Venice vs General Compute

Venice is a privacy-first API for open and proxied closed models. General Compute is a speed-focused host for a short list of open models.

By The Subconscious Team · Updated October 27, 2026

Venice vs General Compute: key differences

Venice covers 370+ models across text, image, audio and video behind an OpenAI-compatible API. Open models like GLM 5.3 and Kimi K3 run with contract-enforced zero data retention, some with TEE or end-to-end encryption, and closed models are proxied under an anonymized tier. Prices run from $0.06 to $12 in per million tokens, payable in USD, crypto or staked DIEM. General Compute serves MiniMax M2.7, DeepSeek V3.2, DeepSeek V3.1 and GPT-OSS 120B from $0.21 in, with ASIC decode that it measured at over 1,000 tokens per second on GPT-OSS 120B in its own benchmark.

Venice is the pick for sensitive prompts, uncensored models, crypto-native billing and access to closed models from one key. General Compute fits teams that want fast output on open models and room to serve their own fine-tuned checkpoints, with dedicated racks on contract for larger volume.

What Venice and General Compute 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 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 Venice or General Compute?

Venice

Choose Venice for

Zero-retention and encrypted inference
Uncensored models
Paying with crypto or staked tokens

General Compute

Choose General Compute for

Fast decode on open models
Serving private fine-tunes
Reserved capacity under contract

Venice vs General Compute at a glance

Attribute	Venice	General Compute
Model access	Open weights, plus proxied closed models	Open weights
Flagship models	GLM 5.3, Kimi K3, DeepSeek V4 Pro	MiniMax M2.7, DeepSeek V3.2, GPT-OSS 120B
Speed	Unknown	1,000+ tok/s on GPT-OSS 120B (vendor)
Price	$0.06–$12 in, $0.28–$60 out per 1M; DIEM staking	$0.21–$0.28 in, $0.38–$1.20 out per 1M
Customization	Unknown	Custom LoRA and fine-tuned checkpoints
Deployment	Serverless API, consumer app	Serverless API, dedicated racks, bare metal
Long context	1M on most current models	192K (MiniMax M2.7)

Frequently asked questions

What is the difference between Venice and General Compute?

Venice is a privacy-first API for open and proxied closed models. General Compute is a speed-focused host for a short list of open models.

When should I choose Venice over General Compute?

Zero-retention and encrypted inference; Uncensored models; Paying with crypto or staked tokens.

When should I choose General Compute over Venice?

Fast decode on open models; Serving private fine-tunes; Reserved capacity under contract.

Is Venice or General Compute cheaper?

Venice: $0.06–$12 in, $0.28–$60 out per 1M; DIEM staking. 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, Venice or General Compute?

Venice: 1M on most current models. 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.

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