What is the context window of DeepSeek: R1?

DeepSeek: R1 accepts up to 64,000 tokens (64K) of context per request.

How much does DeepSeek: R1 cost per million tokens?

Through Wide Area Intelligence, DeepSeek: R1 costs $0.77 per 1M input tokens and $2.75 per 1M output tokens when served from the cloud, billed from prepaid credits. A workload of 1M input plus 1M output tokens costs $3.52.

Can I run DeepSeek: R1 on my own hardware?

Yes — DeepSeek: R1 is an open-weight model you can run on a workstation or on-prem server you own. At Q4_K_M it needs roughly 390.2 GB of GPU memory with an 8,192-token context — more than a single 80 GB card, so plan for multiple GPUs or a large unified-memory machine. Wide Area Intelligence routes requests to your own nodes first and fails over to the cloud only when they can't serve.

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deepseek

DeepSeek: R1

Name: DeepSeek: R1
Brand: deepseek
Price: 0.7700 USD

deepseek/deepseek-r1

↓ runs free on your own hardware⚙ tool calling

DeepSeek R1 is here: Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inference pass....

specs & pricing

Type: text
Provider: deepseek
Model ID: deepseek/deepseek-r1
Capabilities: tools, reasoning
Context window: 64K tokens
Self-hostable: Yes — runs on your own GPU
Input price: $0.77 / 1M tokens
Output price: $2.75 / 1M tokens

Cloud price is billed from prepaid credits when a request fails over to the cloud. Open-weight models run free on GPUs you own — the gateway routes to your nodes first.

What it costs to run

cloud · 1M in + 1M out

$3.52

$0.77 for a million input tokens plus $2.75 for a million output tokens, billed from credits only when a request fails over to the cloud.

your hardware

$0 / token

Served from a workstation or on-prem server you own, there is no per-token fee — the marginal cost is power.

AI cost calculator →GPU power cost →

Run it on your own hardware

Estimated GPU memory for DeepSeek: R1 (685B parameters), including the KV cache for a 8,192-token context.

Q4_K_M: ~390.2 GBmulti-GPU or large unified memory
Q8_0: ~677.1 GBmulti-GPU or large unified memory
FP16 / BF16: ~1276.3 GBmulti-GPU or large unified memory

Install the agent on the machine and it dials out to the gateway over a single WebSocket — no port forwarding or inbound firewall rules. Requests for this model go to your node first and fail over to the cloud only when it can't serve.

VRAM calculator →Which GPU do I need? →

/// initialize

Call DeepSeek: R1 through one OpenAI-compatible endpoint.

Serve it from hardware you own, with cloud failover when your nodes are busy or offline.

Run it on your own hardware Try it in chat

no credit card · 2 nodes free · openai-compatible

Frequently asked questions

What is the context window of DeepSeek: R1?: DeepSeek: R1 accepts up to 64,000 tokens (64K) of context per request.
How much does DeepSeek: R1 cost per million tokens?: Through Wide Area Intelligence, DeepSeek: R1 costs $0.77 per 1M input tokens and $2.75 per 1M output tokens when served from the cloud, billed from prepaid credits. A workload of 1M input plus 1M output tokens costs $3.52.
Can I run DeepSeek: R1 on my own hardware?: Yes — DeepSeek: R1 is an open-weight model you can run on a workstation or on-prem server you own. At Q4_K_M it needs roughly 390.2 GB of GPU memory with an 8,192-token context — more than a single 80 GB card, so plan for multiple GPUs or a large unified-memory machine. Wide Area Intelligence routes requests to your own nodes first and fails over to the cloud only when they can't serve.

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