Best AI Models - Page 18
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Ling 2.6 Flash
Ant Group
Ling 2.6 Flash is the latest cost-effective model in the Ling series, built on a Mixture of Experts architecture with 104B total parameters and 7.4B activated parameters. It is designed to achieve an optimal balance between inference performance and compute cost, making it suitable for general-purpose scenarios where strong reasoning capability, high throughput, and efficient deployment matter. Ling’s MoE architecture routes each token to activate only the most relevant expert subnetworks, compressing actual computation to a minimal fraction while maintaining large-scale model capacity. Ling 2.6 Flash provides a native 256K context window and can process approximately 200,000 characters of long-form input, with reliable long-range information retrieval whether key information appears at the beginning, middle, or end of the context. Its aggregate benchmark performance is comparable to or exceeds 40B-class Dense models.Starting Price: $0.00037 per 1M tokens -
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Ring 2.6
Ant Group
Ring is a trillion-parameter thinking model from Ant Group, designed for real-world Agent workflows. It uses the same Mixture of Experts architecture as Ling, activating about 63B parameters per inference, and focuses on coding agents, tool use, multi-tool collaboration, engineering development, research analysis, and long-horizon task execution. Rather than only pursuing “smarter” results, Ring is built to consistently complete complex tasks at reasonable cost, balancing quality, speed, and execution efficiency in production environments. Ring-2.6-1T introduces an adjustable Reasoning Effort mechanism with high and xhigh reasoning intensity levels, using adaptive reasoning budget allocation based on task complexity. High mode is designed for high-frequency Agent workflows, lower token cost, faster multi-step execution, multi-turn interaction, tool collaboration, and task decomposition.Starting Price: $0.0028 per 1M tokens -
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Grok Speech to Text (STT)
SpaceXSI
Grok Speech to Text is a standalone audio API built to help developers integrate fast, accurate transcription into any application. Built on the same stack that powers Grok Voice, Tesla vehicles, and Starlink customer support, the API is designed for use cases such as voice agents, real-time transcription tools, accessibility solutions, podcasts, meeting capture, telephony, and interactive audio experiences. Grok STT can generate transcripts from large audio files through a REST API or transcribe speech in real time through a low-latency WebSocket API. It includes word-level timestamps, speaker diarization, multichannel support, and intelligent Inverse Text Normalization that converts spoken language into properly formatted structured output for numbers, dates, currencies, and more. Grok Speech to Text is evaluated across phone calls, meetings, video and podcast content, and telephony, with strong performance in entity recognition and business use cases. -
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Mercury 2
Inception
Mercury 2 is the first reasoning model fast enough to pick up the phone, a reasoning diffusion language model built for real-time voice agents. Instead of making callers wait through seconds of dead air while an autoregressive model generates thinking tokens one by one, Mercury 2 uses a diffusion large language model architecture to generate tokens in parallel, decoding 1000+ tokens per second on standard NVIDIA GPUs. That speed is fast enough to run a full reasoning pass and start speaking within the latency budget of a natural conversation, reducing the cost of reasoning from seconds of silence to roughly 300 milliseconds. Mercury models work by corrupting clean text into noise, then training a standard Transformer to reverse the process and predict clean text across all positions simultaneously. Because each denoising pass touches many tokens, generation uses the GPU more efficiently than one-token-at-a-time decoding, making custom-silicon-like speed possible on NVIDIA H100s. -
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Ling 3.0 Flash
Ant Group
Ling 3.0 Flash is a next-generation efficient language model designed for long-horizon agent workflows, combining fast response, low activation, and stable tool use. It uses a Mixture-of-Experts architecture with 124 billion total parameters and 5.1 billion activated parameters per token, providing capability while keeping inference efficient. The model supports a native 256K context window that can be extended up to 1 million tokens, with reliable retrieval across information placed at the beginning, middle, or end of long contexts. Compared with the previous Flash model, Ling 3.0 Flash improves stability on extended tasks, tool-calling accuracy, instruction following, compatibility with agent harnesses, and coding performance. Its optimized spatial understanding can construct physical scene grids and reason about relative positions, while hybrid reasoning improves success rates across tasks of varying difficulty. -
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CosyVoice
Alibaba
CosyVoice is Qwen Cloud’s voice cloning and speech synthesis model in the CosyVoice series, designed for professional text-to-speech scenarios with improved sound quality, naturalness, expressiveness, and cloning fidelity. With a short reference recording, it can create a highly similar custom voice without model training; Qwen recommends 10–20 seconds of clear speech, while at least five seconds of continuous speech is required. The model supports real-time, streaming text-to-speech synthesis, allowing applications to accept text and return audio with low first-packet latency. It supports Chinese, English, French, German, Japanese, Korean, and Russian for cloned voices, with language hints available to improve identification during enrollment. Source recordings can use WAV, MP3, or M4A formats and should contain clean speech without background music, noise, or additional speakers.Starting Price: $0.26 per 10,000 characters -
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Celeris-1
Celeris-1
Celeris-1 is a low-latency, general-purpose language model platform and a diffusion model designed to deliver frontier-level intelligence at dramatically higher speed. Instead of generating one token at a time like traditional autoregressive models, Celeris uses a diffusion-based inference architecture that enables parallel generation and response times measured in milliseconds. On its published MMLU-Pro benchmark, Celeris-1 reaches 75.9 accuracy with a 158 ms median response time and 1,664 output tokens per second, placing it within a few points of frontier models while running more than 10x faster. The model is exposed through an OpenAI-compatible API, so developers can point existing SDKs and clients at Celeris with minimal code changes. Streaming is enabled for interactive applications, with responses as low as 24 ms and no buffering or batch delay.Starting Price: $0.20 per 1M tokens -
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Pokee-Isaac
Pokee AI
Pokee-Isaac text-only agentic model with a usable context window of up to 10 million tokens. It is designed to reason, plan, call tools, and execute long-horizon tasks while remaining small enough to deploy inside a VPC, on customer premises, on a workstation, or on-device. Pokee reports that Isaac maintains strong long-context performance across RULER from 256K through 10M tokens and leads the evaluated panel on multi-needle retrieval at 256K, 512K, and 1M. Its agentic architecture is built for deterministic function calling, sustained multi-turn coherence, real-shell execution, and discovering and composing tools across live MCP servers. In Pokee’s controlled benchmarks, Isaac ranked first on BFCL v4 and τ³-bench, second on the Terminal-Bench 2.1 text-only subset, and third on MCP-Atlas. Security testing with DTAP also showed the lowest combined attack success rate in the comparison panel while retaining strong benign-task performance.Starting Price: $0.15 per 1M tokens -
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Higgs Realtime
Boson AI
Higgs Realtime is a production-quality, real-time speech-to-speech model and API built for natural, continuous conversation. It is an end-to-end, instruction-tuned, audio-native model that can understand audio, text, or both and generate high-quality responses, while also functioning as a text LLM when given text alone. Designed for live voice agents, it follows conversations, handles interruptions, adapts when requests change mid-sentence, and carries multi-step workflows through to completion. The model is trained specifically for voice-agent reflexes such as natural turn-taking, conversational cadence, tone adaptation, spoken tool preambles, multi-turn state tracking, and robust instruction following through changing requests. Semantic turn detection helps distinguish a completed turn from a pause, while multilingual and code-switched understanding supports more than 100 languages without per-language setup.Starting Price: $0.0023 per minute -
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Fastino
Fastino
Fastino is an applied AI platform focused on specialized, open-weight language models and the Fastino Fine-Tuning Agent. The agent lets users describe a task in plain language, then automatically selects the architecture, generates training data, trains and evaluates the model, and returns a task-specific model ready to deploy. Fine-tuning projects can be created and revisited from one interface, with models trained on the user’s terms and deployable in their own environment. The resulting models are designed for production-grade performance, with typical response times under 50 ms, while model weights remain private and owned by the user. Models can move from a task description to a trained result in hours, giving teams a faster path to specialized deployment. Fastino also provides open-source and open-weight models for specialized AI workloads. -
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Solar Pro 4
Upstage
Solar Pro 4 is an agentic AI model built to carry real work to the finish: reading documents, running tools, producing deliverables, and stopping when the evidence runs out. It targets longer, more complex workloads that move across multiple documents, execute tasks in a terminal, and chain tool calls over many steps. The model supports a 512K context window with up to 128K output tokens, allowing agents to load contracts, reports, and data files into a single session without splitting them. It handles English, Korean, and Japanese for both input and output, while adjustable reasoning effort lets users choose deeper analysis or faster, real-time interaction. Solar Pro 4 is designed to stay accurate through long documents, multi-turn tool use, and terminal tasks, and to preserve values and conclusions across sequential deliverables such as Excel workbooks, reports, and slide decks.Starting Price: $0.03 per 1M tokens -
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Qwen3.8-Flash-Next
Alibaba
Qwen3.8-Flash-Next is an open-weight multimodal Mixture-of-Experts model and an early preview of the architecture planned for Qwen4. It systematically upgrades attention, residual connections, embeddings, and optimization to improve capability, computational efficiency, model capacity, and training stability. Its hybrid architecture combines Gated DeltaNet, which efficiently compresses historical information, with Qwen Sparse Attention, which selects important context at the micro-block level to reduce attention and indexing costs on long sequences. Gated Residual widens the residual stream into four branches and dynamically controls information flow across layers, while N-gram Embedding adds large-scale local-pattern memory with very little extra per-token computation and can be offloaded to host memory. The model uses a 125B-parameter main network plus 51B N-gram embedding parameters, while activating only 6B parameters per token.Starting Price: $2 per 1M (input) -
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Cohere Parse
Cohere AI
Cohere Parse is a high-throughput vision-language model for processing large volumes of enterprise documents and converting complex, multimodal files into structured, machine-readable data. It goes beyond traditional OCR by understanding tables, forms, diagrams, embedded images, and document structure, returning clean Markdown for downstream processing and applications. Parse is trained for business documents across major industries and domains, including finance, insurance, and scientific work, and supports documents and images across nine major world languages. Spatial awareness preserves important visual relationships by returning bounding boxes for visual elements, helping improve retrieval, grounding, and automation. The model is designed for production-scale workloads, with high throughput and consistent parsing quality as document volumes grow. It can be used for automated document processing, extracting structured information from claims, contracts, and invoices. -
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Muse Voice Transcribe is Meta’s first real-time audio perception model, delivering streaming automatic speech recognition (ASR), diarization, and endpointing in real time. An autoregressive multimodal model from the Muse Spark family, it processes audio in 80 ms chunks and decides dynamically whether to continue listening or emit text. Its adaptive delay changes the amount of audio context used for each word based on difficulty, balancing transcription accuracy with latency. The model is trained on more than 70 languages, with 25 extensively verified at launch, and natively supports arbitrary code-switching both within and between sentences. Language, keyword, and context biasing can further improve recognition accuracy for specific names, places, contacts, or terminology. Streaming diarization identifies speaker changes and distinguishes more than 20 speakers, while endpointing detects when speech begins and when a user finishes speaking.
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Koa
Salesforce
Salesforce Koa is Salesforce’s first CRM reasoning model for Agentforce, built on NVIDIA Nemotron and trained on 27 years of Salesforce CRM intelligence to reason through complex, multi-step enterprise work. It is grounded in nearly three decades of CRM deployments and post-trained on a proprietary synthetic dataset modeled on real business processes, workflows, and operational policies. Its training scenarios simulate the reasoning, tool use, and decision-making Agentforce agents perform across the customer lifecycle, from generating leads and qualifying opportunities to resolving service cases, spanning more than 14 industries. Koa is purpose-built for targeted CRM tasks and is evaluated on Salesforce CRM Bench using real-world workflows such as updating opportunities, routing cases, and scheduling follow-ups. Salesforce reports that it is 11% more precise at calling the right action, recalls customer context with 2.1 times greater reliability. -
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TabPFN-3.5
Prior Labs
TabPFN-3.5 is a tabular foundation model built for state-of-the-art predictions on structured data. It supports a wide range of prediction tasks, including churn, fraud, pricing, demand forecasting, risk, and other real-world data science problems, allowing teams to serve multiple use cases with one model. The model works with data as it is, handling missing values, outliers, categorical features, multi-table datasets, free text as a feature, thousands of distinct IDs without encoding, and hundreds of measurements per row. Users can feed in raw data, skip feature engineering and preprocessing, and get production-grade predictions from the first predict call. TabPFN-3.5 performs predictions in a single forward pass and is designed for both accuracy and speed, with fast inference for latency-critical predictive workflows. It supports production-scale datasets of up to one million rows natively and delivers 20x faster inference than previous model versions. -
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Step 5 Preview
StepFun
Step 5 Preview is StepFun’s flagship model for agentic work, designed for real-world tasks across software engineering and professional knowledge work, with particular strength in finance. It natively supports text, image, and video input and provides a 1M-token context window, enabling tasks that require large amounts of information, tool calls, and continuous progress toward a deliverable. The model can analyze long documents, multiple source materials, and conversation history for cross-document question answering and research organization. For programming and software engineering, it works across multiple languages and can support troubleshooting, code changes, verification, and test creation. Its multi-step agent capabilities let applications provide tools for retrieving information, processing documents, conducting deep research, and producing analytical reports. Multimodal understanding combines images, video, and text for chart analysis, screenshot question answering, etc.Starting Price: $0.04 per input -
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OmniHuman-2.0
BytePlus
OmniHuman 2.0 is an advanced AI-powered image-driven video avatar model that transforms a single image and an audio track into expressive, high-quality character animations. It generates realistic movements that align with speech rhythm, prosody, and semantic content, synchronizing lip movements, emotions, and gestures with audio. Optional text prompts provide precise control over character actions, object generation, camera movements, and scene interactions. Its cinematic emotional expression interprets audio context to produce natural gestures, authentic emotional shifts, and digital actors with a full dramatic range. The model supports rhythmic performances, creating digital singers with expressive motion, natural pauses, and adaptability across musical genres. Multi-person scene capabilities enable dynamic character interactions, while support for animals, cartoons, 3D styles, and non-humanoid subjects expands creative possibilities.Starting Price: $0.07 per second -
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Nemotron 3.5 Transcribe
NVIDIA
NVIDIA Nemotron 3.5 Transcribe is an open-weight speech and audio model for developers building transcription and audio-processing systems. It converts recordings into text and supports verbatim or edited transcription, contextual vocabulary, speaker attribution, word-level timestamps, speech translation, language identification, and audio question answering. Its language matrix covers 40 languages, with task coverage varying by language. The model can process recordings up to one hour and is designed for offline inference using compatible NVIDIA GPU hardware and supported runtimes. Teams can integrate it into speech interfaces, meeting and media processing, customer-service analytics, translation tools, and other audio-understanding applications. Model weights and a fine-tuning toolkit are released under the OpenMDW-1.1 license; hardware and serving requirements depend on deployment. -
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LUIS
Microsoft
Language Understanding (LUIS): A machine learning-based service to build natural language into apps, bots, and IoT devices. Quickly create enterprise-ready, custom models that continuously improve. Add natural language to your apps. Designed to identify valuable information in conversations, LUIS interprets user goals (intents) and distills valuable information from sentences (entities), for a high quality, nuanced language model. LUIS integrates seamlessly with the Azure Bot Service, making it easy to create a sophisticated bot. Powerful developer tools are combined with customizable pre-built apps and entity dictionaries, such as Calendar, Music, and Devices, so you can build and deploy a solution more quickly. Dictionaries are mined from the collective knowledge of the web and supply billions of entries, helping your model to correctly identify valuable information from user conversations. Active learning is used to continuously improve the quality of the models. -
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Hive Moderation
Hive
Hive’s complete solution to protect your platform. Mobilizing the world's largest distributed workforce of humans labeling data, we are raising the bar for automated content moderation. We offer both best-in-class models as well as manual moderation, allowing us to provide solutions at scale and outperform contract workforces of business process outsourcers (BPOs). In addition to our best-in-class models, our distributed workforce can meet a variety of manual moderation needs. Whether you want to manually moderate user content or annotate training data at scale, our distributed system and consensus policy provide a level of precision that our competitors cannot. -
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OpenAI Whisper
OpenAI
Whisper is an automatic speech recognition (ASR) system developed by OpenAI for converting spoken language into text. It is trained on 680,000 hours of multilingual and multitask audio data collected from the web. The model is designed to handle diverse accents, background noise, and technical language with high accuracy. Whisper supports transcription in multiple languages as well as translation into English. It uses an encoder-decoder Transformer architecture to process audio inputs and generate text outputs. The system can also perform tasks like language identification and timestamp generation. Overall, Whisper enables developers to build robust voice-enabled applications with ease. -
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Sparrow
DeepMind
Sparrow is a research model and proof of concept, designed with the goal of training dialogue agents to be more helpful, correct, and harmless. By learning these qualities in a general dialogue setting, Sparrow advances our understanding of how we can train agents to be safer and more useful – and ultimately, to help build safer and more useful artificial general intelligence (AGI). Sparrow is not yet available for public use. Training a conversational AI is an especially challenging problem because it’s difficult to pinpoint what makes a dialogue successful. To address this problem, we turn to a form of reinforcement learning (RL) based on people's feedback, using the study participants’ preference feedback to train a model of how useful an answer is. To get this data, we show our participants multiple model answers to the same question and ask them which answer they like the most. -
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NVIDIA NeMo
NVIDIA
NVIDIA NeMo LLM is a service that provides a fast path to customizing and using large language models trained on several frameworks. Developers can deploy enterprise AI applications using NeMo LLM on private and public clouds. They can also experience Megatron 530B—one of the largest language models—through the cloud API or experiment via the LLM service. Customize your choice of various NVIDIA or community-developed models that work best for your AI applications. Within minutes to hours, get better responses by providing context for specific use cases using prompt learning techniques. Leverage the power of NVIDIA Megatron 530B, one of the largest language models, through the NeMo LLM Service or the cloud API. Take advantage of models for drug discovery, including in the cloud API and NVIDIA BioNeMo framework. -
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ERNIE Bot
Baidu
ERNIE Bot is an AI-powered conversational assistant developed by Baidu, designed to facilitate seamless and natural interactions with users. Built on the ERNIE (Enhanced Representation through Knowledge Integration) model, ERNIE Bot excels at understanding complex queries and generating human-like responses across various domains. Its capabilities include processing text, generating images, and engaging in multimodal communication, making it suitable for a wide range of applications such as customer support, virtual assistants, and enterprise automation. With its advanced contextual understanding, ERNIE Bot offers an intuitive and efficient solution for businesses seeking to enhance their digital interactions and automate workflows.Starting Price: Free -
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PaLM
Google
PaLM API is an easy and safe way to build on top of our best language models. Today, we’re making an efficient model available, in terms of size and capabilities, and we’ll add other sizes soon. The API also comes with an intuitive tool called MakerSuite, which lets you quickly prototype ideas and, over time, will have features for prompt engineering, synthetic data generation and custom-model tuning — all supported by robust safety tools. Select developers can access the PaLM API and MakerSuite in Private Preview today, and stay tuned for our waitlist soon. -
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Med-PaLM 2
Google Cloud
Healthcare breakthroughs change the world and bring hope to humanity through scientific rigor, human insight, and compassion. We believe AI can contribute to this, with thoughtful collaboration between researchers, healthcare organizations, and the broader ecosystem. Today, we're sharing exciting progress on these initiatives, with the announcement of limited access to Google’s medical large language model, or LLM, called Med-PaLM 2. It will be available in the coming weeks to a select group of Google Cloud customers for limited testing, to explore use cases and share feedback as we investigate safe, responsible, and meaningful ways to use this technology. Med-PaLM 2 harnesses the power of Google’s LLMs, aligned to the medical domain to more accurately and safely answer medical questions. As a result, Med-PaLM 2 was the first LLM to perform at an “expert” test-taker level performance on the MedQA dataset of US Medical Licensing Examination (USMLE)-style questions. -
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Gopher
Google DeepMind
Language, and its role in demonstrating and facilitating comprehension - or intelligence - is a fundamental part of being human. It gives people the ability to communicate thoughts and concepts, express ideas, create memories, and build mutual understanding. These are foundational parts of social intelligence. It’s why our teams at DeepMind study aspects of language processing and communication, both in artificial agents and in humans. As part of a broader portfolio of AI research, we believe the development and study of more powerful language models – systems that predict and generate text – have tremendous potential for building advanced AI systems that can be used safely and efficiently to summarise information, provide expert advice and follow instructions via natural language. Developing beneficial language models requires research into their potential impacts, including the risks they pose. -
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PaLM 2
Google
PaLM 2 is our next generation large language model that builds on Google’s legacy of breakthrough research in machine learning and responsible AI. It excels at advanced reasoning tasks, including code and math, classification and question answering, translation and multilingual proficiency, and natural language generation better than our previous state-of-the-art LLMs, including PaLM. It can accomplish these tasks because of the way it was built – bringing together compute-optimal scaling, an improved dataset mixture, and model architecture improvements. PaLM 2 is grounded in Google’s approach to building and deploying AI responsibly. It was evaluated rigorously for its potential harms and biases, capabilities and downstream uses in research and in-product applications. It’s being used in other state-of-the-art models, like Med-PaLM 2 and Sec-PaLM, and is powering generative AI features and tools at Google, like Bard and the PaLM API. -
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Hippocratic AI
Hippocratic AI
Hippocratic AI is the new state of the art (SOTA) model, outperforming GPT-4 on 105 of 114 healthcare exams and certifications. Hippocratic AI has outperformed GPT-4 on 105 out of 114 tests and certifications, outperformed by a margin of five percent or more on 74 of the certifications, and outperformed by a margin of ten percent or more on 43 of the certifications. Most language models pre-train on the common crawl of the Internet, which may include incorrect and misleading information. Unlike these LLMs, Hippocratic AI is investing heavily in legally acquiring evidence-based healthcare content. We’re conducting a unique Reinforcement Learning with Human Feedback process using healthcare professionals to train and validate the model’s readiness for deployment. We call this RLHF-HP. Hippocratic AI will not release the model until a large number of these licensed professionals deem it safe.
