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Venice vs OpenScale
Venice is a privacy-first API with 370+ models and uncensored fine-tunes. OpenScale is a zero-retention API selling one open model very cheaply.
By The Subconscious Team · Updated
Venice vs OpenScale: key differences
Venice offers 370+ models across text, image, audio and video, with contract-enforced zero retention on open models, TEE and end-to-end encrypted options, proxied closed models and credits in USD, crypto or DIEM staking. OpenScale serves DeepSeek V4.1 Flash at $0.06 in, $0.003 cached and $0.24 out per million tokens under a 50% promotion, at 200+ tokens per second, with a fast tier at about twice the speed. Chat and Messages requests are never stored. OpenScale's own price table lists Venice at $0.30 in and $1.20 out on the same model.
Both lead with privacy. Venice is the broader platform, with uncensored models, closed-model access and crypto payment. OpenScale is the cheaper option for DeepSeek V4.1 Flash behind coding agents, with conventional prepaid billing.
What Venice and OpenScale 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 profileOpenScale
OpenScale is a Y Combinator-backed inference API built by LatentSpace and run out of Singapore. It sells open models behind one endpoint that speaks Chat Completions, the OpenAI Responses API and Anthropic Messages, so Claude Code, Codex, OpenCode, Cline, GitHub Copilot, Zed and other coding agents can point at it with a base URL and key. Its public catalog today is DeepSeek V4.1 Flash in two versions, standard and fast, both with a 1M token context window and up to 384K output tokens.
Example models: DeepSeek V4.1 Flash
Full OpenScale profileShould you choose Venice or OpenScale?
Venice vs OpenScale at a glance
| Attribute | Venice | OpenScale |
|---|---|---|
| Model access | Open weights, plus proxied closed models | Open weights |
| Flagship models | GLM 5.3, Kimi K3, DeepSeek V4 Pro | DeepSeek V4.1 Flash |
| Speed | Unknown | 200+ tok/s; fast tier ~2x |
| Price | $0.06–$12 in, $0.28–$60 out per 1M; DIEM staking | $0.06 in, $0.24 out (promo, DeepSeek V4.1 Flash) |
| Customization | Unknown | N/A |
| Deployment | Serverless API, consumer app | Serverless API |
| Long context | 1M on most current models | 1M context |
Frequently asked questions
What is the difference between Venice and OpenScale?
Venice is a privacy-first API with 370+ models and uncensored fine-tunes. OpenScale is a zero-retention API selling one open model very cheaply.
When should I choose Venice over OpenScale?
Many models across text, image, audio and video; TEE and end-to-end encrypted inference; Uncensored models and crypto billing.
When should I choose OpenScale over Venice?
Cheaper DeepSeek V4.1 Flash calls; Coding agents over Anthropic or OpenAI formats; Zero retention by default on chat requests.
Is Venice or OpenScale cheaper?
Venice: $0.06–$12 in, $0.28–$60 out per 1M; DIEM staking. OpenScale: $0.06 in, $0.24 out (promo, DeepSeek V4.1 Flash). The cheaper choice depends on the model and workload.
Which has more context, Venice or OpenScale?
Venice: 1M on most current models. OpenScale: 1M context.
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.