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Moonshot AI vs Relace
Relace supplies small apply, search and compaction models for coding agents. Paired with Kimi K3, it handles the mechanical steps K3 is slow at.
By The Subconscious Team · Updated
Moonshot AI vs Relace: key differences
Relace builds utility models for coding agents, and Kimi K3 is the kind of main model they are meant to serve. K3 brings the planning: near-frontier coding scores, a 1M window and native vision. Relace brings speed on the mechanical work. Its relace-apply-3 model merges a lazy edit snippet into the original file at about 10,000 tokens per second, which Relace says runs over 3x faster and cheaper than a full rewrite by the big model. Against K3's roughly 33 tokens per second, that gap is large enough to change how an agent is built.
Relace's agentic search explores large codebases in parallel and answers in seconds, and its compaction model runs at 50,000 tokens per second, both useful on the huge repositories K3 targets. The limits differ by size. Relace returns an error past 128K tokens, while K3 works across 1M, so very large files need K3 or another fallback to handle them directly. Relace also offers self-hosted deployment for companies that keep code in-house. K3's weights can be self-hosted too, though that takes a 64+ accelerator cluster.
What Moonshot AI and Relace do
Moonshot AI
Moonshot AI is the Beijing lab behind the Kimi models. Its flagship Kimi K3 launched July 16, 2026 as a 2.8 trillion parameter mixture-of-experts model that activates 16 of 896 experts per token, with native vision and a 1M token context. It is the first open model in the 3T class, and full weights landed on Hugging Face on July 27. The hosted API costs $3 in and $15 out per million tokens, with cached input at $0.30, and it runs through an OpenAI-compatible endpoint, Kimi Code in the terminal, OpenRouter and Cloudflare Workers AI.
Example models: Kimi K3, Kimi K2.6
Full Moonshot AI profileRelace
Relace trains small, fast models that act as tools for coding agents. Its best-known product is Instant Apply: a frontier model writes a lazy edit snippet, and relace-apply-3 merges it into the original file at about 10,000 tokens per second with 128K tokens of input and output. Relace says this runs over 3x faster and cheaper than having the big model rewrite the file. It exposes both a REST endpoint and an OpenAI-compatible one, and the model is also listed on OpenRouter.
Example models: relace-apply-3, Relace agentic search
Full Relace profileShould you choose Moonshot AI or Relace?
Moonshot AI
Choose Moonshot AI for
- The reasoning model behind a coding agent
- Files and contexts beyond Relace's 128K limit
- Visual inputs like screenshots and diagrams
Relace
Choose Relace for
- Fast merges of K3's edit snippets
- Parallel search across large codebases
- Keeping utility models on company hardware
Moonshot AI vs Relace at a glance
| Attribute | Moonshot AI | Relace |
|---|---|---|
| Model access | Open weights, custom license | Specialist models |
| Flagship models | Kimi K3, Kimi K2.6 | relace-apply-3, agentic search |
| Speed | ~33 tok/s on Kimi K3 | ~10,000 tok/s apply |
| Price | $3 in, $15 out (Kimi K3) | 3x+ cheaper than full rewrites |
| Customization | Open weights to fine-tune | Unknown |
| Deployment | API, Kimi Code, OpenRouter | Hosted API or self-hosted |
| Long context | 1M | 128K max |
Frequently asked questions
What is the difference between Moonshot AI and Relace?
Relace supplies small apply, search and compaction models for coding agents. Paired with Kimi K3, it handles the mechanical steps K3 is slow at.
When should I choose Moonshot AI over Relace?
The reasoning model behind a coding agent; Files and contexts beyond Relace's 128K limit; Visual inputs like screenshots and diagrams.
When should I choose Relace over Moonshot AI?
Fast merges of K3's edit snippets; Parallel search across large codebases; Keeping utility models on company hardware.
Is Moonshot AI or Relace cheaper?
Moonshot AI: $3 in, $15 out (Kimi K3). Relace: 3x+ cheaper than full rewrites. The cheaper choice depends on the model and workload.
Which has more context, Moonshot AI or Relace?
Moonshot AI: 1M. Relace: 128K max.
Related comparisons
Running long-horizon agents?
If your agents run past 200K tokens, compare both against Subconscious. Our inference stack treats a long-horizon trace as the primary workload, so speed, cost, and accuracy hold up deep into the trace.