Pricing

Claude-2 is an advanced version in the Claude series of language models. It is characterized by improved language understanding and generation capabilities compared to its predecessors, including Claude-Instant. This model is designed to provide more accurate, nuanced, and contextually aware responses, suitable for a wide range of advanced language processing tasks, including more complex conversation handling, content creation, and information synthesis.

Claude 3 Haiku is the fastest and most affordable model in the Claude family. With state-of-the-art vision capabilities and strong performance on industry benchmarks, Haiku is a versatile solution for a wide range of enterprise applications. Speed is essential for our enterprise users who need to quickly analyze large datasets and generate timely output for tasks like customer support. Claude 3 Haiku is three times faster than its peers for the vast majority of workloads, processing 21K tokens (~30 pages) per second for prompts under 32K tokens [1]. It also generates swift output, enabling responsive, engaging chat experiences and the execution of many small tasks in tandem.

Claude 3 Opus is Anthropic's most intelligent model, with best-in-market performance on highly complex tasks. It can navigate open-ended prompts and sight-unseen scenarios with remarkable fluency and human-like understanding. Opus shows us the outer limits of what’s possible with generative AI.

Claude 3 Sonnet strikes the ideal balance between intelligence and speed—particularly for enterprise workloads. It delivers strong performance at a lower cost compared to its peers, and is engineered for high endurance in large-scale AI deployments.

Claude 3.5 Sonnet sets new industry benchmarks for graduate-level reasoning (GPQA), undergraduate-level knowledge (MMLU), and coding proficiency (HumanEval). It shows marked improvement in grasping nuance, humor, and complex instructions, and is exceptional at writing high-quality content with a natural, relatable tone. Claude 3.5 Sonnet operates at twice the speed of Claude 3 Opus. This performance boost, combined with cost-effective pricing, makes Claude 3.5 Sonnet ideal for complex tasks such as context-sensitive customer support and orchestrating multi-step workflows.

Claude-Instant is a language model designed for rapid response generation while maintaining a high level of language understanding. It is optimized for speed and efficiency, making it suitable for applications where fast interaction is crucial, such as in conversational AI or real-time language processing systems.

Command R is an instruction-following conversational model that performs language tasks at a higher quality, more reliably, and with a longer context than previous models. It can be used for complex workflows like code generation, retrieval augmented generation (RAG), tool use, and agents.

Command R+ is an instruction-following conversational model that performs language tasks at a higher quality, more reliably, and with a longer context than previous models. It is best suited for complex RAG workflows and multi-step tool use.

Gemini is a family of generative AI models that lets developers generate content and solve problems. These models are designed and trained to handle both text and images as input.

Multimodal model that is designed for high volume, cost-effective applications. Gemini 1.5 Flash delivers speed and efficiency to build fast, lower-cost applications that don't compromise on quality.

Gemini 1.5 Pro is a foundation model that performs well at a variety of multimodal tasks such as visual understanding, classification, summarization, and creating content from image, audio and video. It's adept at processing visual and text inputs such as photographs, documents, infographics, and screenshots

PaLM-2 (Pathways Language Model-2) is a sophisticated language model developed by Google. It's recognized for its advanced natural language understanding and generation capabilities. Leveraging Google's Pathways AI architecture, PaLM-2 is designed to handle a wide array of complex language tasks with high efficiency and accuracy. It excels in contextual understanding, conversation, and creative content generation, making it a versatile tool for various AI-driven applications. PaLM-2 represents a significant step in language model development, showcasing Google's commitment to advancing AI and natural language processing technologies.

This model is a specialized version within the LLaMA series, focusing on code-related tasks. With 34 billion parameters and an instructive design, it is tailored to understand and generate programming code, assist in debugging, and provide coding-related guidance. Its 'instruct' nature means it's tuned to follow specific user commands more accurately, making it ideal for applications in software development and coding education.

This is a variant of the LLaMA (Language Model for Many Applications) series, with a size of 13 billion parameters. It's designed to be a versatile language model capable of handling a wide range of tasks. The 13B model offers a balance between computational efficiency and advanced language understanding and generation capabilities, suitable for applications requiring detailed and nuanced language processing.

The LLaMA-2-70B model, features 70 billion parameters. This model provides more depth and complexity in language understanding and generation compared to the 13B version. It's well-suited for tasks that require high-level language comprehension, complex problem-solving, and sophisticated content creation.

Meta's 8B and 70B parameter Llama 3 models are a major leap over Llama 2 and establish a new state-of-the-art for LLM models at those scales. Thanks to improvements in pretraining and post-training, the pretrained and instruction-fine-tuned models are the best models existing today at the 8B and 70B parameter scale. Improvements in post-training procedures substantially reduced false refusal rates, improved alignment, and increased diversity in model responses. Meta also saw greatly improved capabilities like reasoning, code generation, and instruction following making Llama 3 more steerable.

Meta's 8B and 70B parameter Llama 3 models are a major leap over Llama 2 and establish a new state-of-the-art for LLM models at those scales. Thanks to improvements in pretraining and post-training, the pretrained and instruction-fine-tuned models are the best models existing today at the 8B and 70B parameter scale. Improvements in post-training procedures substantially reduced false refusal rates, improved alignment, and increased diversity in model responses. Meta also saw greatly improved capabilities like reasoning, code generation, and instruction following making Llama 3 more steerable.

The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.

The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.

The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.

Mistral-7B-Instruct is a language model with a focus on instruction-based tasks. With 7 billion parameters, it is designed to understand and execute user instructions efficiently. This model is particularly effective in scenarios where clear and concise responses are required, adhering closely to the given instructions. It's useful in a variety of applications, including conversational AI, content summarization, and task-oriented dialogue systems.

Codestral is an open-weight generative AI model explicitly designed for code generation tasks. It helps developers write and interact with code through a shared instruction and completion API endpoint. As it masters code and English, it can be used to design advanced AI applications for software developers. Codestral is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash. It also performs well on more specific ones like Swift and Fortran. This broad language base ensures Codestral can assist developers in various coding environments and projects.

Unlike Transformer models, Mamba models offer the advantage of linear time inference and the theoretical ability to model sequences of infinite length. It allows users to engage with the model extensively with quick responses, irrespective of the input length. This efficiency is especially relevant for code productivity use cases—this is why we trained this model with advanced code and reasoning capabilities, enabling it to perform on par with SOTA transformer-based models.

Mistral Large 2 has a 128k context window and supports dozens of languages including French, German, Spanish, Italian, Portuguese, Arabic, Hindi, Russian, Chinese, Japanese, and Korean, along with 80+ coding languages including Python, Java, C, C++, JavaScript, and Bash. Mistral Large 2 is designed for single-node inference with long-context applications in mind – its size of 123 billion parameters allows it to run at large throughput on a single node.

Mistral NeMo offers a large context window of up to 128k tokens. Its reasoning, world knowledge, and coding accuracy are state-of-the-art in its size category. The model is designed for global, multilingual applications. It is trained on function calling, has a large context window, and is particularly strong in English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese, Korean, Arabic, and Hindi. This is a new step toward bringing frontier AI models to everyone’s hands in all languages that form human culture.

Mixtral 8x22B sets a new standard for performance and efficiency within the AI community. It is a sparse Mixture-of-Experts (SMoE) model that uses only 39B active parameters out of 141B, offering unparalleled cost efficiency for its size.

Mixtral 8x7B is a high-quality sparse mixture of experts model (SMoE) with open weights. Licensed under Apache 2.0. Mixtral outperforms Llama 2 70B on most benchmarks with 6x faster inference. It is the strongest open-weight model with a permissive license and the best model overall regarding cost/performance trade-offs. In particular, it matches or outperforms GPT3.5 on most standard benchmarks.

This variant of GPT-3.5 was specifically tuned to follow instructions more effectively. It was designed to better understand and adhere to user prompts, making it more suitable for applications where precise and instruction-following responses were necessary. This model demonstrated improved performance in understanding and executing specific tasks as directed by the user, though it still retained some limitations in understanding highly complex or nuanced instructions.

As a prominent model in OpenAI's Generative Pre-trained Transformer series, GPT-3.5 is known for its advanced language generation and understanding capabilities. It excels in creating coherent, contextually relevant text, and is adept at a wide range of language tasks including conversation, content creation, and problem-solving. GPT-3.5 is a powerful tool for applications in conversational AI and diverse forms of content generation, representing a significant advancement in natural language processing technology.

The a Generative Pre-trained Transformer series by OpenAI as of April 2023. GPT-4 is an advanced language model known for its increased capacity compared to its predecessors, offering more nuanced and accurate text generation. It boasts improved understanding and generation abilities, making it more effective in understanding context, generating human-like text, and providing more accurate and contextually relevant responses.

GPT-4-Turbo is a variant of the GPT-4 model, designed to offer faster response times while maintaining the sophisticated language understanding and generation capabilities of GPT-4. This model is particularly suited for applications that require both high-quality language processing and quick response times, making it ideal for interactive applications like chatbots or real-time content generation.

GPT-4o (“o” for “omni”) is a step towards much more natural human-computer interaction—it accepts as input any combination of text, audio, and image and generates any combination of text, audio, and image outputs. It can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time(opens in a new window) in a conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models.

GPT-4o mini enables a broad range of tasks with its low cost and latency, pass a large volume of context to the model, or interact with customers through fast, real-time text responses. GPT-4o mini surpasses GPT-3.5 Turbo and other small models on academic benchmarks across both textual intelligence and multimodal reasoning, and supports the same range of languages as GPT-4o.

Llama3 Sonar is Perplexity's latest model family. It surpasses their earlier Sonar models in cost-efficiency, speed, and performance.

Llama3 Sonar is Perplexity's latest model family. It surpasses their earlier Sonar models in cost-efficiency, speed, and performance.

Llama3 Sonar is Perplexity's latest model family. It surpasses their earlier Sonar models in cost-efficiency, speed, and performance.

Llama3 Sonar is Perplexity's latest model family. It surpasses their earlier Sonar models in cost-efficiency, speed, and performance.

Core is the largest & most capable model for complex tasks. Reka Core, approaches the best frontier models (OpenAI, 2023; Google, 2023; Anthropic, 2024) on both automatic evaluations and blind human evaluations. On image question answering benchmarks (e.g., MMMU, VQAv2), Core performs competitively to GPT4-V. Meanwhile, on multimodal chat, Core ranks as the second most preferred model under a blind third-party human evaluation setup, outperforming other models such as Claude 3 Opus. On text benchmarks, Core not only performs competitively to other frontier models on a set of well-established benchmarks (e.g., MMLU, GSM8K) but also outperforms GPT4-0613 on human evaluation. On video question answering (Perception-Test), Core outperforms Gemini Ultra.

Reka Edge is a 7B lightweight and local model. Reka Edge and Reka Flash are not only state-of-the-art but also outperform many much larger models, delivering outsized values for their respective compute class.

Reka Flash is a 21B fast and capable model. Reka Edge and Reka Flash are not only state-of-the-art but also outperform many much larger models, delivering outsized values for their respective compute class.

Ideogram Upscale enlarges the resolution of your Ideogram-generated images, as well as those you’ve uploaded using the upload function (available with the Plus subscription plan or higher), by up to two times (up to 2x), based on the default pixel dimensions of the nearest aspect ratio of your image. For example, if you upload an image that is 1500 pixels by 1500 pixels, the upscale will result in a 2048 pixels by 2048 pixels image. This is because the aspect ratio of your image is 1:1, and the default dimensions for images generated with this aspect ratio are 1024 pixels by 1024 pixels.

Ideogram 1.0 offers state-of-the-art text rendering, unprecedented photorealism and prompt adherence, and a new feature called Magic Prompt that helps you write detailed prompts for beautiful, creative images.

Ideogram 1.0 offers state-of-the-art text rendering, unprecedented photorealism and prompt adherence, and a new feature called Magic Prompt that helps you write detailed prompts for beautiful, creative images.

A text to image model with industry leading capabilities in generating realistic images, graphic design, typography, and more. Trained from scratch like all Ideogram models, Ideogram 2.0 significantly outperforms other text to image models across many quality metrics, including image-text alignment, overall subjective preference, and text rendering accuracy.

Ideogram is a text to image model with industry leading capabilities in generating realistic images, graphic design, typography, and more. Trained from scratch like all Ideogram models, Ideogram 2.0 significantly outperforms other text to image models across many quality metrics, including image-text alignment, overall subjective preference, and text rendering accuracy. The Remix tool is invaluable for making changes to an existing generated image or an uploaded one. When viewing or uploading an image, you can click the "Remix" button to reuse the selected image along with its associated prompt in a new creation.

The previous DALL·E model released in Nov 2022. The 2nd iteration of DALL·E with more realistic, accurate, and 4x greater resolution images than the original model.

DALL-E 3 is the SOTA model for image generation by OpenAI. DALL·E 3 understands significantly more nuance and detail than our previous systems, allowing you to easily translate your ideas into exceptionally accurate images.

Stable Diffusion 3 Medium is the latest and most advanced text-to-image AI model in the Stable Diffusion 3 series, comprising two billion parameters. It excels in photorealism, processes complex prompts, and generates clear text.

Stability AI's primary service for text-to-image generation, Stable Image Core represents the best quality achievable at high speed. No prompt engineering is required! Try asking for a style, a scene, or a character, and see what you get.

Stability AI's most advanced text to image generation service, Stable Image Ultra creates the highest quality images with unprecedented prompt understanding. Ultra excels in typography, complex compositions, dynamic lighting, vibrant hues, and overall cohesion and structure of an art piece. Made from the most advanced models, including Stable Diffusion 3, Ultra offers the best of the Stable Diffusion ecosystem.

ElevenLabs is an AI audio research and deployment company. AI audio models generate realistic, versatile and contextually-aware speech, voices, and sound effects across 32 languages.

TTS is an AI model that converts text to natural sounding spoken text. TTS-1 is the latest text to speech model, optimized for speed.

TTS is an AI model that converts text to natural sounding spoken text. TTS-1 is the latest text to speech model, optimized for speed.

Gemini 1.5 Pro is a foundation model that performs well at a variety of multimodal tasks such as visual understanding, classification, summarization, and creating content from image, audio and video. It's adept at processing visual and text inputs such as photographs, documents, infographics, and screenshots

Multimodal model that is designed for high volume, cost-effective applications. Gemini 1.5 Flash delivers speed and efficiency to build fast, lower-cost applications that don't compromise on quality.

Gemini is a family of generative AI models that lets developers generate content and solve problems. These models are designed and trained to handle both text and images as input.

Ideogram Describe is a powerful feature that allows you to detail the content of an image, whether uploaded or generated by Ideogram.

GPT-4-Turbo is a variant of the GPT-4 model, designed to offer faster response times while maintaining the sophisticated language understanding and generation capabilities of GPT-4. This model is particularly suited for applications that require both high-quality language processing and quick response times, making it ideal for interactive applications like chatbots or real-time content generation.

GPT-4o mini enables a broad range of tasks with its low cost and latency, pass a large volume of context to the model, or interact with customers through fast, real-time text responses. GPT-4o mini surpasses GPT-3.5 Turbo and other small models on academic benchmarks across both textual intelligence and multimodal reasoning, and supports the same range of languages as GPT-4o.

GPT-4o (“o” for “omni”) is a step towards much more natural human-computer interaction—it accepts as input any combination of text, audio, and image and generates any combination of text, audio, and image outputs. It can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time(opens in a new window) in a conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models.