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Cerebras vs Relace
Relace builds small tool models for coding agents: apply, search, compaction. Cerebras serves general open models at top speed. Complements, not rivals.
By The Subconscious Team · Updated
Cerebras vs Relace: key differences
Relace makes utility models for coding agents. Its relace-apply-3 merges a lazy edit into a file at about 10,000 tokens per second with 128K tokens of input and output, and its compaction model runs at 50,000 tokens per second. Relace argues that specialized small models beat frontier LLMs on these tasks while cutting cost. Cerebras is a general inference host, serving GPT-OSS 120B near 3,000 tokens per second on a wafer-scale chip. The two occupy different slots in an agent: Cerebras for generation, Relace for the mechanical steps around it.
Deployment options differ. Relace offers a hosted API, an OpenRouter listing and self-hosted deployment for enterprises that keep code in-house. Cerebras offers a shared API, dedicated endpoints and partners like OpenRouter, Hugging Face, Vercel and AWS Marketplace. Relace returns an error past 128K tokens, so very large files need a fallback model. Teams building PR review or CI tools can use Relace for search and apply, and route reasoning steps to a general host, Cerebras included when speed is the priority.
What Cerebras and Relace do
Cerebras
Cerebras builds a single chip the size of a silicon wafer, and its inference cloud is the fastest public host on the models it serves. Its developer table lists GPT-OSS 120B near 3,000 tokens per second, about six times Groq on the same weights, at $0.35 in and $0.75 out per million. The public shared catalog is thin, just GPT-OSS 120B and Gemma 4 31B as of August 2026. More model families live on dedicated endpoints and through partners like OpenRouter, Hugging Face, Vercel and AWS Marketplace.
Example models: GPT-OSS 120B, Gemma 4 31B
Full Cerebras 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 Cerebras or Relace?
Cerebras
Choose Cerebras for
- Fast generation for a coding agent's reasoning steps
- Live autocomplete in an editor
- Access through AWS Marketplace or Vercel
Relace
Choose Relace for
- Instant apply of edits into user codebases
- Parallel agentic search across large repos
- Self-hosted coding utilities for code kept in-house
Cerebras vs Relace at a glance
| Attribute | Cerebras | Relace |
|---|---|---|
| Model access | Open weights | Specialist models |
| Flagship models | GPT-OSS 120B, Gemma 4 31B | relace-apply-3, agentic search |
| Speed | ~3,000 tok/s on GPT-OSS 120B | ~10,000 tok/s apply |
| Price | $0.35 in, $0.75 out (GPT-OSS 120B) | 3x+ cheaper than full rewrites |
| Customization | Unknown | Unknown |
| Deployment | Shared API, dedicated, partners | Hosted API or self-hosted |
| Long context | Unknown | 128K max |
Frequently asked questions
What is the difference between Cerebras and Relace?
Relace builds small tool models for coding agents: apply, search, compaction. Cerebras serves general open models at top speed. Complements, not rivals.
When should I choose Cerebras over Relace?
Fast generation for a coding agent's reasoning steps; Live autocomplete in an editor; Access through AWS Marketplace or Vercel.
When should I choose Relace over Cerebras?
Instant apply of edits into user codebases; Parallel agentic search across large repos; Self-hosted coding utilities for code kept in-house.
Is Cerebras or Relace cheaper?
Cerebras: $0.35 in, $0.75 out (GPT-OSS 120B). Relace: 3x+ cheaper than full rewrites. The cheaper choice depends on the model and workload.
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.