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Relace vs Particle.AI
Particle.AI serves cheap Flash-class general models with 1M context; Relace serves specialist apply and search models capped at 128K. A cheap main model and a fast tool layer.
By The Subconscious Team · Updated
Relace vs Particle.AI: key differences
Particle.AI sells general text models cheaply. Through Vercel AI Gateway it lists DeepSeek V4.1 Flash at $0.25 in and $1 out, GLM 5.3 Flash at $0.10 in and $0.40 out, and DeepSeek V4 Flash 0731 at $0.14 in and $0.28 out, all with 1M context and $0.03 cache reads. Relace sells coding utilities. relace-apply-3 merges edits at about 10,000 tokens per second with a 128K cap, and Relace also offers agentic codebase search and a compaction model at 50,000 tokens per second.
A budget coding agent could route its reasoning to a Flash model on Particle and its file edits to Relace. Particle covers large inputs that Relace cannot, and Relace cuts the output a general model would spend rewriting whole files. Particle is a very early company with a tiny catalog and some slow listings, like 3.5 seconds of latency on DeepSeek V4.1 Flash. Relace is a narrow point solution with no general-purpose serving but offers self-hosted deployment. Choose by role, not by price.
What Relace and Particle.AI do
Relace
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 profile
Particle.AI
Particle AI is an early San Francisco infrastructure startup with a mission to make intelligence as cheap and abundant as electricity. The team works on post-training, inference optimization and distributed systems, all aimed at pushing down cost per unit of intelligence. It is still hiring its founding team and works fully in person. Public detail about funding and founders is thin as of this writing.
Example models: DeepSeek V4.1 Flash, GLM 5.3 Flash
Full Particle.AI profileShould you choose Relace or Particle.AI?
Relace
Choose Relace for
- Fast file edits and code search
- Self-hosted tooling for private repos
- Offloading chores from the main model
Particle.AI
Choose Particle.AI for
- Cheap general reasoning with 1M context
- Flash-class models through Vercel AI Gateway
- Inputs larger than 128K tokens
Relace vs Particle.AI at a glance
| Attribute | Relace |  Particle.AI |
|---|---|---|
| Model access | Specialist models | Open weights |
| Flagship models | relace-apply-3, agentic search | DeepSeek V4.1 Flash, GLM 5.3 Flash |
| Speed | ~10,000 tok/s apply | ~157 tok/s on DeepSeek V4.1 Flash |
| Price | 3x+ cheaper than full rewrites | $0.10 in, $0.40 out (GLM 5.3 Flash) |
| Customization | Unknown | Unknown |
| Deployment | Hosted API or self-hosted | Via Vercel AI Gateway |
| Long context | 128K max | 1M |
Frequently asked questions
What is the difference between Relace and Particle.AI?
Particle.AI serves cheap Flash-class general models with 1M context; Relace serves specialist apply and search models capped at 128K. A cheap main model and a fast tool layer.
When should I choose Relace over Particle.AI?
Fast file edits and code search; Self-hosted tooling for private repos; Offloading chores from the main model.
When should I choose Particle.AI over Relace?
Cheap general reasoning with 1M context; Flash-class models through Vercel AI Gateway; Inputs larger than 128K tokens.
Is Relace or Particle.AI cheaper?
Relace: 3x+ cheaper than full rewrites. Particle.AI: $0.10 in, $0.40 out (GLM 5.3 Flash). The cheaper choice depends on the model and workload.
Which has more context, Relace or Particle.AI?
Relace: 128K max. Particle.AI: 1M.
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