-V3
Coding
“DeepSeek-V3 achieves the best performance on most benchmarks, especially on math and code tasks.”
github.com2024
General
“At an economical cost of only 2.664M H800 GPU hours, we complete the pre-training of DeepSeek-V3 on 14.8T tokens, producing the currently strongest open-source base model.”
github.com2024“DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models.”
github.com2024
Reasoning
“Our pipeline elegantly incorporates the verification and reflection patterns of R1 into DeepSeek-V3 and notably improves its reasoning performance.”
github.com2024
Kimi K2.5
Agentic / Tool Use
“It seamlessly integrates vision and language understanding with advanced agentic capabilities, instant and thinking modes, as well as conversational and agentic paradigms.”
github.com2026“K2.5 transitions from single-agent scaling to a self-directed, coordinated swarm-like execution scheme.”
github.com2026
Coding
“K2.5 generates code from visual specifications (UI designs, video workflows) and autonomously orchestrates tools for visual data processing.”
github.com2026
Multimodal
“Kimi K2.5 is an open-source, native multimodal agentic model built through continual pretraining on approximately 15 trillion mixed visual and text tokens atop Kimi-K2-Base.”
github.com2026