StepFun
Step-5 — Chinese frontier LLM with strong multimodal and reasoning capability
StepFun is a Chinese AI startup developing the Step model series. Step-5 Preview is the frontier tier, competing with top Chinese and international models on reasoning and multimodal benchmarks. Step-3.7 Flash is the efficient tier for cost-sensitive workloads. StepFun is backed by Tencent and focuses on enterprise and developer API access.
Step-5 is StepFun's frontier reasoning model with multimodal capability, backed by Tencent — positioned as a competitive alternative to Qwen and ERNIE for Chinese enterprise workloads.
- Step-5 Preview is competitive with top Chinese frontier models on reasoning benchmarks
- Step-3.7 Flash at $0.20/$1.15 per 1M tokens offers strong value for efficient inference
- Multimodal capability on Step-5 (text, image, video understanding)
- Backed by Tencent with enterprise-grade infrastructure
- Newer entrant — smaller track record than established frontier labs
- Documentation and support primarily in Chinese
- Limited international ecosystem and third-party integrations
StepFun Models
| Model | Modality | Tier | Price | Cached /1M | TTFT | Throughput | Context | Intel. | Caps | |
|---|---|---|---|---|---|---|---|---|---|---|
step-3.7-flash Step 3 | Chat | efficient | $0.20/ $1.15 in / out per 1M EST Oct 2026 | — | 300ms | 200 t/s | 256K | 74 | ||
step-5-preview Step 5 | Chat | frontier | $1.00/ $2.70 in / out per 1M EST Oct 2026 | — | 700ms | 90 t/s | 256K | 86 |
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StepFun is a proprietary AI lab that develops and hosts its own large language models. StepFun is a Chinese AI startup developing the Step model series. Step-5 Preview is the frontier tier, competing with top Chinese and international models on reasoning and multimodal benchmarks. Step-3.7 Flash is the efficient tier for cost-sensitive workloads. StepFun is backed by Tencent and focuses on enterprise and developer API access. Common use cases include Reasoning, Chinese NLP, Multimodal tasks, Cost-sensitive inference. Headquartered in Beijing, China, founded 2023. All models are accessible via a REST API compatible with standard OpenAI-style request formats, enabling drop-in integration with most LLM frameworks and orchestration tools.
StepFun vs other LLM providers
StepFun competes with OpenAI, Anthropic, Google, Groq, Together AI, Mistral, Cohere, and other inference API providers across dimensions of price, throughput, latency, context window, and model intelligence. Step-5 is StepFun's frontier reasoning model with multimodal capability, backed by Tencent — positioned as a competitive alternative to Qwen and ERNIE for Chinese enterprise workloads. Use the LLM provider comparison tool to see StepFun token pricing, latency, and throughput side-by-side with any other provider. The full LLM pricing table shows all providers ranked by input token cost, output token cost, and throughput in a single sortable view.
Why choose StepFun?
StepFun stands out because: Step-5 is StepFun's frontier reasoning model with multimodal capability, backed by Tencent — positioned as a competitive alternative to Qwen and ERNIE for Chinese enterprise workloads.. The API currently offers 2 models, spanning 1 frontier, 1 efficient tiers. For teams running high-volume inference workloads, prompt caching and batch API endpoints can reduce effective input token costs by 50–90% — check the context window cost guide for a full breakdown of caching economics.
Understanding StepFun token pricing
StepFun charges separately for input (prompt) and output (completion) tokens, priced per 1M tokens in USD. Output tokens are typically 3–5× more expensive than input tokens due to the compute cost of autoregressive generation. The cheapest {meta.name} input rate is currently $0.20/1M tokens. Prices shown are sourced from StepFun's public pricing page and updated every 15 minutes. Need help estimating your spend? Read the LLM API cost calculator guide — it covers tokens, context windows, prompt caching, and batch discounts with worked examples for RAG, chat history, and document processing workloads.
StepFun context window and model capabilities
Context window size directly affects both capability and cost — every token in the context window is charged as an input token. StepFun's largest context window is 256K tokens. For RAG and document processing workloads, longer context windows enable richer retrieval but increase per-call costs proportionally. Prompt caching — where supported — stores the KV state of repeated prefixes and charges 75–90% less for cache hits, making it the most impactful cost optimization for applications with consistent system prompts or retrieved documents. See the LLM context window cost guide for a full analysis of how context length affects your API bill.
Self-hosted vs managed inference: when StepFun makes sense
Managed inference APIs like StepFun eliminate infrastructure overhead — no GPU provisioning, driver management, or model serving stack to maintain. The trade-off is cost at scale: a single H100 at ~$2.50/hr can serve ~500K tokens/min of Llama 3.3 70B, which at StepFun API rates would cost significantly more per token. The break-even point depends on your request volume, latency requirements, and engineering capacity. For teams processing fewer than ~10M tokens/day, managed APIs are almost always cheaper when total cost of ownership is considered. Above that threshold, self-hosted inference on rented GPU compute typically wins on unit economics. Read the cheapest GPU cloud guide for a full break-even analysis. Historical StepFun token price data is available in the LLM price history charts.
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