Replicate
Run any open-source AI model via API — FLUX 1.1 Pro, Llama 4, video, audio, and custom models
Replicate is a platform for running open-source AI models via a simple API. It hosts thousands of community models including FLUX 1.1 Pro, Llama 4, Stable Diffusion, Whisper, and video generation models. Pay per prediction with no infrastructure to manage. The 2026 catalog has expanded significantly with FLUX 1.1 Pro Ultra, Kling v2.1, and Llama 4 Scout/Maverick. Custom model deployment via Cog remains a core differentiator.
A broad catalog of community AI models — if a model exists on Hugging Face, it's likely on Replicate. Strong coverage for image, video, and audio model access, plus custom model deployment via Cog.
- Broad catalog of community AI models — text, image, video, audio
- FLUX 1.1 Pro Ultra and Kling v2.1 in the 2026 catalog
- Llama 4 Scout and Maverick available
- Custom model deployment via Cog — any Hugging Face model can be deployed
- Simple pay-per-prediction pricing with no infrastructure
- Streaming and webhooks for production integrations
- Higher per-token cost than dedicated text inference APIs
- Cold start latency on less popular models
- Less suitable for high-throughput text inference
Replicate Models
| Model | Modality | Tier | Price | Cached /1M | TTFT | Throughput | Context | Intel. | Caps | |
|---|---|---|---|---|---|---|---|---|---|---|
mistral-7b-instruct-v0.2 Mistral | Chat | budget | $0.05/ $0.25 in / out per 1M EST Jul 2026 | — | 250ms | 300 t/s | 33K | 42 | ||
meta-llama-3-70b-instruct Llama 3 | Chat | efficient | $0.65/ $2.75 in / out per 1M EST Jul 2026 | — | 600ms | 90 t/s | 8K | 58 | ||
meta-llama-3.1-405b-instruct Llama 3.1 | Chat | advanced | $9.50/ $9.50 in / out per 1M EST Jul 2026 | — | 1.2s | 45 t/s | 131K | 74 |
Replicate — Frequently Asked Questions (FAQs)
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Replicate is an inference API provider that hosts open-weight and third-party large language models. Replicate is a platform for running open-source AI models via a simple API. It hosts thousands of community models including FLUX 1.1 Pro, Llama 4, Stable Diffusion, Whisper, and video generation models. Pay per prediction with no infrastructure to manage. The 2026 catalog has expanded significantly with FLUX 1.1 Pro Ultra, Kling v2.1, and Llama 4 Scout/Maverick. Custom model deployment via Cog remains a core differentiator. Common use cases include Image generation, Video generation, Audio transcription, Custom model deployment. Headquartered in San Francisco, CA, founded 2021. 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.
Replicate vs other LLM providers
Replicate 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. A broad catalog of community AI models — if a model exists on Hugging Face, it's likely on Replicate. Strong coverage for image, video, and audio model access, plus custom model deployment via Cog. Use the LLM provider comparison tool to see Replicate 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 Replicate?
Replicate stands out because: A broad catalog of community AI models — if a model exists on Hugging Face, it's likely on Replicate. Strong coverage for image, video, and audio model access, plus custom model deployment via Cog.. The API currently offers 3 models, spanning 1 advanced, 1 efficient, 1 budget 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 Replicate token pricing
Replicate 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 $0.05/1M tokens. Prices shown are sourced from Replicate'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.
Replicate 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. Replicate's largest context window is 131K 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 Replicate makes sense
Managed inference APIs like Replicate 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 Replicate 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 Replicate token price data is available in the LLM price history charts.
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