Why Together AI for DeepSeek R1?

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DeepSeek offers its own API for R1 at $0.55/1M input tokens - cheaper than Together AI's $3/1M. But there are practical reasons to prefer Together:

Reliability - DeepSeek's API has experienced outages and rate limiting during peak demand. Together AI runs on dedicated US-based infrastructure with SLA guarantees.
Latency - Together AI has lower TTFT for US-based users (no cross-Pacific routing)
Compliance - data processed via Together AI stays in the US, relevant for regulated industries
No geoblocking - some enterprise networks block Chinese IP ranges

For teams where $3 vs $0.55 per million tokens is a meaningful budget concern, running R1 through DeepSeek direct or self-hosting makes sense. For most product teams, the reliability premium is worth it.

Setting Up Together AI

python

from together import Together

client = Together(api_key=os.environ["TOGETHER_API_KEY"])

response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-R1",
    messages=[
        {
            "role": "user",
            "content": "Solve this step by step: If 5 workers can build a wall in 8 days, how many days would 10 workers take to build 3 such walls?",
        }
    ],
    stream=True,
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

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Streaming Thinking Tokens

DeepSeek R1's "thinking" process - where it reasons through a problem before giving a final answer - can be streamed in real time. This allows you to show users a "thinking..." indicator while the model works, then display the final answer:

python

thinking_buffer = []
answer_buffer = []
in_thinking = False

for chunk in client.chat.completions.create(
    model="deepseek-ai/DeepSeek-R1",
    messages=[{"role": "user", "content": "Prove that the square root of 2 is irrational."}],
    stream=True,
):
    content = chunk.choices[0].delta.content or ""

    if "<think>" in content:
        in_thinking = True
    elif "</think>" in content:
        in_thinking = False
    elif in_thinking:
        thinking_buffer.append(content)
        print(f"[thinking] {content}", end="", flush=True)
    else:
        answer_buffer.append(content)
        print(content, end="", flush=True)

print("\n\nFinal answer:", "".join(answer_buffer))

Parallel Requests for Batch Processing

Together AI's serverless infrastructure handles parallel requests without rate limiting degradation. For batch reasoning tasks:

python

import asyncio
from together import AsyncTogether

async def reason(client: AsyncTogether, problem: str) -> str:
    response = await client.chat.completions.create(
        model="deepseek-ai/DeepSeek-R1",
        messages=[{"role": "user", "content": problem}],
    )
    return response.choices[0].message.content

async def batch_reason(problems: list[str]) -> list[str]:
    client = AsyncTogether(api_key=os.environ["TOGETHER_API_KEY"])
    tasks = [reason(client, p) for p in problems]
    return await asyncio.gather(*tasks)

results = asyncio.run(batch_reason(["Problem 1...", "Problem 2...", "Problem 3..."]))

Distilled R1 Variants for Lower Cost

Together AI also hosts the distilled R1 variants, which are much smaller but retain much of the reasoning quality:

Model	Size	Cost (input)	vs R1 671B
DeepSeek-R1	671B	$3.00/1M	Baseline
DeepSeek-R1-Distill-Llama-70B	70B	$0.88/1M	~90% quality
DeepSeek-R1-Distill-Qwen-32B	32B	$0.27/1M	~85% quality
DeepSeek-R1-Distill-Qwen-7B	7B	$0.20/1M	~75% quality

For most math and coding reasoning tasks, the 70B distill performs within 10% of the full 671B model at less than one-third the price. Start with the 70B distill and only escalate to the full model for problems that require it.

Links

#deepseek-r1#together-ai#reasoning#api#cost

Mahmudul Haque Qudrati

CEO & ML Engineer

Visionary technologist, software engineer, and machine learning specialist. Founder and CEO of Pristren, directing engineering teams that ship production-grade AI/ML pipelines, mission-critical full-stack applications, and developer tooling. Creator of Zlyqor, the unified team workspace platform. Author of 540+ technical guides and benchmark research reports on large language models, agentic workflows, Model Context Protocol (MCP), and modern web stacks.