Model comparison
DeepSeek vs Kimi API
DeepSeek v4 Pro is a strong tool-calling route, while Kimi K2.6 is useful for coding support, instruction following, and Kimi-family comparisons.
Route shortlist
Benchmark comparison
| Model | Total /100 | Extraction | Instructions | Math | Tools | Fully correct | Tokens | Cost (USD) | CPS ($/100 points) |
|---|---|---|---|---|---|---|---|---|---|
DeepSeek v4 Prodeepseek-v4-pro | 99.00 | 98.33 | 98.67 | 99.00 | 100.00 | 57/60 | 116,757 | $0.145106 | $0.146572 |
Kimi K2.6kimi-k2.6 | Not evaluated in V3.2 | — | — | — | — | — | — | — | — |
BenchLocal V3.2 · Original 27 models: 2 October 2026; three full-suite supplements: 15 October 2026 · 60 tasks per model. Total = mean of the four category scores. The original models retain 45 V3 tasks and 15 revised tool tasks; the three additional model IDs each completed all 60 identical tasks. Near-ceiling scores do not establish general capability; V3 scores are not directly comparable. CPS = Cost per Score; shown as USD per 100 score points: retained execution cost ÷ unrounded total score × 100. Free models are shown at $0. Paid-model costs are historical charges, excluding failed requests and discarded retests.
Decision guide
Recommended route
Start with DeepSeek v4 Pro for tool-heavy workflows and Kimi K2.6 when comparing Kimi behavior on your own prompts.
Benchmark signal
DeepSeek v4 Pro scored 89 overall in the earlier pre-V3 evaluation; Kimi K2.6 scored 88 overall in the earlier pre-V3 evaluation. The practical winner depends on prompt shape, latency, and agent guardrails.
Production caveat
Both routes should be tested with fallback logic and human review for destructive or privileged automation.
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