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Venice vs Relace
Venice hosts general models with privacy tiers. Relace builds small coding-agent tools: an apply model near 10,000 tokens per second, agentic search and context compaction.
By The Subconscious Team · Updated
Venice vs Relace: key differences
Relace sells utility models, not general inference. relace-apply-3 merges a frontier model's lazy edit snippet into the original file at about 10,000 tokens per second with 128K tokens of input and output, and Relace says this runs over 3x faster and cheaper than having the big model rewrite the file. Its agentic search explores large codebases in parallel, and its compaction model runs at 50,000 tokens per second. Venice serves the general models those tools would support: GLM 5.3, Kimi K3, DeepSeek V4 and proxied closed models across 370+ options, with zero data retention on open models and 1M context on most current ones.
Deployment is where each has an edge on privacy. Relace can run self-hosted with guided onboarding, so enterprises can keep code in-house. Venice is serverless only but offers TEE inference and end-to-end encryption on select open models, where only an attested enclave can decrypt the prompt. Relace returns an error past 128K tokens, so very large files need a fallback. Venice covers chat, image, audio and video and accepts crypto or DIEM credits, none of which Relace attempts. A coding platform could run a Venice model for reasoning and Relace for apply and search. Picking one alone depends on whether the need is general or code-specific.
What Venice and Relace do
Venice
Venice is a privacy-focused AI platform founded in 2024 by Erik Voorhees, the crypto entrepreneur behind ShapeShift. It pairs a consumer chat app with a developer API that works as a drop-in replacement for OpenAI's chat endpoint and covers text, image, audio and video across 370+ models. Open models such as GLM 5.3, Kimi K3, DeepSeek V4 and Venice's own uncensored fine-tunes run under a private tier with contract-enforced zero data retention, and some add TEE inference or end-to-end encryption, where only an attested enclave can decrypt the prompt. Closed models from Anthropic, OpenAI and Google are proxied under an anonymized tier that hides user identity but leaves prompt content visible to the upstream provider.
Example models: GLM 5.3, Kimi K3, Venice Uncensored 1.2
Full Venice 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 Venice or Relace?
Venice vs Relace at a glance
| Attribute | Venice | Relace |
|---|---|---|
| Model access | Open weights, plus proxied closed models | Specialist models |
| Flagship models | GLM 5.3, Kimi K3, DeepSeek V4 Pro | relace-apply-3, agentic search |
| Speed | Unknown | ~10,000 tok/s apply |
| Price | $0.06–$12 in, $0.28–$60 out per 1M; DIEM staking | 3x+ cheaper than full rewrites |
| Customization | Unknown | Unknown |
| Deployment | Serverless API, consumer app | Hosted API or self-hosted |
| Long context | 1M on most current models | 128K max |
Frequently asked questions
What is the difference between Venice and Relace?
Venice hosts general models with privacy tiers. Relace builds small coding-agent tools: an apply model near 10,000 tokens per second, agentic search and context compaction.
When should I choose Venice over Relace?
General reasoning models with zero retention; Long-context work beyond 128K; Multimodal apps on one key.
When should I choose Relace over Venice?
Fast merges of AI edits into user code; Parallel search across large repos; Self-hosted coding tools kept in-house.
Is Venice or Relace cheaper?
Venice: $0.06–$12 in, $0.28–$60 out per 1M; DIEM staking. Relace: 3x+ cheaper than full rewrites. The cheaper choice depends on the model and workload.
Which has more context, Venice or Relace?
Venice: 1M on most current models. 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.