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OpenAI vs Relace
Relace makes small tool models for coding agents: apply, search and compaction. It pairs with GPT inside an agent rather than competing with it.
By The Subconscious Team · Updated
OpenAI vs Relace: key differences
Relace's models are tools, not assistants. In its Instant Apply flow, 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 that runs over 3x faster and cheaper than having the big model rewrite the file. Paired with GPT-6 Astra or a GPT-5.6 tier, that shifts the mechanical part of an edit off OpenAI's output pricing and onto a model trained for it.
Beyond apply, Relace offers agentic search that explores large codebases in parallel, a compaction model at 50,000 tokens per second, and source control with retrieval built in. It exposes REST and OpenAI-compatible endpoints and can be self-hosted, which suits enterprises that keep code in-house. OpenAI covers what Relace does not: general reasoning, chat, computer use and a 1.05M window. Size is the practical catch, since Relace returns an error past 128K tokens, so very large files need a fallback such as a GPT model doing the rewrite.
What OpenAI and Relace do
OpenAI
OpenAI runs the most widely adopted closed-model API. Its September 2026 lineup has GPT-6 Astra at the top for computer use, coding and long agentic runs, priced at $10 in and $50 out per million tokens. Below it sits the GPT-5.6 family: Sol for hard professional work, Terra as the balanced default, and Luna for high-volume jobs at $0.20 in and $1.20 out. All of them carry a 1.05M token context window with up to 128K output.
Example models: GPT-6 Astra, GPT-5.6 Terra
Full OpenAI 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 OpenAI or Relace?
OpenAI vs Relace at a glance
| Attribute | OpenAI | Relace |
|---|---|---|
| Model access | Closed, plus open gpt-oss | Specialist models |
| Flagship models | GPT-6 Astra, GPT-5.6 Sol, Terra, Luna | relace-apply-3, agentic search |
| Speed | Fast mode: up to 2.5x at 2x price | ~10,000 tok/s apply |
| Price | $0.20–$10 in, $1.20–$50 out per 1M | 3x+ cheaper than full rewrites |
| Customization | N/A | Unknown |
| Deployment | API, Azure OpenAI, Bedrock | Hosted API or self-hosted |
| Long context | 1.05M; 2x input past 272K | 128K max |
Frequently asked questions
What is the difference between OpenAI and Relace?
Relace makes small tool models for coding agents: apply, search and compaction. It pairs with GPT inside an agent rather than competing with it.
When should I choose OpenAI over Relace?
The main model that plans and writes code; Very large files past Relace's 128K limit; Non-coding tasks and general chat.
When should I choose Relace over OpenAI?
App builders applying AI edits to user codebases; Fast codebase search for PR review and CI fixes; Self-hosted code tooling for in-house repos.
Is OpenAI or Relace cheaper?
OpenAI: $0.20–$10 in, $1.20–$50 out per 1M. Relace: 3x+ cheaper than full rewrites. The cheaper choice depends on the model and workload.
Which has more context, OpenAI or Relace?
OpenAI: 1.05M; 2x input past 272K. 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.