What is the context window of qwen/qwq-32b?

qwen/qwq-32b accepts up to 24,000 tokens (24K) of context per request.

How much does qwen/qwq-32b cost per million tokens?

Through Wide Area Intelligence, qwen/qwq-32b costs $0.73 per 1M input tokens and $1.10 per 1M output tokens when served from the cloud, billed from prepaid credits. A workload of 1M input plus 1M output tokens costs $1.83.

Can I run qwen/qwq-32b on my own hardware?

Yes — qwen/qwq-32b is an open-weight model you can run on a workstation or on-prem server you own. At Q4_K_M it needs roughly 18.9 GB of GPU memory, plus room for the context cache — it fits a single 24 GB card. 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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cloudflare

qwen/qwq-32b

Name: qwen/qwq-32b
Brand: cloudflare
Price: 0.7260 USD

cloudflare/qwen/qwq-32b

↓ runs free on your own hardware⚙ tool calling

QwQ is the reasoning model of the Qwen series. Compared with conventional instruction-tuned models, QwQ, which is capable of thinking and reasoning, can achieve significantly enhanced performance in downstream tasks, especially hard problems. QwQ-32B is the medium-sized reasoning model, which is capable of achieving competitive performance against state-of-the-art reasoning models, e.g., DeepSeek-R1, o1-mini.

specs & pricing

Type: text
Provider: cloudflare
Model ID: cloudflare/qwen/qwq-32b
Capabilities: tools
Context window: 24K tokens
Self-hostable: Yes — runs on your own GPU
Input price: $0.73 / 1M tokens
Output price: $1.10 / 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

$1.83

$0.73 for a million input tokens plus $1.10 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 qwen/qwq-32b (32B parameters), for the weights alone — add room for the context cache.

Q4_K_M: ~18.9 GBfits a 24 GB card
Q8_0: ~32.3 GBfits a 48 GB card
FP16 / BF16: ~60.4 GBfits a 80 GB card

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 qwen/qwq-32b 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 qwen/qwq-32b?: qwen/qwq-32b accepts up to 24,000 tokens (24K) of context per request.
How much does qwen/qwq-32b cost per million tokens?: Through Wide Area Intelligence, qwen/qwq-32b costs $0.73 per 1M input tokens and $1.10 per 1M output tokens when served from the cloud, billed from prepaid credits. A workload of 1M input plus 1M output tokens costs $1.83.
Can I run qwen/qwq-32b on my own hardware?: Yes — qwen/qwq-32b is an open-weight model you can run on a workstation or on-prem server you own. At Q4_K_M it needs roughly 18.9 GB of GPU memory, plus room for the context cache — it fits a single 24 GB card. 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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