This model processes text input and generates text responses, capable of handling up to 260 pages of reading material and producing outputs up to 8.1 pages long. It is specifically designed to think through problems before providing an answer, making it suitable for complex tasks that require careful consideration. This approach sets it apart from other models by prioritizing internal reasoning. However, it cannot access real-time information from the internet.

This model is designed to handle text, pictures, and video inputs, allowing it to read up to 130 pages of content. It specializes in generating and editing video clips, producing outputs that include both text and moving images. What sets this model apart is its native ability to process and generate video content directly. It is not suitable for tasks requiring external tool usage or the creation of structured data formats.

This model processes text input up to 260 pages and produces text output up to 250 pages. It is best suited for complex instruction following and managing multi-turn conversations for automated workflows. A key feature is its ability to act as a responsive agent and utilize provided tools. However, it does not have the capability to search the web or access external information.

This model accepts text, pictures, and videos as input, allowing it to read up to 130 pages of content. It specializes in creating and editing video content, converting descriptions or pictures into moving images. A key feature is its ability to search the web for information while processing complex visual data. However, it does not utilize external tools or generate structured data formats.

This model accepts text and pictures as input and produces text as output. It can process up to 1000 pages of material at one time and generate responses up to 65 pages long. It is designed for complex, real-world productivity tasks that require planning and the use of specific tools. However, it cannot access the internet to find new information.

This model accepts text input and produces text output. It can process up to 16 pages of material and generate responses up to 14 pages long. Its primary strength lies in its ability to utilize external tools to complete complex tasks. However, it cannot search the internet or perform complex, multi-step reasoning before responding.

This model is designed to convert spoken language into written text. It accepts audio input and produces a complete transcription. It is a specialized version of a known speech processing model, optimized to improve the accuracy of word recognition. Because it is designed specifically for speech-to-text, it is not suitable for tasks requiring complex reasoning or image analysis.

This model accepts text and pictures as input to generate numerical representations of the content. It can process up to 120 pages of material simultaneously. Its design focuses on being a fast and economical option for content representation tasks. The model is limited to processing and outputting these representations and cannot perform tasks requiring complex reasoning or generating new written material.

This model takes in text, reads about 65 pages at once, and gives back short typed answers that can run to 58 pages. Its best use is putting a fixed list of questions to one long case file and getting a pick, a named thing or a score back for each. Questions asked again about the same file reuse the stored reading, so a batch of questions on one file costs less after the first. It does not handle pictures, sound or video, and it does not write essays, so jobs needing those call for a different kind of model.

This model reads text, pictures and video, keeps about 1,000 pages in mind at once, and writes answers of up to 130 pages. It was tuned on legal material, so it suits work a lawyer sits down to do: reading long contracts and case files, spotting risky clauses, and drafting letters or agreements. It thinks before answering, can fill a fixed answer form, and costs less on repeated reads thanks to cheaper re-reading of the same material. It cannot search the web, so a question needing fresh sources or current law needs a model that can.

This model takes in text and pictures, reads about 65 pages at one go, and hands back decisions as the typed fields you ask for. A buyer would use it to pull named figures out of a long file set, then work out totals and rough estimates from them. What sets it apart here is that it reads pictures alongside text in one pass, so a scanned form and its covering notes can be read together. It stops short of long drafting, since it gives back up to about 58 pages, so open-ended writing needs a different kind of model.

You give this video model a picture, and it brings that picture to life as a short moving clip of up to 5 seconds. It is best when you have a still photo or drawing and want to see it in motion, whether for a social post, a quick concept, or an animated scene. Most video models of its kind also build clips from written descriptions alone or match a chosen end frame; this one starts from your image, which keeps the result close to the picture you handed over. It does not finish on a fixed last frame, so jobs that must end at a precise moment need a different kind of video model.

This model accepts text or pictures as input and provides clear decisions as output. It can process up to 16 pages of information at once and generate responses up to 14 pages long. A key feature is its ability to understand visual data alongside written information. This tool is designed solely for making choices and cannot perform tasks requiring creative writing or complex code generation.

This model is designed to listen to audio recordings and convert the spoken words into written text. It excels at the task of accurately transcribing spoken content from various sources. A key feature is its ability to handle speech in 40 different languages. This model is strictly for speech recognition and cannot perform other general language or visual tasks.

This model accepts text and pictures as input and outputs numerical representations of the content. It can process up to 120 pages of material at once and generate up to 110 pages of output. It is designed to be a higher accuracy tier within its family of models. This model is limited to processing and outputting these representations and cannot perform tasks requiring complex reasoning.

Riverflow 2.6 Pro is the flagship model of Sourceful's Riverflow 2.6 lineup, a unified text-to-image and image-to-image family that treats generation as a production workflow: an integrated reasoning model plans...

This model takes in written text and gives back spoken audio, so a buyer who has a script or a set of lines to be read aloud is its user. It is built for voice assistants that answer callers, for narrated recordings, and for speech in several languages, and it draws on a shared catalogue of thousands of ready-made voices to pick from. It cannot make a voice from a sample of someone's recording, so a job that must sound like one particular person falls outside what it does.

Particle-1 reads text and pictures and writes text back, taking in about 1000 pages at once and writing up to 130 pages in one go. It is built for software work: sitting down with a codebase, making changes across many files, and running tasks in the terminal. It can think through a problem before answering and work with your own tools, and it keeps its answers short on the meter, so long coding sessions cost less than with wordier models. It cannot search the web, so jobs that need fresh pages pulled from the internet need a different kind of model.

This model accepts both text and images to understand a current situation. It can process up to 65 pages of information and generate up to 58 pages of structured choices. Its primary purpose is to act as a decision-making system, taking a defined state and specific questions to output a clear choice. Because it is built for structured outcomes, it is not suited for tasks requiring open-ended creative generation.

This model accepts both text and images to understand a current situation. It can process up to 65 pages of information and generate up to 58 pages of structured choices. Its primary purpose is to act as a decision-making system, taking a defined state and specific questions to output a clear choice. Because it is built for structured outcomes, it is not suited for tasks requiring open-ended creative generation.

Liquid d1-ultra is the largest decision model from Liquid AI. You send a situation made of words and pictures with questions of a fixed kind, and for each one you get a yes or no, a score or a pick from a list, with a probability and no written text. It suits moderation, routing, sorting and checks on pictures.

This model takes in plain text of about 8.1 pages at a time and gives back a numeric code for it, not written words. A buyer sits down with it to sort a large pile of articles or reports by subject, or to match a question to the right passage in a long file. Unlike the picture-matching model in its family, it takes in no photos or captions and is built for whole long documents. It stops where a job needs a written reply, since it only produces codes that computers compare, so that kind of work needs a different kind of model.

#5 for reasoning · This model reads text, pictures and files, taking in about a thousand pages at once, and writes back as many as 120 pages. It thinks before answering, can search the web and run the tools you give it, so it suits everyday coding, writing and multi-step work at the computer. It stands out at making sense of screenshots, and it sits in the middle of its family: the bigger relation above it reads the same amount but costs more to run. It gives back only text, so a job that needs a picture or a voice reply calls for a different kind of model.

This model takes a piece of text of up to about eight pages and turns it into a numeric code that computers can compare, so it finds and groups similar writing rather than writing anything itself. Sit down with it to sort thousands of documents by topic, power a search that matches a question to the right passage, or flag near-duplicate text. Unlike its family's picture-matching sibling, which reads only a caption's worth of text, it digests full long documents in one go. It never composes answers or prose, so any job that needs a written reply needs a different kind of model.

This model takes a short piece of text, a picture, or both together, and turns them into a matching code that computers can compare. It reads about a tenth of a page at a time, so it suits short captions, product names, and single photos rather than long documents. Its strength is finding things that are alike: a shopper's words can be matched to product photos, or one picture to similar pictures, because text and images end up in the same shared space. It does not write text or answers, so any job that needs a written reply needs a different kind of model.

This model reads text, pictures and files, taking in about a thousand pages at once, and writes back as many as 120 pages of text. It is the same brain as its smaller relation but run in a deeper thinking mode, so it takes longer and spends more effort on each answer to handle the hardest jobs well. It thinks before answering, searches the web, and runs the tools you give it, which suits complex analysis, careful long-form writing and multi-step work built on large documents. It gives back only text, so a job that needs a picture or a voice reply calls for a different kind of model.

This model reads text, pictures and files, taking in about a thousand pages at once, and writes back as many as 120 pages of text. It is the same brain as its smaller relation but run in a deeper thinking mode, so it takes longer and spends more effort on each answer to handle the hardest jobs well. It thinks before answering, searches the web, and runs the tools you give it, which suits complex analysis, careful long-form writing and multi-step work built on large documents. It gives back only text, so a job that needs a picture or a voice reply calls for a different kind of model.

#5 for reasoning · This model reads text, pictures and files, taking in about a thousand pages at once, and writes back as many as 120 pages. It thinks before answering, can search the web and run the tools you give it, so it suits everyday coding, writing and multi-step work at the computer. It stands out at making sense of screenshots, and it sits in the middle of its family: the bigger relation above it reads the same amount but costs more to run. It gives back only text, so a job that needs a picture or a voice reply calls for a different kind of model.

#1 for interface design · This model accepts text, pictures, and video, allowing it to read up to 1000 pages of information at once. It is designed to handle complex, long-running coding and reasoning tasks. A key feature is its ability to process visual and auditory data alongside written text. However, it is limited to generating text responses and cannot search the internet.

Drex Max reads long stretches of text — about 260 pages at once — and answers typed questions about them: yes or no calls, choices from a set list, or scores on a scale. It is built for sitting down with a document or a situation and getting a firm decision back, not a chat or an essay. It can also write up to 230 pages of output in one go, which few decision models of this kind manage. It does not take in pictures, audio or video, so jobs that need to look at an image or listen to a recording need a different kind of model.

This model takes in written text only and gives back decisions, reading about 16 pages at once and writing up to 14 pages in reply. It suits a buyer who holds a written account of a situation and a set of questions to settle, such as a yes-or-no question, a choice from a list of options, or a place on a rank scale. Where it stops: it takes in no pictures, sound or video, so any job that starts from a photo, a recording or a clip needs a different kind of model.

This model takes in text and gives back decisions: it answers yes-or-no questions, picks from a set of choices, or puts options in order. It reads up to 16 pages at once and can write out up to 14 pages of reasoning, so a buyer can hand it a whole report and ask what to do next. It was built as a small decision-making model, tuned to judge a given state rather than chat freely. It is not made for open-ended writing or conversation, so a job that needs long free-form text should go to a different kind of model.

Mistral's flagship speech-to-text model that turns audio into transcripts with word timestamps and speaker labels, with context biasing for domain-specific vocabulary

Mistral's compact speech-to-text model that turns audio into transcripts with word timestamps and speaker labels, and can be steered with context biasing to catch domain terms

Turns written text into natural-sounding speech through a library of preset voices, taking emotion and pace tags so narration and spoken content carry the right tone and rhythm

This model accepts text, pictures, and video inputs, and can process up to 1000 pages of information to generate responses up to 520 pages long. It excels at complex tasks like coding, cybersecurity, and advanced problem-solving. Its ability to handle video input and its high ranking in reasoning set it apart from other models. However, it cannot search the web or structure data into specific formats.

#10 for 3D scenes · This is the 17 October 2026 snapshot of V4.1 Flash, the image-and-text model DeepSeek serves under its current Flash name. It keeps about a thousand pages in mind, replies up to about 393 pages, thinks first, uses your connected tools and fills in the exact form you give it. Pick it for agents and coding work that include screenshots, and when you want the newest Flash. The earlier DeepSeek V4.1 Flash is the snapshot before this one; V4 Pro 0813 is the larger model for hard reasoning.

Glimpse takes text, images and video and answers in text, reasoning about what it sees rather than just describing it. It keeps about 260 pages in mind at once and can use the tools you connect. Give it jobs where the answer sits in the picture: images and video of the physical world that must be read closely and reasoned over.

Decider V1.2 does not write text. You send content as text, a table of data or a picture, plus named questions of three kinds: yes or no, a choice among as many as 255 options, or a score on an ordered scale. It returns the probability of every answer, so your own system sets its thresholds for sorting, routing or moderation. It is the larger model of the pair and reads about 262 pages; Decider Flash V1.2 is the smaller, lower-cost version.

Decider Flash V1.2 does not write text. You send content as text, a table of data or a picture, plus named questions: yes or no, a choice among as many as 255 options, or a score on an ordered scale. It returns a probability for every answer, so your own system sets its thresholds for sorting, routing or moderating high volumes.