vs
xAI vs Relace
Grok is a general closed model that can plan and write code. Relace supplies small, fast models for the apply, search and compaction steps around it.
By The Subconscious Team · Updated
xAI vs Relace: key differences
Relace builds tools, not a general model. Its relace-apply-3 merges a lazy edit snippet into the original file at about 10,000 tokens per second with 128K tokens of input and output, its agentic search explores large codebases in parallel, and a compaction model runs at 50,000 tokens per second. Relace says the apply step runs over 3x faster and cheaper than having the big model rewrite the file. xAI supplies that big model: Grok 4.7, or grok-build for coding, with Grok 4.20 offering 1M context.
In a coding agent the two fit together. Grok plans and writes the edit, Relace applies it, and Relace's search and compaction keep the context Grok sees small, which matters because xAI bills the whole request at double once a prompt hits 200K tokens. Relace returns an error past 128K tokens, so very large files need a fallback, and Grok 4.20's 1M window can play that role. Relace also offers self-hosting for enterprises that keep code in-house, which xAI does not.
What xAI and Relace do
xAI
xAI sells the Grok models through its own API and Amazon Bedrock. SpaceX acquired it in February 2026; it became SpaceXAI in July and SpaceX Super Intelligence (SpaceXSI) in October, the name its docs now use. Grok 4.7, released September 21, 2026, is the current flagship, pitched for coding, agentic tasks and knowledge work. It has a 500K context window and costs $2 in and $6 out per million tokens under 200K prompt tokens. Older Grok 4.20 and 4.3 models keep a 1M window at $1.25 in and $2.50 out, which is aggressive for that capability class.
Example models: Grok 4.7, Grok 4.20
Full xAI profileRelace
Relace started out training small, fast models that act as tools for coding agents, and by October 2026 its homepage led with open-model inference instead. It says it served 30 trillion tokens in the 30 days to late October and ranks among the top providers on OpenRouter. The hosted catalog covers DeepSeek V4 Pro, V4 Flash and V4.1 Flash, Kimi K3, GLM 5.3, GLM 5.3 Flash and GLM 5.2 over OpenAI and Anthropic compatible APIs, billed per token with no subscription. Prices run from $0.07 in and $0.14 out per million tokens on DeepSeek V4 Flash to $2.00 in and $14.00 out on Kimi K3, and Relace says it reprices often.
Example models: DeepSeek V4.1 Flash, Kimi K3, GLM 5.3, relace-apply-3
Full Relace profileShould you choose xAI or Relace?
xAI vs Relace at a glance
| Attribute | xAI | Relace |
|---|---|---|
| Model access | Closed | Open weights, plus specialist coding models |
| Flagship models | Grok 4.7, Grok 4.20, grok-build | DeepSeek V4.1 Flash, Kimi K3, GLM 5.3, relace-apply-3 |
| Speed | ~54 tok/s on Grok 4.6 | ~10,000 tok/s apply |
| Price | $2 in, $6 out (Grok 4.7); 2x past 200K | $0.07 in, $0.50 out (DeepSeek V4.1 Flash) |
| Customization | Unknown | Unknown |
| Deployment | First-party API, Amazon Bedrock | Hosted API, OpenRouter or self-hosted |
| Long context | 500K (4.7), 1M (4.20, 4.3) | 128K on apply; varies by open model |
Frequently asked questions
What is the difference between xAI and Relace?
Grok is a general closed model that can plan and write code. Relace supplies small, fast models for the apply, search and compaction steps around it.
When should I choose xAI over Relace?
The reasoning model that writes code edits; Large-file fallback with 1M context on Grok 4.20; Agents that mix coding with live data lookups.
When should I choose Relace over xAI?
Fast merges of lazy edits into files; Parallel search over large repositories; Self-hosted code tooling for in-house repos.
Is xAI or Relace cheaper?
xAI: $2 in, $6 out (Grok 4.7); 2x past 200K. Relace: $0.07 in, $0.50 out (DeepSeek V4.1 Flash). The cheaper choice depends on the model and workload.
Which has more context, xAI or Relace?
xAI: 500K (4.7), 1M (4.20, 4.3). Relace: 128K on apply; varies by open model.
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.