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Meta vs Relace
Muse Spark is a general agentic model. Relace provides small, fast models for the apply, search and compaction steps that surround it in a coding agent.
By The Subconscious Team · Updated
Meta vs Relace: key differences
Relace does not compete with Meta for the main model slot. It trains small specialist models: relace-apply-3 merges a lazy edit into the original file at about 10,000 tokens per second with 128K of input and output, an agentic search model explores large codebases in parallel, and a compaction model runs at 50,000 tokens per second. Relace says apply runs over 3x faster and cheaper than having the big model rewrite the file. Meta's Muse Spark 1.3 would be that big model, with 1M context at $1.25 in and $4.25 out.
The pair covers each other's gaps. Relace returns an error past 128K tokens, so very large files need a fallback, and Muse Spark's 1M window can take that job. Relace's search and compaction keep the context Muse Spark reads small, trimming input cost. Relace also offers self-hosted deployment for enterprises that keep code in-house, which matters since Meta's API is a hosted preview. Meta additionally ships Muse Glimmer weights, a self-hostable option for the main model.
What Meta and Relace do
Meta
Meta has moved from open Llama releases toward its own closed API. Meta Superintelligence Labs builds the Muse family, and in July 2026 Meta opened a public preview of the Meta Model API with Muse Spark 1.1, a multimodal reasoning model aimed at agentic coding, tool use and computer use. The current lineup runs through Muse Spark 1.3 with a 1M token context. Standard pricing is $1.25 in and $4.25 out per million tokens, with cached input at $0.15, and the endpoint speaks OpenAI Chat Completions, Anthropic Messages and a stateful agentic format.
Example models: Muse Spark 1.3, Muse Glimmer
Full Meta 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 Meta or Relace?
Meta vs Relace at a glance
| Attribute | Meta | Relace |
|---|---|---|
| Model access | Closed API; open Muse Glimmer | Specialist models |
| Flagship models | Muse Spark 1.3, Muse Glimmer | relace-apply-3, agentic search |
| Speed | ~145–233 tok/s on Muse Spark 1.3 | ~10,000 tok/s apply |
| Price | $1.25 in, $4.25 out; Contributor tier cheaper | 3x+ cheaper than full rewrites |
| Customization | Open Muse Glimmer weights to fine-tune | Unknown |
| Deployment | Meta Model API (preview) | Hosted API or self-hosted |
| Long context | 1M | 128K max |
Frequently asked questions
What is the difference between Meta and Relace?
Muse Spark is a general agentic model. Relace provides small, fast models for the apply, search and compaction steps that surround it in a coding agent.
When should I choose Meta over Relace?
The main model that plans and writes code; A 1M context fallback for very large files; Tool use and computer use in one model.
When should I choose Relace over Meta?
Fast merges of lazy edits into source files; Parallel search over large repositories; Self-hosted code tooling for private repos.
Is Meta or Relace cheaper?
Meta: $1.25 in, $4.25 out; Contributor tier cheaper. Relace: 3x+ cheaper than full rewrites. The cheaper choice depends on the model and workload.
Which has more context, Meta or Relace?
Meta: 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.