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Venice vs StepFun
StepFun is a Shanghai lab selling its own efficient multimodal models, many under Apache 2.0. Venice is a privacy-first host serving hundreds of other labs' models.
By The Subconscious Team · Updated
Venice vs StepFun: key differences
StepFun makes models; Venice hosts them. StepFun's Step 3.7 Flash is a 198B mixture-of-experts vision-language model with 11B active parameters, 256K context, selectable reasoning levels and tool use, released under Apache 2.0 and priced at $0.20 in and $1.15 out on StepFun's API, at about 128 tokens per second. The earlier Step3 used Multi-Matrix Factorization Attention and Attention-FFN Disaggregation to keep decoding cheap. Venice serves 370+ models including GLM 5.3, Kimi K3, DeepSeek V4 and its own uncensored fine-tunes, with 1M context on most current models and no published speed figures.
Data handling is the sharpest contrast. StepFun's first-party inference is China-hosted with thin Western distribution and support, though OpenRouter carries Step 3.7 Flash and the open weights run on vLLM and SGLang for self-hosting. Venice offers contract-enforced zero retention on open models, TEE and end-to-end encryption on some, and payment in USD, crypto or DIEM credits. StepFun's open weights can be fine-tuned; Venice offers no fine-tuning. StepFun trails frontier models on hard multimodal reasoning. Teams that want cheap image and video understanding they can self-host lean StepFun. Teams that want private access to many models without running servers lean Venice.
What Venice and StepFun 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 profileStepFun
StepFun is a Shanghai AI lab known for efficient multimodal models, with a mix of proprietary API models and open-weight releases. Its current workhorse, Step 3.7 Flash, came out in May 2026 as a 198B mixture-of-experts vision-language model with only 11B active parameters. It has 256K context, selectable reasoning levels, tool use and structured outputs, and it ships under Apache 2.0. StepFun's own API prices it at $0.20 in and $1.15 out per million tokens, and OpenRouter carries it too.
Example models: Step 3.7 Flash, Step3
Full StepFun profileShould you choose Venice or StepFun?
Venice vs StepFun at a glance
| Attribute | Venice | StepFun |
|---|---|---|
| Model access | Open weights, plus proxied closed models | Open (Apache 2.0) and API models |
| Flagship models | GLM 5.3, Kimi K3, DeepSeek V4 Pro | Step 3.7 Flash, Step3 |
| Speed | Unknown | ~128 tok/s on Step 3.7 Flash |
| Price | $0.06–$12 in, $0.28–$60 out per 1M; DIEM staking | $0.20 in, $1.15 out (Step 3.7 Flash) |
| Customization | Unknown | Open weights to fine-tune |
| Deployment | Serverless API, consumer app | First-party API, OpenRouter |
| Long context | 1M on most current models | 256K |
Frequently asked questions
What is the difference between Venice and StepFun?
StepFun is a Shanghai lab selling its own efficient multimodal models, many under Apache 2.0. Venice is a privacy-first host serving hundreds of other labs' models.
When should I choose Venice over StepFun?
Private hosted access to many labs' models; 1M context without self-hosting; Uncensored models and crypto billing.
When should I choose StepFun over Venice?
Low-cost vision and video understanding; Apache 2.0 weights to self-host or fine-tune; Small-active-parameter MoE for cheap serving.
Is Venice or StepFun cheaper?
Venice: $0.06–$12 in, $0.28–$60 out per 1M; DIEM staking. StepFun: $0.20 in, $1.15 out (Step 3.7 Flash). The cheaper choice depends on the model and workload.
Which has more context, Venice or StepFun?
Venice: 1M on most current models. StepFun: 256K.
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