Models / Deep CogitoCogito /  / Cogito v2.1 671B API

Cogito v2.1 671B API

Advanced hybrid reasoning model with self-improving capabilities

Try Now

This model isn’t available on Together’s Serverless API.

Deploy this model on an on-demand Dedicated Endpoint or pick a supported alternative from the Model Library.

Introducing Cogito v2.1 671B

Cogito v2.1 671B is Deep Cogito's flagship open-source hybrid reasoning model, built on Iterated Distillation and Amplification (IDA) that learns to think better through self-improvement. Outperforming all US open models and rivaling Claude 4 Opus and O3, Cogito v2.1 achieves frontier-level performance while using 60% shorter reasoning chains than competitors — delivering breakthrough efficiency with 4,894 average tokens per response (lowest among frontier models) at just $1.25 per million tokens.

89.47%
AIME 2025 (Competition Math)
Elite mathematical reasoning outperforming models 10x larger
60%
More Efficient Reasoning
Shorter chains than DeepSeek R1 with equal accuracy
4,894
Avg Tokens per Response
Lowest among all frontier models for massive cost savings
Key Capabilities
Hybrid Reasoning Modes: Seamlessly switch between fast standard responses and deep step-by-step reasoning with visible thought chains for complex problem-solving
Self-Improving Intelligence: IDA methodology distills reasoning discoveries back into parameters, developing machine intuition that anticipates outcomes rather than just searching longer
State-of-the-Art Benchmarks: 98.57% MATH-500, 77.72% GPQA Diamond, 84.69% MMLU Pro, 86.24% Multilingual MMLU across 30+ languages
Production-Ready Efficiency: 128K context window, OpenAI-compatible API, native tool calling support, and $1.25/1M tokens pricing on Together AI

Cogito v2.1 671B API Usage

Endpoint

curl -X POST "https://api.together.xyz/v1/chat/completions" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepcogito/cogito-v2-1-671b",
    "messages": [
      {
        "role": "user",
        "content": "What are some fun things to do in New York?"
      }
    ]
}'
curl -X POST "https://api.together.xyz/v1/images/generations" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepcogito/cogito-v2-1-671b",
    "prompt": "Draw an anime style version of this image.",
    "width": 1024,
    "height": 768,
    "steps": 28,
    "n": 1,
    "response_format": "url",
    "image_url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"
  }'
curl -X POST https://api.together.xyz/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -d '{
    "model": "deepcogito/cogito-v2-1-671b",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Describe what you see in this image."},
        {"type": "image_url", "image_url": {"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"}}
      ]
    }],
    "max_tokens": 512
  }'
curl -X POST https://api.together.xyz/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -d '{
    "model": "deepcogito/cogito-v2-1-671b",
    "messages": [{
      "role": "user",
      "content": "Given two binary strings `a` and `b`, return their sum as a binary string"
    }]
  }'
curl -X POST https://api.together.xyz/v1/rerank \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -d '{
    "model": "deepcogito/cogito-v2-1-671b",
    "query": "What animals can I find near Peru?",
    "documents": [
      "The giant panda (Ailuropoda melanoleuca), also known as the panda bear or simply panda, is a bear species endemic to China.",
      "The llama is a domesticated South American camelid, widely used as a meat and pack animal by Andean cultures since the pre-Columbian era.",
      "The wild Bactrian camel (Camelus ferus) is an endangered species of camel endemic to Northwest China and southwestern Mongolia.",
      "The guanaco is a camelid native to South America, closely related to the llama. Guanacos are one of two wild South American camelids; the other species is the vicuña, which lives at higher elevations."
    ],
    "top_n": 2
  }'
curl -X POST https://api.together.xyz/v1/embeddings \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "Our solar system orbits the Milky Way galaxy at about 515,000 mph.",
    "model": "deepcogito/cogito-v2-1-671b"
  }'
curl -X POST https://api.together.xyz/v1/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -d '{
    "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
    "prompt": "A horse is a horse",
    "max_tokens": 32,
    "temperature": 0.1,
    "safety_model": "deepcogito/cogito-v2-1-671b"
  }'
curl --location 'https://api.together.ai/v1/audio/generations' \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer $TOGETHER_API_KEY' \
  --output speech.mp3 \
  --data '{
    "input": "Today is a wonderful day to build something people love!",
    "voice": "helpful woman",
    "response_format": "mp3",
    "sample_rate": 44100,
    "stream": false,
    "model": "deepcogito/cogito-v2-1-671b"
  }'
curl -X POST "https://api.together.xyz/v1/audio/transcriptions" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -F "model=deepcogito/cogito-v2-1-671b" \
  -F "language=en" \
  -F "response_format=json" \
  -F "timestamp_granularities=segment"
curl --request POST \
  --url https://api.together.xyz/v2/videos \
  --header "Authorization: Bearer $TOGETHER_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "deepcogito/cogito-v2-1-671b",
    "prompt": "some penguins building a snowman"
  }'
curl --request POST \
  --url https://api.together.xyz/v2/videos \
  --header "Authorization: Bearer $TOGETHER_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "deepcogito/cogito-v2-1-671b",
    "frame_images": [{"input_image": "https://cdn.pixabay.com/photo/2020/05/20/08/27/cat-5195431_1280.jpg"}]
  }'

from together import Together

client = Together()

response = client.chat.completions.create(
  model="deepcogito/cogito-v2-1-671b",
  messages=[
    {
      "role": "user",
      "content": "What are some fun things to do in New York?"
    }
  ]
)
print(response.choices[0].message.content)
from together import Together

client = Together()

imageCompletion = client.images.generate(
    model="deepcogito/cogito-v2-1-671b",
    width=1024,
    height=768,
    steps=28,
    prompt="Draw an anime style version of this image.",
    image_url="https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png",
)

print(imageCompletion.data[0].url)


from together import Together

client = Together()

response = client.chat.completions.create(
    model="deepcogito/cogito-v2-1-671b",
    messages=[{
    	"role": "user",
      "content": [
        {"type": "text", "text": "Describe what you see in this image."},
        {"type": "image_url", "image_url": {"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"}}
      ]
    }]
)
print(response.choices[0].message.content)

from together import Together

client = Together()
response = client.chat.completions.create(
  model="deepcogito/cogito-v2-1-671b",
  messages=[
  	{
	    "role": "user", 
      "content": "Given two binary strings `a` and `b`, return their sum as a binary string"
    }
 ],
)

print(response.choices[0].message.content)

from together import Together

client = Together()

query = "What animals can I find near Peru?"

documents = [
  "The giant panda (Ailuropoda melanoleuca), also known as the panda bear or simply panda, is a bear species endemic to China.",
  "The llama is a domesticated South American camelid, widely used as a meat and pack animal by Andean cultures since the pre-Columbian era.",
  "The wild Bactrian camel (Camelus ferus) is an endangered species of camel endemic to Northwest China and southwestern Mongolia.",
  "The guanaco is a camelid native to South America, closely related to the llama. Guanacos are one of two wild South American camelids; the other species is the vicuña, which lives at higher elevations.",
]

response = client.rerank.create(
  model="deepcogito/cogito-v2-1-671b",
  query=query,
  documents=documents,
  top_n=2
)

for result in response.results:
    print(f"Relevance Score: {result.relevance_score}")

from together import Together

client = Together()

response = client.embeddings.create(
  model = "deepcogito/cogito-v2-1-671b",
  input = "Our solar system orbits the Milky Way galaxy at about 515,000 mph"
)

from together import Together

client = Together()

response = client.completions.create(
  model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
  prompt="A horse is a horse",
  max_tokens=32,
  temperature=0.1,
  safety_model="deepcogito/cogito-v2-1-671b",
)

print(response.choices[0].text)

from together import Together

client = Together()

speech_file_path = "speech.mp3"

response = client.audio.speech.create(
  model="deepcogito/cogito-v2-1-671b",
  input="Today is a wonderful day to build something people love!",
  voice="helpful woman",
)
    
response.stream_to_file(speech_file_path)

from together import Together

client = Together()
response = client.audio.transcribe(
    model="deepcogito/cogito-v2-1-671b",
    language="en",
    response_format="json",
    timestamp_granularities="segment"
)
print(response.text)
from together import Together

client = Together()

# Create a video generation job
job = client.videos.create(
    prompt="A serene sunset over the ocean with gentle waves",
    model="deepcogito/cogito-v2-1-671b"
)
from together import Together

client = Together()

job = client.videos.create(
    model="deepcogito/cogito-v2-1-671b",
    frame_images=[
        {
            "input_image": "https://cdn.pixabay.com/photo/2020/05/20/08/27/cat-5195431_1280.jpg",
        }
    ]
)
import Together from 'together-ai';
const together = new Together();

const completion = await together.chat.completions.create({
  model: 'deepcogito/cogito-v2-1-671b',
  messages: [
    {
      role: 'user',
      content: 'What are some fun things to do in New York?'
     }
  ],
});

console.log(completion.choices[0].message.content);
import Together from "together-ai";

const together = new Together();

async function main() {
  const response = await together.images.create({
    model: "deepcogito/cogito-v2-1-671b",
    width: 1024,
    height: 1024,
    steps: 28,
    prompt: "Draw an anime style version of this image.",
    image_url: "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png",
  });

  console.log(response.data[0].url);
}

main();

import Together from "together-ai";

const together = new Together();
const imageUrl = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png";

async function main() {
  const response = await together.chat.completions.create({
    model: "deepcogito/cogito-v2-1-671b",
    messages: [{
      role: "user",
      content: [
        { type: "text", text: "Describe what you see in this image." },
        { type: "image_url", image_url: { url: imageUrl } }
      ]
    }]
  });
  
  console.log(response.choices[0]?.message?.content);
}

main();

import Together from "together-ai";

const together = new Together();

async function main() {
  const response = await together.chat.completions.create({
    model: "deepcogito/cogito-v2-1-671b",
    messages: [{
      role: "user",
      content: "Given two binary strings `a` and `b`, return their sum as a binary string"
    }]
  });
  
  console.log(response.choices[0]?.message?.content);
}

main();

import Together from "together-ai";

const together = new Together();

const query = "What animals can I find near Peru?";
const documents = [
  "The giant panda (Ailuropoda melanoleuca), also known as the panda bear or simply panda, is a bear species endemic to China.",
  "The llama is a domesticated South American camelid, widely used as a meat and pack animal by Andean cultures since the pre-Columbian era.",
  "The wild Bactrian camel (Camelus ferus) is an endangered species of camel endemic to Northwest China and southwestern Mongolia.",
  "The guanaco is a camelid native to South America, closely related to the llama. Guanacos are one of two wild South American camelids; the other species is the vicuña, which lives at higher elevations."
];

async function main() {
  const response = await together.rerank.create({
    model: "deepcogito/cogito-v2-1-671b",
    query: query,
    documents: documents,
    top_n: 2
  });
  
  for (const result of response.results) {
    console.log(`Relevance Score: ${result.relevance_score}`);
  }
}

main();


import Together from "together-ai";

const together = new Together();

const response = await client.embeddings.create({
  model: 'deepcogito/cogito-v2-1-671b',
  input: 'Our solar system orbits the Milky Way galaxy at about 515,000 mph',
});

import Together from "together-ai";

const together = new Together();

async function main() {
  const response = await together.completions.create({
    model: "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
    prompt: "A horse is a horse",
    max_tokens: 32,
    temperature: 0.1,
    safety_model: "deepcogito/cogito-v2-1-671b"
  });
  
  console.log(response.choices[0]?.text);
}

main();

import Together from 'together-ai';

const together = new Together();

async function generateAudio() {
   const res = await together.audio.create({
    input: 'Today is a wonderful day to build something people love!',
    voice: 'helpful woman',
    response_format: 'mp3',
    sample_rate: 44100,
    stream: false,
    model: 'deepcogito/cogito-v2-1-671b',
  });

  if (res.body) {
    console.log(res.body);
    const nodeStream = Readable.from(res.body as ReadableStream);
    const fileStream = createWriteStream('./speech.mp3');

    nodeStream.pipe(fileStream);
  }
}

generateAudio();

import Together from "together-ai";

const together = new Together();

const response = await together.audio.transcriptions.create(
  model: "deepcogito/cogito-v2-1-671b",
  language: "en",
  response_format: "json",
  timestamp_granularities: "segment"
});
console.log(response)
import Together from "together-ai";

const together = new Together();

async function main() {
  // Create a video generation job
  const job = await together.videos.create({
    prompt: "A serene sunset over the ocean with gentle waves",
    model: "deepcogito/cogito-v2-1-671b"
  });
import Together from "together-ai";

const together = new Together();

const job = await together.videos.create({
  model: "deepcogito/cogito-v2-1-671b",
  frame_images: [
    {
      input_image: "https://cdn.pixabay.com/photo/2020/05/20/08/27/cat-5195431_1280.jpg",
    }
  ]
});

How to use Cogito v2.1 671B

Model details

Architecture Overview:
• Cogito v2.1 671B employs a sophisticated Mixture-of-Experts (MoE) architecture with 671 billion total parameters, utilizing sparse routing mechanisms to activate only specialized expert subnetworks per token, enabling massive scale without proportional compute costs
• Features a 128K token context window optimized for long-form reasoning, technical documentation, and multi-turn conversations
• Implements a hybrid inference system supporting both standard mode (direct answers using internalized "intuition") and reasoning mode (step-by-step self-reflection with visible thought chains)
• Optimized for efficient serverless deployment on Together AI's infrastructure

Training Methodology - Iterated Distillation & Amplification (IDA):
• Revolutionary self-improvement approach where the model runs reasoning chains during training, then is trained on its own intermediate thoughts to develop stronger "machine intuition"
• Unlike traditional models that rely on extended inference-time reasoning, Cogito distills successful reasoning patterns directly into model parameters
• Training process explicitly rewards shorter, more efficient reasoning paths while discouraging unnecessary computational detours
• Trained on multilingual datasets spanning 30+ languages with emphasis on coding, STEM, instruction following, and general helpfulness
• Total training cost remarkably achieved at under $3.5 million for the entire Cogito family (3B to 671B), demonstrating unprecedented cost efficiency

Performance Characteristics:
• AIME 2025 (Competition Mathematics): 89.47% - outperforming models 10x larger
• MATH-500 benchmark: 98.57% accuracy
• GPQA Diamond (Scientific Reasoning): 77.72%
• SWE-Bench Verified (Coding): 42.00% solve rate
• MMLU Pro (Reasoning & Knowledge): 84.69%
• Multilingual MMLU: 86.24% across 30+ languages
• Average token efficiency: 4,894 tokens per response (lowest among frontier models)
• Performance competitive with DeepSeek v3, matching or exceeding latest 0528 model while using 60% shorter reasoning chains
• Approaches capabilities of closed models like Claude 4 Opus, O3, and GPT-5 across diverse benchmarks
• Demonstrates emergent multimodal reasoning capabilities, able to reason about images despite not being explicitly trained for visual tasks

Prompting Cogito v2.1 671B

Applications & Use Cases

High-Performance Use Cases:
• Advanced Mathematical Problem Solving: Superior performance on competition mathematics (AIME 2025: 89.47%), calculus, optimization problems, and quantitative analysis
• Software Engineering & Code Generation: 42% solve rate on SWE-Bench demonstrates strong debugging, code review, and system design capabilities
• Scientific Research & STEM: 77.72% on GPQA Diamond showcases expertise in physics, chemistry, biology, and interdisciplinary scientific reasoning
• Multilingual Applications: 86.24% on Multilingual MMLU enables global deployment across 30+ languages with native-level comprehension
• Legal & Policy Analysis: Reasoning mode excels at applying precedents, analyzing case law, and providing nuanced legal interpretations

Enterprise Applications:
• Intelligent Document Processing: 128K context window handles entire technical documents, contracts, research papers in single context
• Customer Support Automation: Hybrid mode allows fast responses for simple queries, deep reasoning for complex troubleshooting
• Financial Analysis & Risk Assessment: Strong quantitative reasoning combined with efficient token usage for cost-effective at-scale deployment
• Educational Technology: Step-by-step reasoning mode ideal for tutoring, homework help, and adaptive learning systems
• Research Assistance: Frontier performance at $1.25/1M tokens makes large-scale research analysis economically viable

Developer & Research Applications:
• Rapid Prototyping: Together AI's serverless platform enables instant deployment without infrastructure setup
• Model Experimentation: Compare standard vs reasoning modes in real-time via playground interface
• Benchmark Development: Performance approaching closed frontier models enables reproducible research
• Scalable Research: Serverless infrastructure scales automatically for large-scale experiments

Cost-Sensitive Deployments:
• High-Volume Production: Lowest token usage (4,894 avg) among frontier models translates to 20-40% cost savings vs alternatives
• Serverless Efficiency: Pay-per-use pricing on Together AI eliminates infrastructure costs and management overhead
• Startup & SMB Applications: Frontier capabilities at accessible pricing ($1.25/1M tokens) democratizes advanced AI
• Auto-scaling: Together AI's serverless infrastructure automatically handles traffic spikes without manual intervention

Unique Capabilities:
• Emergent Image Reasoning: Despite no explicit visual training, demonstrates ability to reason about images when presented in context
• Efficiency-First Design: 60% shorter reasoning chains mean faster responses and lower costs without sacrificing accuracy
• Hybrid Intelligence: Seamlessly switch between fast intuition and deep deliberation based on query complexity

Looking for production scale? Deploy on a dedicated endpoint

Deploy Cogito v2.1 671B on a dedicated endpoint with custom hardware configuration, as many instances as you need, and auto-scaling.

Get started