Alternatives to WhichModel

Model Playground AI is an online platform that allows users to investigate, contrast, and prototype with more than 150 leading AI models within a cohesive interface. It features two primary modes: Explore for free-form prompt experimentation and Workflows for structured, repeatable tasks, where users can modify parameters such as temperature and max tokens, submit prompts to multiple models at once, and observe results side by side in real time. Additionally, it offers presets and saving capabilities to store settings and chat histories for convenient reproducibility, while API endpoints and a credit-based subscription model facilitate smooth integration into personal applications without hidden fees. With its lightweight, no-code design, the platform accommodates tasks related to text, images, video, and code generation, all from a single dashboard, simplifying the process of evaluating model performance, refining prompts, and speeding up AI-driven initiatives. Furthermore, the user-friendly interface enhances accessibility for both beginners and seasoned developers alike, making it an ideal choice for anyone looking to harness the potential of AI technology.

The official launch of the Seedance 1.0 API makes ByteDance’s industry-leading video generation technology accessible to creators worldwide. Recently ranked #1 globally in the Artificial Analysis benchmark for both T2V and I2V tasks, Seedance is recognized for its cinematic realism, smooth motion, and advanced multi-shot storytelling capabilities. Unlike single-scene models, it maintains subject identity, atmosphere, and style across multiple shots, enabling narrative video production at scale. Users benefit from precise instruction following, diverse stylistic expression, and studio-grade 1080p video output in just seconds. Pricing is transparent and cost-effective, with 2 million free tokens to start and affordable tiers at $1.8–$2.5 per million tokens, depending on whether you use the Lite or Pro model. For a 5-second 1080p video, the cost is under a dollar, making high-quality AI content creation both accessible and scalable. Beyond affordability, Seedance is optimized for high concurrency, meaning developers and teams can generate large volumes of videos simultaneously without performance loss. Designed for film production, marketing campaigns, storytelling, and product pitches, the Seedance API empowers businesses and individuals to scale their creativity with enterprise-grade tools.

Optimize the management of source code context and generate effective prompts efficiently. Ship alongside ChatGPT and Claude, the 16x Prompt tool enables developers to oversee source code context and prompts for tackling intricate coding challenges within existing codebases. By inputting your personal API key, you gain access to APIs from OpenAI, Anthropic, Azure OpenAI, OpenRouter, and other third-party services compatible with the OpenAI API, such as Ollama and OxyAPI. Utilizing these APIs ensures that your code remains secure, preventing it from being exposed to the training datasets of OpenAI or Anthropic. You can also evaluate the code outputs from various LLM models, such as GPT-4o and Claude 3.5 Sonnet, side by side, to determine the most suitable option for your specific requirements. Additionally, you can create and store your most effective prompts as task instructions or custom guidelines to apply across diverse tech stacks like Next.js, Python, and SQL. Enhance your prompting strategy by experimenting with different optimization settings for optimal results. Furthermore, you can organize your source code context through designated workspaces, allowing for the efficient management of multiple repositories and projects, facilitating seamless transitions between them. This comprehensive approach not only streamlines development but also fosters a more collaborative coding environment.

ChainForge serves as an open-source visual programming platform aimed at enhancing prompt engineering and evaluating large language models. This tool allows users to rigorously examine the reliability of their prompts and text-generation models, moving beyond mere anecdotal assessments. Users can conduct simultaneous tests of various prompt concepts and their iterations across different LLMs to discover the most successful combinations. Additionally, it assesses the quality of responses generated across diverse prompts, models, and configurations to determine the best setup for particular applications. Evaluation metrics can be established, and results can be visualized across prompts, parameters, models, and configurations, promoting a data-driven approach to decision-making. The platform also enables the management of multiple conversations at once, allows for the templating of follow-up messages, and supports the inspection of outputs at each interaction to enhance communication strategies. ChainForge is compatible with a variety of model providers, such as OpenAI, HuggingFace, Anthropic, Google PaLM2, Azure OpenAI endpoints, and locally hosted models like Alpaca and Llama. Users have the flexibility to modify model settings and leverage visualization nodes for better insights and outcomes. Overall, ChainForge is a comprehensive tool tailored for both prompt engineering and LLM evaluation, encouraging innovation and efficiency in this field.

Uncover the top AI-generated responses by engaging in comparison, sharing, and voting at thisorthis.ai, a platform designed to simplify the evaluation of different AI models and save you valuable time. You can test various prompts across multiple AI models, analyze their differences, and share your findings in real-time, effectively enhancing your AI strategy through insightful, data-driven comparisons that lead to quicker, informed decisions. As your definitive resource for AI model comparisons, thisorthis.ai offers a seamless side-by-side view of responses generated by different models, allowing you to determine which one delivers the most accurate answers or simply to enjoy exploring the range of available responses. By entering any prompt, you can effortlessly view and compare the outputs of renowned models like GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Flash, and others with just a simple click. Additionally, your participation in voting for the best responses helps to emphasize which models are performing exceptionally well. You can also easily share links to your prompts along with the AI-generated responses with others, fostering a collaborative exploration of AI capabilities. This interactive experience not only enhances your understanding of AI but also connects you with a community of users interested in the evolving landscape of artificial intelligence.

StarCoder and StarCoderBase represent advanced Large Language Models specifically designed for code, developed using openly licensed data from GitHub, which encompasses over 80 programming languages, Git commits, GitHub issues, and Jupyter notebooks. In a manner akin to LLaMA, we constructed a model with approximately 15 billion parameters trained on a staggering 1 trillion tokens. Furthermore, we tailored the StarCoderBase model with 35 billion Python tokens, leading to the creation of what we now refer to as StarCoder. Our evaluations indicated that StarCoderBase surpasses other existing open Code LLMs when tested against popular programming benchmarks and performs on par with or even exceeds proprietary models like code-cushman-001 from OpenAI, the original Codex model that fueled early iterations of GitHub Copilot. With an impressive context length exceeding 8,000 tokens, the StarCoder models possess the capability to handle more information than any other open LLM, thus paving the way for a variety of innovative applications. This versatility is highlighted by our ability to prompt the StarCoder models through a sequence of dialogues, effectively transforming them into dynamic technical assistants that can provide support in diverse programming tasks.

JustSimple.Chat serves as an AI-driven inbound sales and support agent that can be quickly integrated into any website within minutes. It features conversational chat and voice functionalities in over 175 languages, ensuring engagement with site visitors around the clock, guiding them toward suitable products or resources, and capturing essential contact details without losing any potential leads. After implementation, it customizes every interaction through engaging, personalized conversations and automated follow-ups, effectively qualifying leads, scheduling meetings with effortless calendar integrations, and boosting lead generation by up to three times while also doubling the number of qualified meetings. The platform employs enterprise-grade automation to apply tailored rules and machine-learning algorithms, allowing only the most complex inquiries to be forwarded to human agents for further handling, while intuitive dashboards monitor key performance indicators, lead traffic, and return on investment. Additionally, it is designed with compliance in mind, incorporating support for SOC 2, GDPR, and CCPA to safeguard data privacy and security, while also providing businesses with the insights they need to enhance their customer engagement strategies over time. By leveraging these advanced features, companies can ensure a more efficient sales process that maximizes both customer satisfaction and operational effectiveness.

Klu.ai, a Generative AI Platform, simplifies the design, deployment, and optimization of AI applications. Klu integrates your Large Language Models and incorporates data from diverse sources to give your applications unique context. Klu accelerates the building of applications using language models such as Anthropic Claude (Azure OpenAI), GPT-4 (Google's GPT-4), and over 15 others. It allows rapid prompt/model experiments, data collection and user feedback and model fine tuning while cost-effectively optimising performance. Ship prompt generation, chat experiences and workflows in minutes. Klu offers SDKs for all capabilities and an API-first strategy to enable developer productivity. Klu automatically provides abstractions to common LLM/GenAI usage cases, such as: LLM connectors and vector storage, prompt templates, observability and evaluation/testing tools.

Fuser is a browser-based, model-agnostic AI workspace for people who actually make things—designers, creative directors, studios, and in-house teams. Most AI tools live at two extremes: one-click toys that spit out a single image, or hardcore toolchains like ComfyUI that assume you have GPUs, config patience, and time. Fuser tries to live in the middle. You get a node-based canvas in your browser where you can wire up text, image, video, audio, 3D, and chatbot/LLM models into multimodal workflows. No local install, no Docker, no drivers. Just open a link and start building. Under the hood, Fuser is provider-agnostic. You can plug in your own API keys from OpenAI, Anthropic, Runway, Fal, OpenRouter, and others, or use Fuser’s own pay-as-you-go credits (which don’t expire). That makes it easier to experiment across models, keep costs visible, and avoid getting locked into a single vendor. The main users are design and creative teams who need to move from brief to concepts quickly: campaign moodboards, product and industrial visualizations, motion tests, content pipelines, and experimental media. Instead of a pile of ad-hoc prompts and screenshots, they get reusable workflows they can share, version, and improve. If you like the power and transparency of node graphs but you’d rather not babysit local installs and drivers, Fuser gives you that orchestration layer as a web app, tuned for people whose job is to ship work, not maintain infra.

Introducing a robust prompt editing tool designed to assist you in crafting, enhancing, testing, and sharing prompts efficiently. This platform includes every essential feature for developing advanced prompts. During its beta phase, ZenPrompts is fully accessible at no cost; simply provide your own OpenAI API key to begin. With ZenPrompts, you can curate a collection of prompts that highlight your skills in the evolving landscape of AI and LLMs. The design and engineering of intricate prompts demand the ability to easily evaluate outputs from various OpenAI models. ZenPrompts facilitates this by allowing you to contrast model results side-by-side, empowering you to select the most suitable model based on factors like quality, cost, or performance requirements. Furthermore, ZenPrompts presents a sleek, minimalist environment to showcase your prompt collection. With its clean design and intuitive user experience, the platform focuses on ensuring your creativity shines through. Enhance the effectiveness of your prompts by displaying them with elegance, capturing the attention of your audience effortlessly. In addition, ZenPrompts continually evolves, incorporating user feedback to refine its features and improve your experience.

Stableoutput is an intuitive AI chat platform that enables users to engage with leading AI models, including OpenAI's GPT-4o and Anthropic's Claude 3.5 Sonnet, without the need for any programming skills. It functions on a bring-your-own-key system, allowing users to input their own API keys, which are kept securely in the local storage of their browser; these keys are never sent to Stableoutput's servers, thus maintaining user privacy and security. The platform comes equipped with various features such as cloud synchronization, a tracker for API usage, and options for customizing system prompts along with model parameters like temperature and maximum tokens. Users are also able to upload various file types, including PDFs, images, and code files for enhanced AI analysis, enabling more tailored and context-rich interactions. Additional features include the ability to pin conversations and share chats with specific visibility settings, as well as managing message requests to help streamline API usage. With a one-time payment, Stableoutput provides users with lifetime access to these robust features, making it a valuable tool for anyone looking to harness the power of AI in a user-friendly manner.

LMArena is an online platform designed for users to assess large language models via anonymous pair-wise comparisons; participants submit prompts, receive responses from two unidentified models, and then cast votes to determine which answer is superior, with model identities disclosed only after voting to ensure a fair evaluation of quality. The platform compiles the votes into leaderboards and rankings, enabling model contributors to compare their performance against others and receive feedback based on actual usage. By supporting a variety of models from both academic institutions and industry players, LMArena encourages community involvement through hands-on model testing and peer evaluations, while also revealing the strengths and weaknesses of the models in real-time interactions. This innovative approach expands beyond traditional benchmark datasets, capturing evolving user preferences and facilitating live comparisons, thus allowing both users and developers to discern which models consistently provide the best responses in practice. Ultimately, LMArena serves as a vital resource for understanding the competitive landscape of language models and improving their development.

Utilize the most advanced AI model router to ensure you engage the optimal model at the perfect moment. Maximize the effectiveness of each model with unmatched speed and accuracy. Not only does Not Diamond function seamlessly right away, but you can also create a personalized router using your own evaluation data, thus tailoring model routing specifically to your needs. Choose the appropriate model faster than it takes to process a single token, allowing you to make use of more efficient and cost-effective models without compromising on quality. Craft the ideal prompt for each language model (LLM) so that you consistently access the right model with the appropriate prompt, eliminating the need for manual adjustments and trial-and-error. Importantly, Not Diamond operates as a direct client-side tool rather than a proxy, ensuring all requests are securely handled. You can activate fuzzy hashing through our API or deploy it directly within your infrastructure to enhance security. For any given input, Not Diamond instinctively identifies the most suitable model to generate a response, achieving remarkable performance that surpasses all leading foundation models across key benchmarks. Moreover, this capability not only streamlines workflows but also enhances overall productivity in AI-driven tasks.

ModelMatch is a web-based service that enables users to assess leading open-source vision-language models for image analysis tasks without requiring any programming skills. Individuals can upload as many as four images and enter particular prompts to obtain comprehensive evaluations from various models at the same time. The platform assesses models that vary in size from 1 billion to 12 billion parameters, all of which are open-source and come with commercial licenses. Each model is assigned a quality score ranging from 1 to 10, reflecting its effectiveness for the specified task, as well as providing metrics on processing times and real-time updates throughout the analysis process. In addition, the platform's user-friendly interface makes it accessible for those who may not have technical expertise, further broadening its appeal among a diverse range of users.

DeepSeek-R1 is a cutting-edge open-source reasoning model created by DeepSeek, aimed at competing with OpenAI's Model o1. It is readily available through web, app, and API interfaces, showcasing its proficiency in challenging tasks such as mathematics and coding, and achieving impressive results on assessments like the American Invitational Mathematics Examination (AIME) and MATH. Utilizing a mixture of experts (MoE) architecture, this model boasts a remarkable total of 671 billion parameters, with 37 billion parameters activated for each token, which allows for both efficient and precise reasoning abilities. As a part of DeepSeek's dedication to the progression of artificial general intelligence (AGI), the model underscores the importance of open-source innovation in this field. Furthermore, its advanced capabilities may significantly impact how we approach complex problem-solving in various domains.

Introducing Narrow AI: Eliminating the Need for Prompt Engineering by Engineers Narrow AI seamlessly generates, oversees, and fine-tunes prompts for any AI model, allowing you to launch AI functionalities ten times quicker and at significantly lower costs. Enhance quality while significantly reducing expenses - Slash AI expenditures by 95% using more affordable models - Boost precision with Automated Prompt Optimization techniques - Experience quicker responses through models with reduced latency Evaluate new models in mere minutes rather than weeks - Effortlessly assess prompt effectiveness across various LLMs - Obtain benchmarks for cost and latency for each distinct model - Implement the best-suited model tailored to your specific use case Deliver LLM functionalities ten times faster - Automatically craft prompts at an expert level - Adjust prompts to accommodate new models as they become available - Fine-tune prompts for optimal quality, cost efficiency, and speed while ensuring a smooth integration process for your applications.

Kerlig is an AI writing assistant designed specifically for macOS, offering a range of features that help users enhance their written communication in various apps. With multi-language support, Kerlig allows users to proofread, summarize, translate, and extract key information from documents, web pages, and ebooks. Its seamless integration into any macOS app makes it ideal for professionals looking to streamline their workflow and avoid switching between multiple tools. The app also includes customizable presets, so users can tailor their experience to match their writing style and needs. Kerlig supports over 350 AI models, including OpenAI, Anthropic, and Google, ensuring users have access to powerful AI tools at their fingertips. The software is highly regarded for its ease of use, allowing users to quickly generate content, correct spelling errors, and brainstorm new ideas. With a pay-once pricing model and no subscription required, Kerlig provides flexibility and a cost-effective solution for anyone looking to improve their productivity with AI.

Claude Sonnet 4.5 represents Anthropic's latest advancement in AI, crafted to thrive in extended coding environments, complex workflows, and heavy computational tasks while prioritizing safety and alignment. It sets new benchmarks with its top-tier performance on the SWE-bench Verified benchmark for software engineering and excels in the OSWorld benchmark for computer usage, demonstrating an impressive capacity to maintain concentration for over 30 hours on intricate, multi-step assignments. Enhancements in tool management, memory capabilities, and context interpretation empower the model to engage in more advanced reasoning, leading to a better grasp of various fields, including finance, law, and STEM, as well as a deeper understanding of coding intricacies. The system incorporates features for context editing and memory management, facilitating prolonged dialogues or multi-agent collaborations, while it also permits code execution and the generation of files within Claude applications. Deployed at AI Safety Level 3 (ASL-3), Sonnet 4.5 is equipped with classifiers that guard against inputs or outputs related to hazardous domains and includes defenses against prompt injection, ensuring a more secure interaction. This model signifies a significant leap forward in the intelligent automation of complex tasks, aiming to reshape how users engage with AI technologies.

Anthropic’s release of Claude Opus 4.5 introduces a frontier AI model that excels at coding, complex reasoning, deep research, and long-context tasks. It sets new performance records on real-world engineering benchmarks, handling multi-system debugging, ambiguous instructions, and cross-domain problem solving with greater precision than earlier versions. Testers and early customers reported that Opus 4.5 “just gets it,” offering creative reasoning strategies that even benchmarks fail to anticipate. Beyond raw capability, the model brings stronger alignment and safety, with notable advances in prompt-injection resistance and behavior consistency in high-stakes scenarios. The Claude Developer Platform also gains richer controls including effort tuning, multi-agent orchestration, and context management improvements that significantly boost efficiency. Claude Code becomes more powerful with enhanced planning abilities, multi-session desktop support, and better execution of complex development workflows. In the Claude apps, extended memory and automatic context summarization enable longer, uninterrupted conversations. Together, these upgrades showcase Opus 4.5 as a highly capable, secure, and versatile model designed for both professional workloads and everyday use.

Professional prompt editors utilizing models like GPT-4o, Claude 3 Opus, and Gemini-1.5, along with function call simulation capabilities, enable the creation of diverse projects tailored for various use cases with distinct project members and configurations. Each member can have different access control levels, promoting collaborative prompting and sharing. Users can incorporate multiple image inputs in their messages while having control over individual detail parameters, facilitating easy manipulation of each message. The function call schema editor allows for simulation of function call returns seamlessly, and inline variables in prompts enable the running and comparison of results across different variable groups simultaneously. All sensitive information is secured through RSA-OAEP and AES-256-GCM encryption during both transmission and storage, ensuring privacy and data integrity. With Knit, no edits are ever lost, as all edit history is meticulously saved and can be restored at any moment. The platform is compatible with various models, including OpenAI, Claude, and Azure OpenAI, with plans to expand support for even more models. Almost all API parameters can be adjusted within the prompt editors, allowing users to optimize their prompts effectively and discover the most suitable parameters for their needs. This comprehensive approach ensures a streamlined experience for prompt editing and model interaction, fostering creativity and collaboration across teams.

Vello is an exceptional platform for engaging with AI, offering rapid, robust, and collaborative multi-player AI chat that consolidates leading models and human partners in one accessible location. It is compatible across web, mobile, and desktop interfaces, providing a swift, keyboard-driven experience that enhances productivity. By integrating models from OpenAI, Anthropic, Google, Facebook, Cohere, and its proprietary web and document models, Vello facilitates immediate research with accurate source citations, effortless document composition, editing, and code refinement. The platform's team spaces foster collaboration between humans and AI, enabling users to work collaboratively in shared environments and chat rooms to complete tasks efficiently. Additionally, users have the ability to craft various AI personas, train them using diverse file types (including PDF, DOCX, code, CSV), and deploy them either individually or within multi-persona chat rooms, resulting in intricate workflows through round-robin model responses. This flexibility allows for a dynamic and highly customizable approach to utilizing AI technologies in a multitude of applications.

DeepSeek V3.1 stands as a revolutionary open-weight large language model, boasting an impressive 685-billion parameters and an expansive 128,000-token context window, which allows it to analyze extensive documents akin to 400-page books in a single invocation. This model offers integrated functionalities for chatting, reasoning, and code creation, all within a cohesive hybrid architecture that harmonizes these diverse capabilities. Furthermore, V3.1 accommodates multiple tensor formats, granting developers the versatility to enhance performance across various hardware setups. Preliminary benchmark evaluations reveal strong results, including a remarkable 71.6% on the Aider coding benchmark, positioning it competitively with or even superior to systems such as Claude Opus 4, while achieving this at a significantly reduced cost. Released under an open-source license on Hugging Face with little publicity, DeepSeek V3.1 is set to revolutionize access to advanced AI technologies, potentially disrupting the landscape dominated by conventional proprietary models. Its innovative features and cost-effectiveness may attract a wide range of developers eager to leverage cutting-edge AI in their projects.

Ninja AI is a monthly subscription that combines the world's best AI agents and models into one affordable package. Ninja can help with research, writing, image generation, code creation, and meeting scheduling. Access the best AI models by Meta, OpenAI Anthropic, Google and more. Choose the models that you want to use and compare results across AI models. Plans with unlimited tasks start at $5/month. Try it for free at Myninja.ai.

CotranslatorAI is a third-party Windows program which enables you to communicate with OpenAI GPT models directly without using your web browser. To speed up your work, the program can process text selected in any application with OpenAI GPT using keyboard shortcuts, e.g. obtain AI response on the selected text using your instructions or even replace it with the AI response automatically. It also offers an unlimited, reusable prompt library which you can customize based on your workflows. CotranslatorAI lets you choose between current GPT models and control other setting. Originally designed for language experts, CotranslatorAI is suitable for academics, administrators, business managers, educationalists, journalists, lawyers, etc., since users are encouraged to create their own prompts, in addition to the basic prompt library bundled with it. CotranslatorAI communicates DIRECTLY with OpenAI’s APIs. It does not employ intermediary servers, thus ensuring your privacy and minimizing downtime.

SillyTavern is an open-source AI chat platform offered at no cost, enabling users to design and engage with AI-created characters, making it perfect for activities such as role-playing, storytelling, and fan fiction. This user-friendly interface is installed locally and connects to various large language models, including OpenAI, KoboldAI, and Claude, thus providing a flexible and immersive experience tailored to individual preferences. Participants can take part in one-on-one or group conversations, create prompts to guide discussions, and make use of functionalities like chat bookmarks and a personalized interface. The platform is extensible and works across multiple devices, enhancing its accessibility. Although the software itself is free to use, users must link it to an AI model backend, which might incur extra charges depending on the selected model. Additionally, users can add bookmarks at any part of a chat, allowing for easy navigation to revisit conversations or redirect discussions in new directions. With its engaging features and adaptability, SillyTavern caters to a wide audience of creative individuals seeking to explore their imaginations.

PromptSignal is an advanced analytics platform that tracks the visibility of prominent large language models, including ChatGPT, Claude, Perplexity, and Gemini, in relation to brand mentions, rankings, and descriptions. With the growing reliance of consumers on AI assistants for product research and evaluation over traditional search engines, PromptSignal equips businesses with insights to enhance their brand's presence in AI-generated content. The platform offers comprehensive daily monitoring across various models, supplying visibility scores, rankings, sentiment assessments, and competitive benchmarks to users. Additionally, it features customized prompt suggestions that allow brands to evaluate their performance and provides strategic recommendations aimed at refining their positioning and perception within LLM responses. Key metrics such as brand visibility, competitor analysis, sentiment scores, ranking positions, and prompt efficacy enable teams to identify areas of success and opportunities for improvement, ultimately guiding their strategies in the evolving landscape of AI-assisted consumer behavior. By leveraging these insights, brands can proactively adapt and thrive in a market increasingly influenced by artificial intelligence.

OpenPipe offers an efficient platform for developers to fine-tune their models. It allows you to keep your datasets, models, and evaluations organized in a single location. You can train new models effortlessly with just a click. The system automatically logs all LLM requests and responses for easy reference. You can create datasets from the data you've captured, and even train multiple base models using the same dataset simultaneously. Our managed endpoints are designed to handle millions of requests seamlessly. Additionally, you can write evaluations and compare the outputs of different models side by side for better insights. A few simple lines of code can get you started; just swap out your Python or Javascript OpenAI SDK with an OpenPipe API key. Enhance the searchability of your data by using custom tags. Notably, smaller specialized models are significantly cheaper to operate compared to large multipurpose LLMs. Transitioning from prompts to models can be achieved in minutes instead of weeks. Our fine-tuned Mistral and Llama 2 models routinely exceed the performance of GPT-4-1106-Turbo, while also being more cost-effective. With a commitment to open-source, we provide access to many of the base models we utilize. When you fine-tune Mistral and Llama 2, you maintain ownership of your weights and can download them whenever needed. Embrace the future of model training and deployment with OpenPipe's comprehensive tools and features.

DeepEval offers an intuitive open-source framework designed for the assessment and testing of large language model systems, similar to what Pytest does but tailored specifically for evaluating LLM outputs. It leverages cutting-edge research to measure various performance metrics, including G-Eval, hallucinations, answer relevancy, and RAGAS, utilizing LLMs and a range of other NLP models that operate directly on your local machine. This tool is versatile enough to support applications developed through methods like RAG, fine-tuning, LangChain, or LlamaIndex. By using DeepEval, you can systematically explore the best hyperparameters to enhance your RAG workflow, mitigate prompt drift, or confidently shift from OpenAI services to self-hosting your Llama2 model. Additionally, the framework features capabilities for synthetic dataset creation using advanced evolutionary techniques and integrates smoothly with well-known frameworks, making it an essential asset for efficient benchmarking and optimization of LLM systems. Its comprehensive nature ensures that developers can maximize the potential of their LLM applications across various contexts.

GradientJ offers a comprehensive suite of tools designed to facilitate the rapid development of large language model applications, ensuring their long-term management. You can explore and optimize your prompts by saving different versions and evaluating them against established benchmarks. Additionally, you can streamline the orchestration of intricate applications by linking prompts and knowledge sources into sophisticated APIs. Moreover, boosting the precision of your models is achievable through the incorporation of your unique data assets, thus enhancing overall performance. This platform empowers developers to innovate and refine their models continuously.

Strands Agents presents a streamlined, code-oriented framework aimed at facilitating the creation of AI agents, which capitalizes on the advanced reasoning skills of contemporary language models to ease the development process. With just a few lines of Python code, developers can swiftly construct agents by outlining a prompt and specifying a set of tools, empowering the agents to carry out intricate tasks independently. The framework is compatible with various model providers, such as Amazon Bedrock (with Claude 3.7 Sonnet as the default), Anthropic, OpenAI, among others, providing users with diverse options for model selection. An adaptable agent loop is a standout feature, managing user inputs, determining appropriate tool usage, executing those tools, and crafting responses, thereby accommodating both streaming and non-streaming interactions. Furthermore, the inclusion of built-in tools, along with the option to create custom tools, enables agents to undertake a broad spectrum of activities that extend well beyond mere text generation, enhancing their utility in various applications. This versatility positions Strands Agents as an innovative solution in the realm of AI agent development.

Dify serves as an open-source platform aimed at enhancing the efficiency of developing and managing generative AI applications. It includes a wide array of tools, such as a user-friendly orchestration studio for designing visual workflows, a Prompt IDE for testing and refining prompts, and advanced LLMOps features for the oversight and enhancement of large language models. With support for integration with multiple LLMs, including OpenAI's GPT series and open-source solutions like Llama, Dify offers developers the versatility to choose models that align with their specific requirements. Furthermore, its Backend-as-a-Service (BaaS) capabilities allow for the effortless integration of AI features into existing enterprise infrastructures, promoting the development of AI-driven chatbots, tools for document summarization, and virtual assistants. This combination of tools and features positions Dify as a robust solution for enterprises looking to leverage generative AI technologies effectively.

Streamline your prompt testing, collaboration, versioning, and deployment all in one location with PromptHub. Eliminate the hassle of constant copy and pasting by leveraging variables for easier prompt creation. Bid farewell to cumbersome spreadsheets and effortlessly compare different outputs side-by-side while refining your prompts. Scale your testing with batch processing to effectively manage your datasets and prompts. Ensure the consistency of your prompts by testing across various models, variables, and parameters. Simultaneously stream two conversations and experiment with different models, system messages, or chat templates to find the best fit. You can commit prompts, create branches, and collaborate without any friction. Our system detects changes to prompts, allowing you to concentrate on analyzing outputs. Facilitate team reviews of changes, approve new versions, and keep everyone aligned. Additionally, keep track of requests, associated costs, and latency with ease. PromptHub provides a comprehensive solution for testing, versioning, and collaborating on prompts within your team, thanks to its GitHub-style versioning that simplifies the iterative process and centralizes your work. With the ability to manage everything in one place, your team can work more efficiently and effectively than ever before.

Appaca is an innovative no-code platform that empowers users to design and launch AI-driven applications quickly and efficiently. The platform boasts an extensive array of features, such as a customizable interface editor, action workflows, an AI studio for developing models, and a built-in database to facilitate data management. With support for integration with top AI models like OpenAI's GPT, Google's Gemini, Anthropic's Claude, and OpenAI's DALL·E 3, users can access a wide range of functionalities, including both text and image generation capabilities. Furthermore, Appaca includes user management and monetization options, such as Stripe integration for subscription services and AI credit billing, making it ideal for businesses, agencies, influencers, and startups aiming to create white-label AI solutions, web applications, internal tools, chatbots, and much more without any coding knowledge. In addition to these features, Appaca’s user-friendly interface ensures that even those without technical backgrounds can easily navigate and utilize the platform to its full potential.

Tülu 3 is a cutting-edge language model created by the Allen Institute for AI (Ai2) that aims to improve proficiency in fields like knowledge, reasoning, mathematics, coding, and safety. It is based on the Llama 3 Base and undergoes a detailed four-stage post-training regimen: careful prompt curation and synthesis, supervised fine-tuning on a wide array of prompts and completions, preference tuning utilizing both off- and on-policy data, and a unique reinforcement learning strategy that enhances targeted skills through measurable rewards. Notably, this open-source model sets itself apart by ensuring complete transparency, offering access to its training data, code, and evaluation tools, thus bridging the performance divide between open and proprietary fine-tuning techniques. Performance assessments reveal that Tülu 3 surpasses other models with comparable sizes, like Llama 3.1-Instruct and Qwen2.5-Instruct, across an array of benchmarks, highlighting its effectiveness. The continuous development of Tülu 3 signifies the commitment to advancing AI capabilities while promoting an open and accessible approach to technology.

Prompt Selected is an innovative browser extension that harnesses the power of AI, enabling users to apply personalized ChatGPT prompts to any highlighted text while necessitating their own OpenAI API key for operation (BYOK). It offers an extensive range of unlimited prompts, a variety of prebuilt examples, and compatibility with different GPT models, making tasks like grammar correction, translation, and text summarization effortless. The extension prioritizes user data security by employing local key storage and ensuring no tracking occurs. This combination of features allows users to tailor their AI interactions effectively, all within one versatile and customizable tool. Embrace the future of text manipulation and take charge of your AI functionalities with this remarkable extension.

Repo Prompt is an AI coding assistant designed specifically for macOS, which serves as a context engineering tool that empowers developers to interact with and refine codebases through the use of large language models. By enabling users to select particular files or directories, it allows for the creation of structured prompts that contain only the most relevant context, thereby facilitating the review and application of AI-generated code alterations as diffs instead of requiring rewrites of entire files, which ensures meticulous and traceable modifications. Additionally, it features a visual file explorer for efficient project navigation, an intelligent context builder, and CodeMaps that minimize token usage while enhancing the models' comprehension of project structures. Users benefit from multi-model support, enabling them to utilize their own API keys from various providers such as OpenAI, Anthropic, Gemini, and Azure, ensuring that all processing remains local and private unless the user chooses to send code to a language model. Repo Prompt is versatile, functioning as both an independent chat/workflow interface and as an MCP (Model Context Protocol) server, allowing for seamless integration with AI editors, making it an essential tool in modern software development. Overall, its robust features significantly streamline the coding process while maintaining a strong emphasis on user control and privacy.

Qwen3-Coder is an advanced code model that comes in various sizes, prominently featuring the 480B-parameter Mixture-of-Experts version (with 35B active) that inherently accommodates 256K-token contexts, which can be extended to 1M, and demonstrates cutting-edge performance in Agentic Coding, Browser-Use, and Tool-Use activities, rivaling Claude Sonnet 4. With a pre-training phase utilizing 7.5 trillion tokens (70% of which are code) and synthetic data refined through Qwen2.5-Coder, it enhances both coding skills and general capabilities, while its post-training phase leverages extensive execution-driven reinforcement learning across 20,000 parallel environments to excel in multi-turn software engineering challenges like SWE-Bench Verified without the need for test-time scaling. Additionally, the open-source Qwen Code CLI, derived from Gemini Code, allows for the deployment of Qwen3-Coder in agentic workflows through tailored prompts and function calling protocols, facilitating smooth integration with platforms such as Node.js and OpenAI SDKs. This combination of robust features and flexible accessibility positions Qwen3-Coder as an essential tool for developers seeking to optimize their coding tasks and workflows.

Entry Point AI serves as a cutting-edge platform for optimizing both proprietary and open-source language models. It allows users to manage prompts, fine-tune models, and evaluate their performance all from a single interface. Once you hit the ceiling of what prompt engineering can achieve, transitioning to model fine-tuning becomes essential, and our platform simplifies this process. Rather than instructing a model on how to act, fine-tuning teaches it desired behaviors. This process works in tandem with prompt engineering and retrieval-augmented generation (RAG), enabling users to fully harness the capabilities of AI models. Through fine-tuning, you can enhance the quality of your prompts significantly. Consider it an advanced version of few-shot learning where key examples are integrated directly into the model. For more straightforward tasks, you have the option to train a lighter model that can match or exceed the performance of a more complex one, leading to reduced latency and cost. Additionally, you can configure your model to avoid certain responses for safety reasons, which helps safeguard your brand and ensures proper formatting. By incorporating examples into your dataset, you can also address edge cases and guide the behavior of the model, ensuring it meets your specific requirements effectively. This comprehensive approach ensures that you not only optimize performance but also maintain control over the model's responses.

EXAONE Deep represents a collection of advanced language models that are enhanced for reasoning, created by LG AI Research, and come in sizes of 2.4 billion, 7.8 billion, and 32 billion parameters. These models excel in a variety of reasoning challenges, particularly in areas such as mathematics and coding assessments. Significantly, the EXAONE Deep 2.4B model outshines other models of its size, while the 7.8B variant outperforms both open-weight models of similar dimensions and the proprietary reasoning model known as OpenAI o1-mini. Furthermore, the EXAONE Deep 32B model competes effectively with top-tier open-weight models in the field. The accompanying repository offers extensive documentation that includes performance assessments, quick-start guides for leveraging EXAONE Deep models with the Transformers library, detailed explanations of quantized EXAONE Deep weights formatted in AWQ and GGUF, as well as guidance on how to run these models locally through platforms like llama.cpp and Ollama. Additionally, this resource serves to enhance user understanding and accessibility to the capabilities of EXAONE Deep models.

Qwen3-Coder is a versatile coding model that comes in various sizes, prominently featuring the 480B-parameter Mixture-of-Experts version with 35B active parameters, which naturally accommodates 256K-token contexts that can be extended to 1M tokens. This model achieves impressive performance that rivals Claude Sonnet 4, having undergone pre-training on 7.5 trillion tokens, with 70% of that being code, and utilizing synthetic data refined through Qwen2.5-Coder to enhance both coding skills and overall capabilities. Furthermore, the model benefits from post-training techniques that leverage extensive, execution-guided reinforcement learning, which facilitates the generation of diverse test cases across 20,000 parallel environments, thereby excelling in multi-turn software engineering tasks such as SWE-Bench Verified without needing test-time scaling. In addition to the model itself, the open-source Qwen Code CLI, derived from Gemini Code, empowers users to deploy Qwen3-Coder in dynamic workflows with tailored prompts and function calling protocols, while also offering smooth integration with Node.js, OpenAI SDKs, and environment variables. This comprehensive ecosystem supports developers in optimizing their coding projects effectively and efficiently.

Grok 4 Heavy represents xAI’s flagship AI model, leveraging a multi-agent architecture to deliver exceptional reasoning, problem-solving, and multimodal understanding. Developed using the Colossus supercomputer, it achieves a remarkable 50% score on the HLE benchmark, placing it among the leading AI models worldwide. This version can process text, images, and is expected to soon support video inputs, enabling richer contextual comprehension. Grok 4 Heavy is designed for advanced users, including developers and researchers, who demand state-of-the-art AI capabilities for complex scientific and technical tasks. Available exclusively through a $300/month SuperGrok Heavy subscription, it offers early access to future innovations like video generation. xAI has addressed past controversies by strengthening content moderation and removing harmful prompts. The platform aims to push AI boundaries while balancing ethical considerations. Grok 4 Heavy is positioned as a formidable competitor to other leading AI systems.

GPT-Realtime, OpenAI's latest and most sophisticated speech-to-speech model, is now available via the fully operational Realtime API. This model produces audio that is not only highly natural but also expressive, allowing users to finely adjust elements such as tone, speed, and accent. It is capable of understanding complex human audio cues, including laughter, can switch languages seamlessly in the middle of a conversation, and accurately interprets alphanumeric information such as phone numbers in various languages. With a notable enhancement in reasoning and instruction-following abilities, it has achieved impressive scores of 82.8% on the BigBench Audio benchmark and 30.5% on MultiChallenge. Additionally, it features improved function calling capabilities, demonstrating greater reliability, speed, and accuracy, with a score of 66.5% on ComplexFuncBench. The model also facilitates asynchronous tool invocation, ensuring that dialogues flow smoothly even during extended calls. Furthermore, the Realtime API introduces groundbreaking features like support for image input, integration with SIP phone networks, connections to remote MCP servers, and the ability to reuse conversation prompts effectively. These advancements make it an invaluable tool for enhancing communication technology.

LMOps is a stack that allows you to launch production-ready applications for monitoring, model management and more. Portkey is a replacement for OpenAI or any other provider APIs. Portkey allows you to manage engines, parameters and versions. Switch, upgrade, and test models with confidence. View aggregate metrics for your app and users to optimize usage and API costs Protect your user data from malicious attacks and accidental exposure. Receive proactive alerts if things go wrong. Test your models in real-world conditions and deploy the best performers. We have been building apps on top of LLM's APIs for over 2 1/2 years. While building a PoC only took a weekend, bringing it to production and managing it was a hassle! We built Portkey to help you successfully deploy large language models APIs into your applications. We're happy to help you, regardless of whether or not you try Portkey!

Molmo 2 represents a cutting-edge suite of open vision-language models that come with completely accessible weights, training data, and code, thereby advancing the original Molmo series' capabilities in grounded image comprehension to encompass video and multiple image inputs. This evolution enables sophisticated video analysis, including pointing, tracking, dense captioning, and question-answering functionalities, all of which demonstrate robust spatial and temporal reasoning across frames. The suite consists of three distinct models: an 8 billion-parameter variant tailored for comprehensive video grounding and QA tasks, a 4 billion-parameter model that prioritizes efficiency, and a 7 billion-parameter model backed by Olmo, which features a fully open end-to-end architecture that includes the foundational language model. Notably, these new models surpass their predecessors on key benchmarks, setting unprecedented standards for open-model performance in image and video comprehension tasks. Furthermore, they often rival significantly larger proprietary systems while being trained on a much smaller dataset compared to similar closed models, showcasing their efficiency and effectiveness in the field. This impressive achievement marks a significant advancement in the accessibility and performance of AI-driven visual understanding technologies.

ChatBetter is an all-in-one AI chat solution that provides seamless access to leading large language models through a user-friendly interface. It intelligently directs your queries to the most appropriate model for each task, anticipates the required reasoning level, and allows you to compare the top 2–3 responses side by side to analyze different perspectives and resolve discrepancies. You can conveniently combine those responses into a cohesive answer. The platform enhances efficiency with capabilities like chaining different models for intricate workflows (such as leveraging analytical models for research, structured planning models, and creative writing models), folder-based organization, a searchable history, context windows that maintain continuity during conversations, and editable memory features. Additionally, for team collaboration, ChatBetter offers single sign-on options, comprehensive admin controls, customizable branding, collaborative tools, role-based access permissions, multi-factor authentication, IP restrictions, and more. This comprehensive approach ensures that teams can work together more effectively while maintaining security and personalization.