DeepSeek vs Kimi vs MiniMax: Which Chinese LLM API to Use?
deepseek vs kimi vs minimaxchinese llm apiopen weight llmcheap agent modelllm comparison

DeepSeek vs Kimi vs MiniMax: Which Chinese LLM API to Use?

2026-07-10

Three Cheap, Capable Open-Weight Models

DeepSeek, Kimi (Moonshot), and MiniMax are the three open-weight Chinese LLMs most Western developers evaluate for cost-efficient production. They overlap a lot on price, but differ on the things that actually decide a build: context, multimodality, and what each was trained to do. All three are on LinkModel under one key.

The Specs

DeepSeek V4 FlashKimi K2.6MiniMax M2.7
Input / Output (1M)$0.14 / $0.28$0.95 / $4.00$0.30 / $1.20
Cached input~$0.0028 (98%)$0.16 (~6x)
Context1M256K205K
MultimodalTextText + imageText
LicenseMITModified-MITModified-MIT
Trained forEfficiency, cache-heavyMultimodal agent swarmsLong-horizon agents

Where Each Wins

  • DeepSeek V4 Flash — the cost king. Cheapest by far, 1M context, and a 98% cache discount that makes cache-heavy workloads (RAG, chatbots, agents with stable prefixes) nearly free on input. Default for high-volume, stable-context, output-heavy work. (Step up to V4 Pro for competition-grade coding.)
  • Kimi K2.6 — the only one of the three that takes image input. Pick it for UI-from-screenshot, document-with-figures, and large multi-agent orchestration (up to 300 sub-agents). Beats GPT-5.4 on HLE-Full with tools.
  • MiniMax M2.7 — the cheapest capable agent model, explicitly trained for multi-agent collaboration, live debugging, and multi-step delivery. Best when the model must hold a coherent plan across dozens of tool calls over long runs.

Quick Decision

NeedPick
Cheapest / cache-heavy / high-volumeDeepSeek V4 Flash
Image input / UI generationKimi K2.6
Long-horizon agents on a budgetMiniMax M2.7
Competition-grade codingDeepSeek V4 Pro

Shared Caveats

  • Data residency: all three official APIs process data in China (a GDPR consideration). Self-host or use a zero-retention gateway. LinkModel defaults to zero data retention.
  • Licensing: DeepSeek is fully MIT; Kimi and MiniMax are Modified-MIT (commercial self-hosting needs authorization).
  • Thinking modes bill at the same rate but burn more tokens — off by default.

The Best Answer Is "Route"

These aren't mutually exclusive. A common stack: DeepSeek V4 Flash as the cheap default, MiniMax M2.7 for long agent plans, Kimi K2.6 when a task has images. Because all three are OpenAI-compatible and share one request shape on LinkModel, routing is a config change. More in open-source LLM API and best coding LLM API.

Start free with a $1 credit and test all three on your workload.

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