Kimi K2.7 Code
kimi-k2-7-codekimi-k2.7-code- 🧠 Coding-focused agentic model built on Kimi K2.6.
- 🏢 Made by Moonshot AI, released June 2026.
- 📏 1T total parameters, 32B active, 256K context.
- 🔧 Mixture-of-Experts; always operates in thinking mode.
- 👁️ Accepts text and image input for coding workflows.
- 🔧 Supports function-calling and web-search for agentic loops.
- 🎯 Targets long-horizon software engineering and tool use.
- 🔒 Open weights published on Hugging Face.
Moonshot is an AI research lab known for developing the Kimi family of large language models. The organization has gained recognition for building capable reasoning-oriented models, with the Kimi line representing its flagship series of text generation systems.
Explore 4 more models by Moonshot →Kimi K2.7 Code is Moonshot AI's coding-specialized member of the Kimi K2 line, trained directly on top of the general-purpose [[sibling:kimi-k2-6|Kimi K2.6]] released two months earlier, which itself succeeded [[sibling:kimi-k2-5|Kimi K2.5]]. Rather than a broad capability bump, it is a focused agentic-coding release that keeps the trillion-parameter Mixture-of-Experts architecture (1T total, 32B active per token) and adds long-horizon software-engineering training for tasks like codebase analysis, debugging, refactoring, and multi-step tool use.
Architecturally it stays close to its predecessors, so existing deployment setups can largely be reused, and it ships with a 256K-token context window and always-on thinking mode. The Venice catalog lists it as supporting text and image input with function-calling and web-search capabilities, distributed here at int4 quantization.
Compared with its predecessor, Moonshot reports gains on its own coding and agent evaluations, attributing the improvement to the model's tool-calling and long-horizon software-engineering focus; these figures are vendor self-reported on internal benchmarks, so treat them as the provider's claims rather than independent results, and third-party evaluation data was limited at release.
The weights are published on Hugging Face, and the model is paired with Moonshot's coding agent tooling for terminal and multi-turn workflows.
This About section is AI-generated from public sources via VeniceStats + Venice inference, with no human editing. It may contain inaccuracies.
| Seller | Reputation↓ | Routing | Input $/M | Cached $/M | Output $/M | Categories | API |
|---|---|---|---|---|---|---|---|
| ▲ Apex Ant 0x73b4…e736 | 83 | #1 | $0.2005 | $0.0936 | $0.9356 | chat,coding,open-source,vision,multimodal,reasoning,long-context,agents | openai-chat-completions |
| Open Ant 0xe4f6…5bc4 | 74 | #3 | $0.4875 | $0.104 | $2.275 | chat,coding,reasoning,vision,multimodal,tools | openai-chat-completions |
| surplusintelligence.ai 0x0e49…8927 | 68 | #2 | $0.225 | $0.048 | $1.05 | agents,chat,code,coding,developer,frontier,function-calling,multimodal,reasoning,research,tasks,tools,vision,web-search | openai-chat-completions |
| NovaRoute AI 0xc50d…ed7b | 60 | gated | $0.2228 | $0.2228 | $1.0395 | chat,coding,code,reasoning,tasks,kimi,value,surplus,openai-compatible,low-cost,verified,github,response-auth,base-usdc,monitored | openai-chat-completions |
| Super Seeder 0xd19f…41f3 | 40 | gated | $0.5625 | $0.12 | $2.625 | chat,coding,reasoning,vision,multimodal,tools | openai-chat-completions |
| Fire Ant 🔥🐜 0xbe05…bc5d | 37 | gated | $0.665 | $0.144 | $3.15 | base-usdc,chat,code,coding,github,kimi,low-cost,math,monitored,openai-compatible,reasoning,response-auth,surplus,tasks,value,verified | — |
| adfreellm Gateway 0xabd5…4e98 | 5 | gated | $0.1337 | $0.0134 | $0.6237 | coding,math | openai-chat-completions |
| antseed-neon-puma-944e 0x6650…944e | 3 | gated | $0.3069 | $0.20 | $1.4663 | chat,coding,math | openai-chat-completions |
| antseed-opal-badger-2580 0xc85d…2580 | 1 | gated | $0.375 | $0.375 | $1.75 | chat | openai-chat-completions |
| Skeffo Inference 0x1af8…e2b5 | 0 | gated | $0.50 | $0.50 | $3.25 | chat,reasoning,agent,function-calling | openai-chat-completions |
| Apex TEE Test 0xe672…7955 | 0 | gated | $0.2025 | $0.0432 | $0.945 | chat,coding,open-source,vision,multimodal,reasoning,long-context,agents | openai-chat-completions |
"Best price" and the seller table are live AntSeed catalog data (advertised $/1M tokens — or $ per generated image for unit-billed image models — not settled amounts). Reputation = on-chain trust (0-100). "Routing" = the SDK's default buyer routing (what the VPR desktop app ships with): a trust ≥ 60 gate on the effective reputation, then cheapest-first among routable sellers; live failover state (per-peer cooldowns) is buyer-side runtime and not included. Model knowledge (TLDR, provider, About) via the VeniceStats enrichment layer. Advertised catalog, not the model used in any specific purchase. "Usage on AntSeed" counts only settlements whose buyers share the per-model split on-chain (metadata v2/v3, opt-in), so every usage figure is a lower bound.