🔥 Qwen/Qwen3-32B
High-performance 32B parameter LLM. Excellent for reasoning, coding, and multilingual tasks.
Input: $0.15/M tokensOutput: $0.44/M tokens
33.54M runs in 7 days
Advanced reasoning model with chain-of-thought capabilities, rivaling OpenAI o1 at a fraction of the cost.
Parameters
671B (MoE, 37B active)
Context
128,000 tokens
Organization
DeepSeek
Start using DeepSeek R1 in minutes. VoltageGPU provides an OpenAI-compatible API — just change the base_url.
from openai import OpenAI
client = OpenAI(
base_url="https://api.voltagegpu.com/v1",
api_key="YOUR_VOLTAGE_API_KEY"
)
response = client.chat.completions.create(
model="deepseek-ai/DeepSeek-R1",
messages=[
{"role": "user", "content": "Prove that there are infinitely many prime numbers."}
],
max_tokens=4096,
temperature=0.6
)
print(response.choices[0].message.content)curl -X POST https://api.voltagegpu.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_VOLTAGE_API_KEY" \
-d '{
"model": "deepseek-ai/DeepSeek-R1",
"messages": [
{"role": "user", "content": "Prove that there are infinitely many prime numbers."}
],
"max_tokens": 4096,
"temperature": 0.6
}'| Component | Price | Unit |
|---|---|---|
| Input tokens | $0.14 | per 1M tokens |
| Output tokens | $0.28 | per 1M tokens |
New accounts receive $5 free credit. No credit card required to start.
DeepSeek R1 achieves state-of-the-art results on reasoning benchmarks including AIME 2024 (79.8%), MATH-500 (97.3%), and Codeforces (2029 Elo rating). It supports chain-of-thought reasoning with transparent step-by-step problem solving. The model handles complex multi-step mathematical proofs, competitive programming challenges, logical deduction, and scientific reasoning. Its MoE architecture ensures efficient inference despite the massive 671B total parameter count.
DeepSeek R1 is a cutting-edge reasoning model developed by DeepSeek, featuring 671 billion parameters in a Mixture-of-Experts architecture with 37 billion active parameters per forward pass. It excels at complex reasoning tasks including mathematics, coding, logic puzzles, and scientific analysis. R1 uses chain-of-thought reasoning to break down complex problems step by step, producing transparent and verifiable reasoning chains. It matches or exceeds the performance of models like OpenAI o1 on many benchmarks while being fully open source and significantly more affordable.
Solve complex math problems, proofs, and competition-level questions with step-by-step reasoning.
Write, review, and debug code across multiple programming languages with logical analysis.
Analyze scientific papers, design experiments, and reason through complex hypotheses.
Process complex documents requiring multi-step logical reasoning and evidence evaluation.
Explain complex concepts step-by-step with transparent reasoning chains students can follow.
https://api.voltagegpu.com/v1/chat/completions| Authorization | Bearer YOUR_VOLTAGE_API_KEY | Required |
| Content-Type | application/json | Required |
deepseek-ai/DeepSeek-R1Use this value as the model parameter in your API requests.
curl -X POST https://api.voltagegpu.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_VOLTAGE_API_KEY" \
-d '{
"model": "deepseek-ai/DeepSeek-R1",
"messages": [
{"role": "user", "content": "Prove that there are infinitely many prime numbers."}
],
"max_tokens": 4096,
"temperature": 0.6
}'Recommended GPU for large models requiring high VRAM and memory bandwidth.
Best performance for large model inference with HBM3 memory.
Access this model and 140+ others through our OpenAI-compatible API.
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DeepSeek R1 matches or exceeds OpenAI o1 on many reasoning benchmarks while being open source and up to 95% cheaper. On AIME 2024 math benchmarks, R1 scores 79.8% compared to o1's 83.3%, and on MATH-500 it achieves 97.3% vs o1's 96.4%. The main advantage is cost: at $0.14/M input tokens vs OpenAI o1's significantly higher pricing.
Chain-of-thought (CoT) reasoning means the model explicitly shows its step-by-step thinking process before arriving at a final answer. This makes the reasoning transparent and verifiable, allowing you to see exactly how the model arrived at its conclusion and catch any errors in logic.
Yes, DeepSeek R1 is fully open source under the MIT license. The model weights, training methodology, and technical report are all publicly available. You can run it locally or use it through VoltageGPU's API for managed inference.
DeepSeek R1 supports a context window of 128,000 tokens, allowing it to process long documents, codebases, and multi-turn conversations. This is equivalent to roughly 96,000 words or about 300 pages of text.
You can use DeepSeek R1 through VoltageGPU's OpenAI-compatible API. Simply use the OpenAI Python SDK with base_url set to "https://api.voltagegpu.com/v1" and model set to "deepseek-ai/DeepSeek-R1". No code changes are needed if you're already using the OpenAI SDK.
Get $5 free credit when you sign up. No credit card required. Deploy in under 30 seconds with our OpenAI-compatible API.