Kimi 2.7 vs. DeepSeek Coder

Three Chinese AI labs—Moonshot AI, Xiaomi, and DeepSeek—released open-source coding tools this spring: Kimi K2.7 Code, MiMo Code, and DeepSeek V4 Pro. Kimi K2.7 Code is a coding-optimized API model, MiMo Code is a terminal coding agent with persistent memory, and DeepSeek V4 Pro is a general-purpose model with strong coding capabilities. The choice depends on whether developers need a model, an agent, or a general-purpose tool.

Kimi K2.7 Code vs MiMo Code vs DeepSeek V4 Pro: Three Open-Source Coding Tools Compared Three Chinese AI labs shipped major coding tools in the same window this spring: Moonshot AI released Kimi K2.7 Code, Xiaomi shipped MiMo Code, and DeepSeek launched V4 Pro. All three are open-source, all three target developers who want a coding AI, and all three benchmark well. But they are not the same type of thing. Kimi K2.7 Code is a model you call via API. MiMo Code is a terminal coding agent in the same category as Claude Code, not a model you call directly. DeepSeek V4 Pro is a general-purpose model with strong coding capabilities. The right choice depends on which layer of the stack you actually need. Quick comparison What each tool is Kimi K2.7 Code is a coding-optimized model from Moonshot AI Kimi K2.7 Code is a code-optimized variant of the Kimi K2 model family, built on a Mixture-of-Experts architecture with 1T total parameters and 32B active per token. It has a 262,144-token context window, supports vision inputs images and video , and always runs in thinking mode. Non-thinking mode is not supported and will throw an error if requested. The model is available via the Moonshot AI API and on Cloudflare Workers AI. The API is OpenAI-compatible: python from openai import OpenAIclient = OpenAI api key="YOUR MOONSHOT API KEY", base url="https://api.moonshot.ai/v1" response = client.chat.completions.create model="kimi-k2.7-code", messages= {"role": "user", "content": "Write a Rust function to parse a CSV file"} You can also use it as a model backend inside Claude Code, Cline, or Roo Code rather than running it as a standalone API. MiMo Code is a terminal coding agent from Xiaomi, not just a model MiMo Code is easy to misread as another model release. It is a terminal-native AI coding agent in the same category as Claude Code or OpenCode, which it is forked from. It can read and write code, run commands, manage Git, and maintain persistent memory across sessions. Install it with a single command: macOS / Linuxcurl -fsSL https://mimo.xiaomi.com/install | bash Windowsnpm install -g @mimo-ai/cli The first launch walks you through configuration. The default option, MiMo Auto, connects to Xiaomi's MiMo-V2.5-Pro model 1T total parameters, 42B active, 1M context window at no cost, with no account required, for a limited time. The core differentiator is memory. Xiaomi's argument is that context compression fails at scale: "What we need is not better compression, but an explicit storage-and-retrieval mechanism that decides what information should be written into persistent structures, and when it should be recalled." MiMo Code implements a four-layer memory system backed by SQLite FTS5: Project memory MEMORY.md — persistent project knowledge, rules, and architecture decisions Session checkpoints checkpoint.md — maintained automatically by an independent checkpoint-writer subagent so the primary agent never pauses to take notes Scratch notes notes.md — temporary area for agents mid-task Task progress tasks/<id /progress.md — per-task logs preserved across sessions When context approaches its limit, the agent rebuilds from the latest checkpoint, project memory, and task progress rather than losing state. The /dream command run periodically scans session history, deduplicates it, and compresses it into long-term memory. The /distill command finds repeated workflows and packages them as reusable skills. MiMo Code has three modes switchable with Tab: build full permissions, default , plan read-only analysis , and compose spec-driven development . It imports MCP servers and authentication from Claude Code automatically, and supports any OpenAI-compatible API as a backend if you want to swap out the bundled model. DeepSeek V4 Pro is a general-purpose model with strong agentic coding focus DeepSeek V4 Pro is the largest of the three, with 1.6T total parameters, 49B active per token, a 1M token context window, and the ability to run in both thinking and non-thinking modes. DeepSeek positions it as "open-source SOTA in Agentic Coding benchmarks" and notes it is already integrated with Claude Code, OpenCode, and OpenClaw. The API is straightforward to adopt if you are already using OpenAI or Anthropic's client libraries. It supports both formats at the same base URL: base url: https://api.deepseek.commodel: deepseek-v4-pro or deepseek-v4-flash for lower cost DeepSeek V4-Flash 284B total, 13B active offers nearly the same reasoning performance as V4-Pro on simpler agent tasks at roughly a third of the cost. Note: deepseek-chat and deepseek-reasoner are being retired on July 24, 2026. They currently route to V4-Flash. Benchmark results Comparing these three tools directly is difficult. They run different benchmarks, report results differently, and no independent evaluation covers all three. Kimi K2.7 Code gains over K2.6 on internal coding benchmarks Cloudflare reports gains over K2.6: +21.8% on Kimi Code Bench v2, +11.0% on Program Bench, +31.5% on MLS Bench Lite. K2.7 Code also uses 30% fewer reasoning tokens than K2.6, which reduces inference cost on long coding tasks. For context, K2.6 scored 58.6 on SWE-Bench Pro and 66.7 on Terminal-Bench 2.0, ahead of GPT-5.4 57.7 and 65.4 and Claude Opus 4.6 53.4 and 65.4 on those two benchmarks. MiMo Code beats Claude Code on tasks over 200 steps Xiaomi reports these numbers against Claude Code + Claude Sonnet 4.6, using MiMo Code + MiMo-V2.5-Pro: Running the same MiMo-V2.5-Pro model in both harnesses, MiMo Code scores 62% on SWE-bench Pro versus 57% for the Claude Code harness, about five points attributable to the agent architecture rather than the underlying model. In a human A/B evaluation across 576 developers and 1,213 judged task pairs, the two tools split roughly 50/50 on tasks under 200 steps. Past 200 steps, MiMo Code's win rate rose above 65%. These are vendor self-reported numbers that have not been independently verified. MiMo Code does not yet appear on the official SWE-bench or Terminal-Bench leaderboards, and harness comparisons are sensitive to configuration. For reference, OpenAI's Codex CLI running GPT-5.5 scores 82.2% on Terminal-Bench 2, nine points above MiMo Code's reported 73%. On SWE-Bench Pro, GPT-5.5 scores 58.6%, below MiMo Code's claimed 62%. DeepSeek V4 Pro claims open-source SOTA on agentic coding DeepSeek claims V4-Pro is "open-source SOTA in Agentic Coding benchmarks." The full technical report with benchmark details is available at the HuggingFace repo. Official release notes do not include SWE-bench or Terminal-Bench scores directly, so independent verification requires reading the paper. Pricing DeepSeek V4-Flash is the cheapest API option at $0.14/$0.28 per million tokens, with cache hits dropping input cost to $0.0028. MiMo Code's MiMo Auto channel is free to start. Xiaomi is offering zero-cost access to MiMo-V2.5-Pro with no registration, though this is limited-time. Kimi K2.7 Code's full pricing non-cached input, output is not published in the official API docs. Only the cached input rate $0.19/M is confirmed from the Cloudflare integration page. Kimi K2.6 runs $0.95/$4.00, so K2.7 pricing is likely in a similar range. Which to use Use Kimi K2.7 Code when you need a coding-optimized model backend Kimi K2.7 Code makes the most sense when you are building or running a coding agent and need a model that handles long, complex tasks without generating unnecessary reasoning tokens. The 30% reduction in reasoning tokens versus K2.6 matters at scale. It slots directly into Claude Code, Cline, and Roo Code, and the vision support is useful for tasks that involve UI screenshots alongside code. The 262K context window is smaller than DeepSeek's 1M, but 32B active parameters makes it more efficient per token. Use MiMo Code when you want a ready-to-run terminal coding agent with persistent memory MiMo Code makes the most sense when long-running coding sessions are your bottleneck. It is built for tasks that span many steps, many sessions, or multiple files where context loss causes the agent to forget earlier decisions. It is free to start, installs in under a minute, and imports your Claude Code configuration automatically if you are already using it. Three caveats apply. MiMo Code is at V0.1.0 maturity. MiMo Auto routes your code through Xiaomi's servers, which is a hard block for strict data-residency requirements. The free tier will end. Organizations that need full control can run MiMo Code against their own model endpoint. The MIT license and bring-your-own-model support make that practical. Use DeepSeek V4 Pro when you need 1M context and dual API compatibility DeepSeek V4-Pro is the right choice when you need the full 1M context window, want to stay within OpenAI or Anthropic API conventions without changing client code, or need an open-weight model you can self-host. V4-Flash covers the same use case at lower cost for workloads where V4-Pro's larger size is not needed. All three are actively developed and all three will look different in six months. The meaningful question today is what you are actually building.