Hyperbolic
Open-source inference marketplace — Llama, DeepSeek R1, and more
Hyperbolic provides a marketplace for open-source model inference, hosting Llama 3.3, DeepSeek R1, and other popular models at competitive prices. Their platform emphasises accessibility and affordability, making frontier open-weight models available to developers and researchers at low cost.
One of the most affordable inference marketplaces for open-weight models — ideal for researchers and cost-sensitive workloads.
- Among the lowest prices for open-weight model inference
- DeepSeek R1 and Llama 3.3 available at competitive rates
- Marketplace model — broad model selection
- OpenAI-compatible API
- Good for research and experimentation
- Less established reliability than larger providers
- Throughput lower than Groq/Cerebras for speed-critical apps
- No fine-tuning support
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Hyperbolic — Frequently Asked Questions
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Full comparison tableHyperbolic LLM pricing overview
Hyperbolic is an inference API provider that hosts open-weight and third-party large language models. Hyperbolic provides a marketplace for open-source model inference, hosting Llama 3.3, DeepSeek R1, and other popular models at competitive prices. Their platform emphasises accessibility and affordability, making frontier open-weight models available to developers and researchers at low cost. Common use cases include Cost-efficiency, Research, Open-source, Experimentation. Headquartered in San Francisco, CA, 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.
Hyperbolic vs other LLM providers
Hyperbolic 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. One of the most affordable inference marketplaces for open-weight models — ideal for researchers and cost-sensitive workloads. Use the LLM provider comparison tool to see Hyperbolic 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 Hyperbolic?
Hyperbolic's key strengths are: Among the lowest prices for open-weight model inference; DeepSeek R1 and Llama 3.3 available at competitive rates; Marketplace model — broad model selection. Limitations to consider: Less established reliability than larger providers; Throughput lower than Groq/Cerebras for speed-critical apps. 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 Hyperbolic token pricing
Hyperbolic 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. Prices shown are sourced from Hyperbolic's public pricing page and updated daily. 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.
Hyperbolic 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. 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 Hyperbolic makes sense
Managed inference APIs like Hyperbolic 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 Hyperbolic 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 Hyperbolic token price data is available in the LLM price history charts.