Cerebras vs StepFun: Token Pricing, Speed & Intelligence
Full comparison of Cerebras and StepFun — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Rates sourced from public APIs — verify before use.
Cerebras
CS-4 wafer-scale — high-throughput inference on GPT OSS 120B and Qwen 3.8 27B
Cerebras uses wafer-scale silicon (the CS-4 chip) to deliver high-throughput inference significantly faster than conventional GPU systems. The Developer tier provides self-serve access; Enterprise tier adds production capacity, custom model weights, fine-tuning, and dedicated support. Available on AWS Marketplace, OpenRouter, HuggingFace, and Vercel. Check cerebras.ai/pricing for current rates and any free-credit offers.
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.
Key metric comparison
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Live token pricing
Strengths and weaknesses
Cerebras
StepFun
Key differentiators
Cerebras CS-4 wafer-scale silicon delivers high-throughput inference on GPT OSS 120B and Qwen 3.8 27B — significantly faster than conventional GPU systems, enabling real-time agentic workflows and interactive AI.
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.
Frequently asked questions
Cerebras FAQs
How fast is Cerebras inference?
Cerebras delivers significantly higher throughput than conventional GPU-based providers on GPT OSS 120B and Qwen 3.8 27B, enabling real-time agentic workflows and interactive AI applications. Check cerebras.ai for current benchmark figures.
What is the Cerebras CS-4 chip?
The CS-4 is Cerebras's latest wafer-scale chip, 58× larger than a GPU. It provides 900,000+ AI cores and massive on-chip SRAM, eliminating the memory bandwidth bottleneck that limits GPU inference speed.
What models does Cerebras support?
Cerebras supports GPT OSS 120B and Qwen 3.8 27B on the Developer tier. Enterprise adds access to all models including production and preview models. Check cerebras.ai/pricing for current rates.
StepFun FAQs
What is StepFun?
StepFun is a Chinese AI startup backed by Tencent, developing the Step model series. Step-5 Preview is the frontier tier and Step-3.7 Flash is the efficient tier, both available via the StepFun API.
How much does the StepFun API cost?
Step-5 Preview is $1.00/$2.70 per 1M input/output tokens. Step-3.7 Flash is $0.20/$1.15 per 1M tokens. See platform.stepfun.com/docs/pricing for current rates.
Does Step-5 support multimodal input?
Yes. Step-5 Preview supports text, image, and video understanding in a single API call, making it suitable for multimodal enterprise applications.
Provider resources
Cerebras — CS-4 wafer-scale — high-throughput inference on GPT OSS 120B and Qwen 3.8 27B
Cerebras uses wafer-scale silicon (the CS-4 chip) to deliver high-throughput inference significantly faster than conventional GPU systems. The Developer tier provides self-serve access; Enterprise tier adds production capacity, custom model weights, fine-tuning, and dedicated support. Available on AWS Marketplace, OpenRouter, HuggingFace, and Vercel. Check cerebras.ai/pricing for current rates and any free-credit offers.
Cerebras CS-4 wafer-scale silicon delivers high-throughput inference on GPT OSS 120B and Qwen 3.8 27B — significantly faster than conventional GPU systems, enabling real-time agentic workflows and interactive AI.
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.
Key strengths compared
Cerebras
- GPT OSS 120B: high-throughput inference on CS-4 wafer-scale silicon
- Qwen 3.8 27B: fast efficient-tier inference
- CS-4 chip: 58× larger than GPUs, purpose-built for AI inference
- Limited model selection vs. Together AI or Fireworks
- No vision or multimodal support
StepFun
- 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)
- Newer entrant — smaller track record than established frontier labs
- Documentation and support primarily in Chinese
Key weaknesses compared
Cerebras
- Limited model selection vs. Together AI or Fireworks
- No vision or multimodal support
- No fine-tuning on Developer tier
StepFun
- Newer entrant — smaller track record than established frontier labs
- Documentation and support primarily in Chinese
- Limited international ecosystem and third-party integrations
Provider category context
Cerebras is a inference api, founded in 2016. StepFun is a frontier lab, founded in 2023. Cerebras as an inference API provider hosts open-weight models — typically offering lower prices for equivalent capability tiers. StepFun as a frontier lab trains and serves proprietary models with capabilities not available elsewhere.
How to choose between them
Choose Cerebras if you need gpt oss 120b: high-throughput inference on cs-4 wafer-scale silicon. Choose StepFun if you need Chinese NLP. For high-volume production workloads, run a cost comparison using the token pricing table above with your actual prompt/completion token ratio — the cheapest provider depends heavily on your input-to-output token ratio.