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Model Library

Start building with open‑source models

Explore our 200+ open-source and specialized multimodal models for chat, images, code, and more.

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Featured

models

Chat

DeepSeek-R1-0528

Upgraded DeepSeek-R1 with better reasoning, function calling, and coding, using 23K-token thinking to score 87.5% on AIME.

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Updated

Chat

Qwen3 235B A22B FP8 Throughput

Hybrid instruct + reasoning model (232Bx22B MoE) optimized for high-throughput, cost-efficient inference and distillation.

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Chat

Llama 4 Maverick

SOTA 128-expert MoE powerhouse for multilingual image/text understanding, creative writing, and enterprise-scale applications.

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Chat

Llama 4 Scout

SOTA 109B model with 17B active params & large context, excelling at multi-document analysis, codebase reasoning, and personalized tasks.

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Chat

Gemma 3 27B

Lightweight model with vision-language input, multilingual support, visual reasoning, and top-tier performance per size.

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Chat

DeepSeek-V3-0324

DeepSeek's latest open Mixture-of-Experts model challenging top AI models at much lower cost.

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Chat

DeepSeek-R1-0528 Throughput

Throughput DeepSeek-R1 is a state of the art reasoning model trained with reinforcement learning. It delivers strong performance on math, code, and logic tasks – comparable to OpenAI-o1. It is especially good at tasks like code review, document analysis, planning, information extraction, and coding.

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NEW

Chat

Qwen QwQ-32B

Qwen series reasoning model excelling in complex tasks, outperforming conventional instruction-tuned models on hard problems.

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All

Chat

DeepSeek-V3-0324

DeepSeek's latest open Mixture-of-Experts model challenging top AI models at much lower cost.

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DeepSeek

Chat

Qwen3 235B A22B FP8 Throughput

Hybrid instruct + reasoning model (232Bx22B MoE) optimized for high-throughput, cost-efficient inference and distillation.

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Qwen

Chat

Llama 4 Maverick

SOTA 128-expert MoE powerhouse for multilingual image/text understanding, creative writing, and enterprise-scale applications.

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Meta

Chat

Gemma 3 27B

Lightweight model with vision-language input, multilingual support, visual reasoning, and top-tier performance per size.

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Google

Image

FLUX.1 Kontext [dev]

In-context image generation and editing model using both text and image inputs.

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New
Black Forest Labs

Chat

Gemma 3n E4B Instruct

Selective parameter activation delivers 2B/4B multimodal performance on low-resource devices, handling text, image, video, audio

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New
Google

Chat

Llama 3.3 70B

The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model is optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.

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Meta

Chat

DeepSeek-R1-0528 Throughput

Throughput DeepSeek-R1 is a state of the art reasoning model trained with reinforcement learning. It delivers strong performance on math, code, and logic tasks – comparable to OpenAI-o1. It is especially good at tasks like code review, document analysis, planning, information extraction, and coding.

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NEW
DeepSeek

Image

FLUX.1 Kontext [pro]

In-context image generation and editing model with enhanced prompt adherence.

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New
Black Forest Labs

Chat

Qwen2.5 72B

Powerful decoder-only models available in 7B and 72B variants, developed by Alibaba Cloud's Qwen team for advanced language processing.

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Qwen

Chat

DeepSeek-R1-0528

Upgraded DeepSeek-R1 with better reasoning, function calling, and coding, using 23K-token thinking to score 87.5% on AIME.

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Updated
DeepSeek

Image

FLUX.1 [schnell] Free

Free endpoint for the SOTA open-source image generation model by Black Forest Labs.

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Free
Black Forest Labs

Audio

Cartesia Sonic-2

Low-latency, ultra-realistic voice model, served in partnership with Cartesia.

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Cartesia

Chat

R1 1776

DeepSeek-R1 model post-trained by Perplexity AI to remove censorship and bias while preserving reasoning strength.

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New
Perplexity AI

Chat

NIM Llama 3.1 Nemotron 70B Instruct

NVIDIA NIM for GPU accelerated Llama 3.1 Nemotron 70B Instruct inference through OpenAI compatible APIs.

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Meta

Code

Qwen 2.5 Coder 32B Instruct

SOTA code LLM with advanced code generation, reasoning, fixing, and support for up to 128K tokens.

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Qwen

Image

FLUX1.1 [pro]

Premium image generation model by Black Forest Labs.

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Black Forest Labs

Vision

Qwen2.5-VL 72B Instruct

Vision-language model with advanced visual reasoning, video understanding, structured outputs, and agentic capabilities.

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Qwen

Chat

Arcee AI Caller

A 32B SLM optimized for managing complex tool-based interactions and API function calls. Its strength lies in precise execution, intelligent orchestration, and effective communication between systems – making it ideal for automation pipelines.

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Arcee AI

Chat

Llama 4 Scout

SOTA 109B model with 17B active params & large context, excelling at multi-document analysis, codebase reasoning, and personalized tasks.

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Meta

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Run any model on the fastest endpoints

Use our API to deploy any open-source model on the fastest inference stack available with optimal cost efficiency.

Scale into a dedicated deployment anytime with a custom number of instances to get optimal throughput.

Start buildingRequest dedicated endpoint
Curl
Python
Typescript

RUN INFERENCE

curl -X POST "https://api.together.xyz/v1/chat/completions" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
	"model": "meta-llama/Llama-Vision-Free",
	"messages": [{"role": "user", "content": "What are some fun things to do in New York?"}]
  }'

RUN INFERENCE

from together import Together

client = Together()

response = client.chat.completions.create(
    model="meta-llama/Llama-Vision-Free",
    messages=[{"role": "user", "content": "What are some fun things to do in New York?"}],
)

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

RUN INFERENCE

import Together from "together-ai";

const together = new Together({ apiKey: process.env.TOGETHER_API_KEY });

const response = await together.chat.completions.create({
    messages: [{"role": "user", "content": "What are some fun things to do in New York?"}],
    model: "meta-llama/Llama-Vision-Free",
});

console.log(response.choices[0].message.content)

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