Venice vs Thinking Machines

Venice is a general inference API with privacy tiers. Thinking Machines sells Tinker, a post-training API, and serves only its own Inkling models on a beta serverless tier.

By The Subconscious Team · Updated September 30, 2026

Venice vs Thinking Machines: key differences

The overlap is narrow. Venice serves 370+ models for production traffic, from GLM 5.3 and Kimi K3 to proxied Claude and GPT, with zero retention on open models and 1M context on most current ones. Thinking Machines is mainly a training company. Tinker exposes four low-level calls, forward_backward, optim_step, sample and save_state, so teams write their own SFT or RL loops on models like Qwen3.5, GLM-5.3, Kimi K2.6 and gpt-oss while the lab runs the GPUs. Training uses LoRA only. Its beta serverless API covers just Inkling and Inkling-Small, with Inkling at $1.00 in and $4.05 out, and checkpoint sampling is scoped to testing and low internal traffic.

Thinking Machines wins on customization and model design. Its Apache 2.0 Inkling models, 975B total with 41B active and 276B with 12B active, take text, image and audio input with up to 1M context. Venice offers no fine-tuning but covers far more models and modalities, including image and video generation, and adds uncensored fine-tunes and crypto or DIEM billing. A research team building a task-specialized model through RL belongs on Tinker. A product team that needs private, user-facing inference across many models today belongs on Venice. The two could pair, though Venice does not list hosting for customer fine-tunes.

What Venice and Thinking Machines 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

Thinking Machines

Thinking Machines Lab is the San Francisco lab Mira Murati, formerly CTO of OpenAI, founded in February 2025, with OpenAI co-founder John Schulman as chief scientist. It raised about $2 billion at a $12 billion valuation in July 2025 in a round led by Andreessen Horowitz, and in March 2026 signed a multi-year Nvidia deal for one gigawatt of Vera Rubin capacity. Its main developer product is Tinker, an API for post-training open-weight models that launched in October 2025 and is now generally available. Tinker exposes four low-level calls, forward_backward, optim_step, sample and save_state, so teams write their own supervised or reinforcement learning loops while Thinking Machines runs the distributed GPU work. Training uses LoRA adapters rather than full weight updates.

Example models: Inkling, Inkling-Small, Qwen3.5, Kimi K2.6

Full Thinking Machines profile

Should you choose Venice or Thinking Machines?

Venice

Choose Venice for

User-facing inference across 370+ models
Private handling of sensitive prompts
Image, audio and video generation on one API

Thinking Machines

Choose Thinking Machines for

Custom SFT or RL loops without managing clusters
LoRA post-training on large MoE models
Evaluating the Apache 2.0 Inkling models

Venice vs Thinking Machines at a glance

Attribute	Venice	Thinking Machines
Model access	Open weights, plus proxied closed models	Open weights
Flagship models	GLM 5.3, Kimi K3, DeepSeek V4 Pro	Inkling, Inkling-Small
Speed	Unknown	Unknown
Price	$0.06–$12 in, $0.28–$60 out per 1M; DIEM staking	Per 1M tokens by prefill, sample, train; Inkling $1.00 in, $4.05 out
Customization	Unknown	LoRA SFT and RL via Tinker
Deployment	Serverless API, consumer app	Training API, beta serverless (Inkling only)
Long context	1M on most current models	Inkling up to 1M; Tinker 32K–256K

Frequently asked questions

What is the difference between Venice and Thinking Machines?

Venice is a general inference API with privacy tiers. Thinking Machines sells Tinker, a post-training API, and serves only its own Inkling models on a beta serverless tier.

When should I choose Venice over Thinking Machines?

User-facing inference across 370+ models; Private handling of sensitive prompts; Image, audio and video generation on one API.

When should I choose Thinking Machines over Venice?

Custom SFT or RL loops without managing clusters; LoRA post-training on large MoE models; Evaluating the Apache 2.0 Inkling models.

Is Venice or Thinking Machines cheaper?

Venice: $0.06–$12 in, $0.28–$60 out per 1M; DIEM staking. Thinking Machines: Per 1M tokens by prefill, sample, train; Inkling $1.00 in, $4.05 out. The cheaper choice depends on the model and workload.

Which has more context, Venice or Thinking Machines?

Venice: 1M on most current models. Thinking Machines: Inkling up to 1M; Tinker 32K–256K.

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 Venice Subconscious vs Thinking Machines