Relace vs General Compute

Relace trains small tool models for coding agents. General Compute runs open chat models on ASIC decode for fast agent loops.

By The Subconscious Team · Updated October 27, 2026

Relace vs General Compute: key differences

Relace's relace-apply-3 merges lazy edit snippets into files at about 10,000 tokens per second with up to 128K tokens of input and output, and its compaction model runs at 50,000 tokens per second. It also offers agentic codebase search and a self-hosted option. General Compute serves MiniMax M2.7, DeepSeek V3.2, DeepSeek V3.1 and GPT-OSS 120B through an OpenAI-compatible API in us-west-2, at $0.21 to $0.28 in per million tokens. It uses GPUs for prefill and ASICs for decode, and in its own benchmark measured over 1,000 tokens per second on GPT-OSS 120B.

Relace has no general-purpose model serving, so it fits beside a main model. General Compute can be that main model when decode speed matters, and Relace can take the apply, search and compaction steps. Relace's self-hosted deployment suits teams that keep code in-house, and General Compute's dedicated racks suit teams that need reserved throughput.

What Relace and General Compute 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

General Compute

General Compute is a San Francisco inference cloud built on alternative chips. It splits each request across hardware, with NVIDIA GPUs handling prefill and ASICs from Cerebras, SambaNova, Positron AI and d-Matrix handling decode. In its own open benchmark on GPT-OSS 120B it measured 1,025 to 1,234 output tokens per second against 100 to 121 on an unnamed GPU cloud, and up to 16.2x faster end to end. Founders Finn Puklowski and Jason Goodison raised a $15M seed led by FUSE, an up to $400M debt facility from Upper90 and, on October 27, 2026, a $100M Series A co-led by Blackbird and Abstract.

Example models: MiniMax M2.7, DeepSeek V3.2, GPT-OSS 120B

Full General Compute profile

Should you choose Relace or General Compute?

Relace

Choose Relace for

Instant apply on lazy edit snippets
Parallel search over large codebases
Self-hosted coding tools

General Compute

Choose General Compute for

A fast main model for coding agents
High decode speed on GPT-OSS 120B
Dedicated racks with throughput SLAs

Relace vs General Compute at a glance

Attribute	Relace	General Compute
Model access	Specialist models	Open weights
Flagship models	relace-apply-3, agentic search	MiniMax M2.7, DeepSeek V3.2, GPT-OSS 120B
Speed	~10,000 tok/s apply	1,000+ tok/s on GPT-OSS 120B (vendor)
Price	3x+ cheaper than full rewrites	$0.21–$0.28 in, $0.38–$1.20 out per 1M
Customization	Unknown	Custom LoRA and fine-tuned checkpoints
Deployment	Hosted API or self-hosted	Serverless API, dedicated racks, bare metal
Long context	128K max	192K (MiniMax M2.7)

Frequently asked questions

What is the difference between Relace and General Compute?

Relace trains small tool models for coding agents. General Compute runs open chat models on ASIC decode for fast agent loops.

When should I choose Relace over General Compute?

Instant apply on lazy edit snippets; Parallel search over large codebases; Self-hosted coding tools.

When should I choose General Compute over Relace?

A fast main model for coding agents; High decode speed on GPT-OSS 120B; Dedicated racks with throughput SLAs.

Is Relace or General Compute cheaper?

Relace: 3x+ cheaper than full rewrites. General Compute: $0.21–$0.28 in, $0.38–$1.20 out per 1M. The cheaper choice depends on the model and workload.

Which has more context, Relace or General Compute?

Relace: 128K max. General Compute: 192K (MiniMax M2.7).

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.

Subconscious vs Relace Subconscious vs General Compute