SiliconFlow

SiliconFlow is a China-based AI infrastructure company whose flagship product, SiliconCloud, gives developers a single OpenAI-compatible API to more than 200 models across text, image, video, audio, embeddings, and reranking. It was founded in 2023 by Yuan Jinhui, the former chief executive of the deep-learning framework OneFlow, and Pan Yang, and it is best known as the leading commercial host for Chinese open-source model families: DeepSeek, Qwen, GLM, Kimi, MiniMax, and Step. The platform runs on its own SiliconLLM inference engine across both NVIDIA GPUs and Huawei Ascend chips, and it closed a Series A led by Alibaba Cloud in 2025. If you have tried to call a Chinese frontier model from outside China and hit a wall, SiliconFlow is usually the platform that solves it.

One key reaches reasoning and chat models like DeepSeek and Qwen, image models including FLUX and the free Kolors, video through Wan2.2, speech with CosyVoice2 and Fish-Speech, and a full set of embedding and reranker models for retrieval. New accounts get one dollar of free credit plus several models that are permanently free, pricing runs from about $0.10 per million tokens for small models, and a managed pipeline fine-tunes Qwen2.5 variants. Because the API is OpenAI-compatible, most teams point an existing client at SiliconFlow's base URL and run unchanged code. The important caveat sits in the same breath as the pitch: inference happens on Chinese infrastructure, real-name verification became mandatory in May 2026, and there is no SOC2 or GDPR documentation, so data residency is a real decision rather than a footnote.

SiliconFlow at a glance, June 2026

SiliconCloud is a model-as-a-service platform, not a chat app or an IDE. You sign up, get an API key, and call models over HTTP. The headline number is breadth: more than 200 models under one account. The language catalog is where it stands apart, carrying DeepSeek V4, the Qwen 3.5 family from 9B up to large mixture-of-experts variants, GLM 5.x, Kimi from Moonshot AI, MiniMax, and Step, many of which are difficult or impossible to reach through a Western provider. Around the language models sit BAAI and Qwen embeddings, Qwen3 rerankers, FLUX and Kolors for images, Wan2.2 for video, and CosyVoice2 and Fish-Speech for audio.

Two operational details shape how you use it. First, there are two API endpoints, an international one on the .com domain and a mainland one on .cn, and the platform has been steering users toward .cn. Second, the same OpenAI-compatible surface means the migration cost from another provider is mostly changing a base URL and a key. Both serverless pay-as-you-go and dedicated reserved endpoints are available, and fine-tuning is offered for several Qwen2.5 instruct variants.

What SiliconFlow is actually good at

The platform's real moat is Chinese model access at scale. Its breakout came in January 2025, when DeepSeek-R1 and V3 went viral worldwide and SiliconFlow was the first platform to serve both at large scale on Huawei Ascend hardware in addition to NVIDIA. That moment turned into money and validation.

"Our business has grown explosively since the beginning of this year with the rise of open-source large models, including Alibaba's Qwen and DeepSeek, as well as soaring demand for AI inference power." Yuan Jinhui, SiliconFlow founder, Yicai Global, June 10, 2025.

Beyond model breadth, SiliconFlow is genuinely cheap on its smaller models, fully multimodal under one bill, and friendly to existing code through its OpenAI compatibility. Teams running retrieval pipelines can get chat, embeddings, and reranking from a single vendor, and the permanently free small models plus a dollar of starting credit make evaluation almost frictionless. For anyone who specifically needs DeepSeek inference that does not depend on NVIDIA hardware, SiliconFlow's Huawei Cloud partnership makes it the documented primary option.

Where SiliconFlow breaks, the limits users keep hitting

The most practical pain is rate limiting. The free-tier caps are strict and enforced hard, and integration frameworks that fire concurrent requests can trip account-level blocks with little warning.

"To avoid being banned, set the maximum request per second for siliconflow's embedding. The system has not imposed any restrictions and I can see a large number of blocked requests from the backend logs." ukhack, GitHub Dify issue, September 2024.

The bigger blocker for non-Chinese users is identity. Since May 2026, real-name verification is mandatory, and unverified accounts cannot add funds or request invoices. Verification accepts specific Chinese identity documents, and standard foreign passports are not accepted online, so many Western developers can evaluate on free credit but cannot actually scale up without contacting support. On top of that, the model roster churns aggressively. Through 2025 and into 2026 the release notes show frequent deprecations and several previously free models moving to paid, which is a risk if you pin a workflow to one model. Independent benchmarks have also flagged missing JSON mode on some models, a real problem for structured-output pipelines, and the platform's privacy claims are not backed by any independent audit. None of this is hidden, but all of it needs planning.

SiliconFlow vs Together AI vs DeepInfra

Against Together AI, the trade is geography and catalog. Together runs its own GPU clusters in the United States, posts strong throughput around 900 tokens per second, supports fine-tuning, and carries no real-name or data-residency friction, but it does not offer the Chinese exclusives. SiliconFlow is frequently the only practical way to reach GLM, Kimi, MiniMax, and Step at API scale. Against DeepInfra, the contrast is price versus features: DeepInfra is US-based, positions as a low-cost leader, and can undercut SiliconFlow on shared models like DeepSeek and Qwen, but it does not offer fine-tuning or the Chinese-family catalog. Fireworks AI is a third reference point, optimized for very low time-to-first-token on Western open models with prompt caching, but again without Chinese models or SiliconFlow's image, video, and audio breadth. The pattern is consistent. For Western open models with low regulatory friction, a US host or a routing layer like OpenRouter is simpler. For Chinese models and multimodal breadth, SiliconFlow wins by default.

Is SiliconFlow actually cheaper?

For its smaller and mid-size models, yes. Qwen3.5-9B runs around $0.10 per million input tokens, and even a large model like DeepSeek-V4-Pro sits at roughly $1.60 input and $3.14 output per million, with cached input far cheaper. Image generation starts at half a cent for Z-Image-Turbo and runs to four cents for FLUX 1.1 pro, and a Wan2.2 video is about $0.29. The free models and the dollar of starting credit mean evaluation costs nothing, and rate-limit tiers from L0 to L5 raise your throughput automatically as monthly spend grows. The honest qualifier is that on the exact models you can also get elsewhere, a US competitor such as DeepInfra may match or beat the blended price without the verification and residency overhead, so the savings argument is strongest when you actually need the Chinese catalog.

Best use cases (and when to skip it)

SiliconFlow is a strong default for developers in China or building China-facing products, for anyone evaluating or running DeepSeek, Qwen, GLM, Kimi, or MiniMax at API scale, for multimodal pipelines that want text, image, audio, and retrieval from one vendor, and for teams that specifically need Ascend-hosted inference. Skip it when data residency outside China is a requirement, because all inference runs on Chinese infrastructure with no Western compliance documentation. Skip it if you cannot complete real-name verification, since that caps account functionality. And if you only need Western open models, a US host or a routing layer avoids the regulatory questions entirely for similar money.

Getting started with SiliconFlow

Sign up on the SiliconCloud console, claim the dollar of free credit, and create an API key. Because the API mirrors OpenAI's, you usually only change the base URL to SiliconFlow's endpoint and drop in your key, then call a model by name. Start on the permanently free models or a cheap Qwen variant to validate your integration, add request throttling early to avoid tripping the rate limits, and plan for real-name verification before you intend to add funds or move to production volume. Teams that pin to a specific model should watch the release notes for deprecations.