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DeepSeek vs Relace
Relace's models merge edits, search repos and compact context for coding agents. DeepSeek supplies a cheap model to drive the agent. Complements, not rivals.
By The Subconscious Team · Updated
DeepSeek vs Relace: key differences
Relace makes utility models, not general ones. Its relace-apply-3 merges a lazy edit into the original file at about 10,000 tokens per second, with 128K tokens of input and output, and Relace says that is over 3x faster and cheaper than a full rewrite by the big model. Agentic search answers codebase questions in seconds, and a compaction model runs at 50,000 tokens per second. DeepSeek sells the model that would generate the edits and plan the work, with 1M context and some of the lowest first-party prices anywhere.
Where they meet is context size. DeepSeek handles 1M tokens and up to 384K output, while relace-apply-3 returns an error past 128K, so very large files fall back to the main model. Relace offers self-hosted deployment for enterprises that keep code in-house, and DeepSeek's MIT weights can also be self-hosted, which together allow a fully in-house coding agent. On the hosted route, DeepSeek stores data in China, a point to weigh for proprietary code.
What DeepSeek and Relace do
DeepSeek
DeepSeek is the Chinese lab whose open-weight models reset price expectations for the whole market. Its API now serves two models, both with 1M context and 384K max output. V4.1 Flash shipped September 10, 2026 with built-in image understanding at $0.30 in and $1.20 out at peak. V4 Pro, generally available since August 13, costs $1.32 in and $3.96 out at peak. Cache hits cost a few cents per million or less, and the weights ship on Hugging Face under an MIT license.
Example models: DeepSeek V4.1 Flash, DeepSeek V4 Pro
Full DeepSeek 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 DeepSeek or Relace?
DeepSeek vs Relace at a glance
| Attribute | DeepSeek | Relace |
|---|---|---|
| Model access | Open weights (MIT) | Specialist models |
| Flagship models | DeepSeek V4.1 Flash, V4 Pro | relace-apply-3, agentic search |
| Speed | ~35 tok/s on V4 Pro | ~10,000 tok/s apply |
| Price | Off-peak hours at half price | 3x+ cheaper than full rewrites |
| Customization | Open weights to fine-tune | Unknown |
| Deployment | First-party API, Hugging Face weights | Hosted API or self-hosted |
| Long context | 1M, 384K max output | 128K max |
Frequently asked questions
What is the difference between DeepSeek and Relace?
Relace's models merge edits, search repos and compact context for coding agents. DeepSeek supplies a cheap model to drive the agent. Complements, not rivals.
When should I choose DeepSeek over Relace?
Cheap planning and code generation in an agent; Files and contexts past 128K tokens; Self-hosted main model under MIT.
When should I choose Relace over DeepSeek?
Fast merges of edit snippets into files; Parallel search across large codebases; Context compaction at 50,000 tokens per second.
Is DeepSeek or Relace cheaper?
DeepSeek: Off-peak hours at half price. Relace: 3x+ cheaper than full rewrites. The cheaper choice depends on the model and workload.
Which has more context, DeepSeek or Relace?
DeepSeek: 1M, 384K max output. 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.