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Chutes vs StepFun: Token Pricing, Speed & Intelligence

Full comparison of Chutes and StepFun — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Rates sourced from public APIs — verify before use.

Chutes

Open-weight model inference on TEE GPUs

Chutes (chutes.ai) is an inference platform for open-weight models. Workloads run inside Trusted Execution Environments (TEEs) on GPU hardware, with cryptographic attestation of model and input confidentiality. The API is OpenAI-compatible and pricing is per token with no minimum commitment.

Open-weight inferencePrivacy-sensitive workloadsServerless LLM
Open-weight hostHosts open weights

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.

ReasoningChinese NLPMultimodal tasksCost-sensitive inference
Proprietary models

Key metric comparison

Cheapest input ($/1M)

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Cheapest output ($/1M)

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Peak throughput

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Best latency (TTFT)

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Intelligence score

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Context window

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Live token pricing

Strengths and weaknesses

Chutes

TEE-backed inference with cryptographic attestation
OpenAI-compatible API
Serverless, per-token pricing
Supports open-weight models including Llama, Mistral, Qwen, and DeepSeek

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)
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

Key differentiators

Chutes

Open-weight inference platform running workloads inside TEEs with cryptographic attestation.

StepFun

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

Chutes FAQs

What is Chutes?

Chutes is an inference platform for open-weight models. Workloads run inside Trusted Execution Environments (TEEs) on GPU hardware. See chutes.ai for details.

What does TEE inference mean on Chutes?

TEE (Trusted Execution Environment) inference runs model workloads inside a hardware-isolated enclave. Chutes provides cryptographic attestation of this isolation. See their documentation at docs.chutes.ai.

What models does Chutes offer?

Chutes hosts open-weight models including families from Llama, Mistral, Qwen, and DeepSeek. See chutes.ai for the current catalog.

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

Chutes — Open-weight model inference on TEE GPUs

Chutes (chutes.ai) is an inference platform for open-weight models. Workloads run inside Trusted Execution Environments (TEEs) on GPU hardware, with cryptographic attestation of model and input confidentiality. The API is OpenAI-compatible and pricing is per token with no minimum commitment.

Open-weight inference platform running workloads inside TEEs with cryptographic attestation.

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

Chutes

  • TEE-backed inference with cryptographic attestation
  • OpenAI-compatible API
  • Serverless, per-token pricing

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

Chutes

    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

    Chutes is a open source host, founded in 2024. StepFun is a frontier lab, founded in 2023. The category difference means these providers serve partially overlapping use cases — compare the model lists and pricing tables above to find the best fit for your specific workload.

    How to choose between them

    Choose Chutes if you need tee-backed inference with cryptographic attestation. 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.