Alternatives to SubQ Snap 1.1
Compare SubQ Snap 1.1 alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to SubQ Snap 1.1 in 2026. Compare features, ratings, user reviews, pricing, and more from SubQ Snap 1.1 competitors and alternatives in order to make an informed decision for your business.
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1
Gemini 4 Argon
Google
Gemini 4 Argon is a frontier artificial intelligence model developed by Google DeepMind for complex reasoning, software engineering, cybersecurity, and professional knowledge work. The model supports multimodal inputs, including text, images, audio, video, and PDF documents, with a unified context window of approximately one million tokens. Its advanced reasoning capabilities enable it to analyze difficult problems, maintain context across lengthy workflows, and execute multistep tasks. Gemini 4 Argon supports autonomous AI agents, code generation, application development, scientific research, and analysis of legal and financial information. Developer capabilities include function calling, code execution, structured outputs, search grounding, and preview support for computer use. The model is available through the Gemini API, Google AI Studio, Google Antigravity, and supported Gemini applications and enterprise services.Starting Price: $2 per 1M tokens (input) -
2
GPT-5.6 Sol
OpenAI
GPT-5.6 Sol is a next-generation OpenAI model designed for advanced reasoning, coding, agentic workflows, biology analysis, cybersecurity support, and complex knowledge work. It is part of the GPT-5.6 model family alongside Terra and Luna, with Sol positioned as the flagship model for the most demanding tasks. The model introduces a new max reasoning effort for deeper thinking and an ultra mode that uses subagents to accelerate complex work beyond a single-agent approach. GPT-5.6 Sol shows strong performance in command-line coding workflows, long-horizon security tasks, genomics analysis, vulnerability research, debugging, patch development, and defensive testing. OpenAI pairs the model’s stronger capabilities with layered safeguards, real-time misuse classifiers, account-level review, automated red-teaming, and enterprise controls for sensitive workflows. GPT-5.6 Sol helps developers, enterprises, researchers, and security teams complete sophisticated technical work.Starting Price: $4 per 1M tokens (input) -
3
GPT-6 Astra
OpenAI
GPT-6 Astra is OpenAI’s frontier AI model for computer use, software engineering, scientific research, cybersecurity, browsing, and complex professional work. It combines advanced reasoning with agentic capabilities that allow it to navigate software, use tools, conduct research, manipulate data, troubleshoot systems, and complete multistep workflows. Astra is also designed to produce polished documents, spreadsheets, presentations, websites, applications, and other business or technical artifacts while following existing templates and organizational standards. In Codex, the model introduces improved long-running context management that can preserve notes and retrieve information from earlier context windows during extended software engineering tasks. OpenAI positions Astra as its most aligned model to date, with improvements in respecting task boundaries, interpreting user intent, communicating limitations, and avoiding unauthorized actions.Starting Price: $10 per 1M tokens (input) -
4
Claude Opus 5
Anthropic
Claude Opus 5 is Anthropic’s advanced everyday AI model built for coding, knowledge work, problem-solving, visual outputs, and production AI workflows. The model delivers stronger performance than Opus 4.8 at the same base price and is positioned as a cost-effective alternative close to Claude Fable 5 frontier intelligence. Claude Opus 5 supports configurable effort settings so users can optimize for intelligence, speed, or token efficiency. It performs especially well on software engineering, automation, computer use, scientific research, and knowledge work evaluations. The model is available on Claude Max, Claude Pro, Claude API, Claude Code, and other Claude platforms, with Fast mode available at a higher price. Built for developers, researchers, enterprises, and everyday Claude users, Claude Opus 5 helps teams complete complex tasks with stronger verification, careful iteration, and practical cost efficiency.Starting Price: $5 per 1M tokens (input) -
5
Claude Sonnet 5
Anthropic
Claude Sonnet 5 is Anthropic's latest AI model, designed to deliver stronger agentic capabilities for coding, reasoning, tool use, and knowledge work while maintaining the efficiency of the Sonnet family. The model can independently plan tasks, use external tools such as browsers and terminals, and complete complex workflows that previously required larger AI models. Sonnet 5 significantly improves upon Claude Sonnet 4.6 with better reasoning, coding performance, reduced hallucinations, stronger safety behavior, and more effective autonomous task execution. It is available across Claude plans and through the Claude API with OpenAI-style developer access for application integration. Anthropic also introduced lower introductory API pricing, making Sonnet 5 a cost-effective option for developers building AI-powered products. By combining advanced agentic capabilities with improved safety and competitive pricing, Claude Sonnet 5 helps developers build more capable AI applications.Starting Price: $2 per 1M tokens (input) -
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GPT-5.6 Luna
OpenAI
GPT-5.6 Luna is the fast and affordable model in OpenAI’s GPT-5.6 series, built to bring strong capability to users and developers who need practical intelligence with lower overhead. In the new GPT-5.6 naming system, the number identifies the model generation, while Sol, Terra, and Luna identify durable capability tiers that can advance on their own cadence, giving people and developers clearer choices across intelligence, speed, and cost. Luna sits alongside Sol, the flagship model, and Terra, the balanced model for everyday work, as part of a family designed for broader access to next-generation AI. During the limited preview, GPT-5.6 models are initially available through the API and Codex to a select group of trusted partners and organizations, with plans for broader availability in ChatGPT, Codex, and the API. OpenAI developed GPT-5.6 Sol, Terra, and Luna with its most robust safeguards to date, with configurations matched to each model’s capabilities.Starting Price: $0.20 per 1M tokens (input) -
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GPT-5.6 Terra
OpenAI
GPT-5.6 Terra is a balanced model in the GPT-5.6 series designed for everyday work, coding, agentic workflows, cybersecurity support, biology analysis, and enterprise automation. It sits between GPT-5.6 Sol, the flagship model, and GPT-5.6 Luna, the faster and lower-cost option. Terra is positioned to deliver competitive performance to GPT-5.5 while being significantly cheaper to run. The model supports improved reasoning, coding, tool coordination, long-horizon workflows, and legitimate defensive security work. It is part of a model family built with layered safeguards, including trained refusals, real-time misuse classifiers, account-level review, differentiated access, monitoring, and continued red-team testing. GPT-5.6 Terra helps developers, enterprises, and technical teams access strong AI capabilities with a more practical balance of intelligence, speed, and cost.Starting Price: $2 per 1M tokens (input) -
8
Kimi K3
Moonshot AI
Kimi K3 is Moonshot AI’s most capable model, built for frontier intelligence scenarios such as software engineering, knowledge work, deep reasoning, and multimodal understanding. The model has 2.8 trillion parameters and uses Kimi Delta Attention, a hybrid linear attention mechanism, along with Attention Residuals for long-context performance. Kimi K3 supports a 1 million token context window, making it useful for analyzing large codebases, long documents, complex knowledge bases, and multi-step workflows. It includes native visual understanding for images and videos, with support for structured message formats, base64 image input, uploaded video files, and multimodal reasoning. Developers can use Kimi K3 through an OpenAI-compatible API with support for streaming, structured JSON output, partial mode, custom tools, dynamic tool loading, and automatic context caching.Starting Price: $3 per 1M tokens (input) -
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MiniMax M3
MiniMax
MiniMax M3 is an open-weight multimodal AI model designed for coding, agentic workflows, long-context reasoning, and complex automation tasks. The model combines frontier-level coding performance, native multimodal understanding, and a context window of up to 1 million tokens. MiniMax M3 uses MiniMax Sparse Attention to improve long-context efficiency while reducing compute requirements for large-scale inputs. It supports text, image, and video understanding, making it useful for workflows that combine code, documents, visual references, and tool-driven tasks. The model is built for repository-scale reasoning, software engineering, autonomous task execution, tool calling, and multi-step agent workflows. MiniMax M3 helps developers, AI teams, and enterprises build capable agents that can reason across large contexts and work with multimodal information.Starting Price: $0.30 per million input tokens -
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GLM-5.3-Flash
Z.ai
GLM-5.3-Flash is Z.ai’s natively multimodal model in the GLM-5 series (previously previewed as Ox Alpha), designed to deliver strong coding, agentic, visual, and knowledge-work performance at relatively low inference cost. It uses 320 billion total parameters with 18 billion active parameters, along with a hybrid architecture that combines sparse and linear attention to reduce the cost of long-context processing. The model supports context lengths of up to one million tokens and was trained on a 30-trillion-token multimodal corpus. GLM-5.3-Flash can reason across text, images, documents, interfaces, dashboards, and other visual information while using that feedback to refine its own outputs. Z.ai reports substantial gains over GLM-5.2 on coding and agentic benchmarks, including DeepSWE and AutomationBench, while approaching higher-cost frontier models on several evaluations.Starting Price: $0.15 per 1M tokens (input) -
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Muse Spark 1.1
Meta
Muse Spark 1.1 is a multimodal reasoning model from Meta Superintelligence Labs built for agentic tasks, coding, computer use, tool use, and multimodal understanding. The model improves on the original Muse Spark with stronger performance in planning, orchestration, long-context work, coding workflows, and external app interactions. Muse Spark 1.1 can manage a 1 million token context window, remember earlier actions, retrieve important information, compact context, and delegate tasks across parallel subagents. It is designed to operate across tools, MCP servers, custom skills, browsers, native apps, scripts, images, video, PDFs, and audio-based workflows. Developers can access Muse Spark 1.1 through the new Meta Model API public preview, while users can try it in Thinking mode in the Meta AI app and on meta.ai.Starting Price: $1.25 per 1M tokens (input) -
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Inkling
Thinking Machines Lab
Inkling is an open-weights multimodal AI model from Thinking Machines designed as a customizable foundation model for developers, researchers, and enterprises. The model is a Mixture-of-Experts transformer with 975 billion total parameters, 41 billion active parameters, and support for context windows up to 1 million tokens. Inkling was trained from scratch on text, images, audio, and video, giving it native capabilities across reasoning, coding, agentic tool use, vision, audio, factuality, and instruction following. It is built with controllable thinking effort so users can balance performance, latency, and token efficiency for different workloads. The model is available for fine-tuning on Tinker, with playground access, API availability through ecosystem partners, and full weights published on Hugging Face. Built for customization, Inkling gives teams an open-weights base model for building domain-specific AI systems, multimodal agents, coding workflows, research tools, and more.Starting Price: Free -
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MiniMax H3
MiniMax
MiniMax H3 is a general-purpose omni-modal generation model that jointly understands multimodal contexts spanning text, images, video, and audio. It generates videos with native stereo sound at up to 2K resolution and 15 seconds in length, delivering content for advertising, branding, ecommerce, product design, UI/UX, gaming, and creative workflows. Users can combine reference types in one instruction, for example, transferring camera movement from a video, placing a character from an image into the scene, and matching vocals from an audio clip, while describing the relationships in natural language. H3 supports text-to-image, text-to-video with jointly generated audio, multi-shot modeling, text-to-audio, and generalized reference and editing across images, videos, and audio. Voice, sound effects, and music are modeled together. The model excels at instruction following, accurate text and brand presentation, and video-to-video motion transfer. -
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Claude Haiku 4.5
Anthropic
Anthropic has launched Claude Haiku 4.5, its latest small-language model designed to deliver near-frontier performance at significantly lower cost. The model provides similar coding and reasoning quality as the company’s mid-tier Sonnet 4, yet it runs at roughly one-third of the cost and more than twice the speed. In benchmarks cited by Anthropic, Haiku 4.5 meets or exceeds Sonnet 4’s performance in key tasks such as code generation and multi-step “computer use” workflows. It is optimized for real-time, low-latency scenarios such as chat assistants, customer service agents, and pair-programming support. Haiku 4.5 is made available via the Claude API under the identifier “claude-haiku-4-5” and supports large-scale deployments where cost, responsiveness, and near-frontier intelligence matter. Claude Haiku 4.5 is available now on Claude Code and our apps. Its efficiency means you can accomplish more within your usage limits while maintaining premium model performance.Starting Price: $1 per million input tokens -
15
Clef
Cloudflare
Clef is a 27B multimodal decision model that turns a state and a schema of typed questions into structured decisions. It can read state information as text, JSON, images, or video and return a probability for every allowed option of every question. The model supports a 65,536-token context window and vision inputs, allowing developers to evaluate both structured application data and visual content in the same decision workflow. Clef supports three typed question formats: noul for estimating the probability that a proposition is true, choice for selecting among predefined options and returning per-option probabilities, and score for evaluating an input against ordered criteria and producing a probability-weighted score. Applications can submit between 1 and 64 questions in a single request, with answers returned under the same question IDs. -
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Clef-flash
Cloudflare
Clef-flash is a fast 9B multimodal decision model that turns a state and a schema of typed questions into structured decisions. It can read state information as text, JSON, images, or video and returns a probability for every allowed option of every question. The model supports a 24,576-token context window and vision input, allowing developers to evaluate structured application data and visual content within the same workflow. Clef Flash supports three typed question formats: noul for estimating the probability that a proposition is true, choice for selecting among predefined options with per-option probabilities, and score for evaluating an input against ordered criteria and returning a probability-weighted score. Applications can submit from 1 to 64 questions in a single request, with answers returned under the same question IDs. Example uses include deciding whether a support request is urgent, routing it to the correct team, and scoring the severity of customer impact. -
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DeepSeek-V4-Flash
DeepSeek
DeepSeek-V4-Flash is a high-efficiency Mixture-of-Experts (MoE) language model designed for fast, scalable reasoning and text generation. It features 284 billion total parameters with 13 billion activated parameters, delivering strong performance while optimizing computational cost. The model supports an extensive context window of up to one million tokens, enabling it to process large documents and complex workflows with ease. Its hybrid attention architecture enhances long-context efficiency by reducing memory and compute requirements. Trained on over 32 trillion tokens, DeepSeek-V4-Flash demonstrates solid capabilities across knowledge, reasoning, and coding tasks. It is designed for scenarios where speed and efficiency are critical, offering a balance between performance and resource usage. The model also supports multiple reasoning modes, allowing users to adjust between faster outputs and deeper analysis.Starting Price: $0.14 per 1M tokens (input) -
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Liquid d1
Liquid AI
d1 is Liquid AI’s first decision model, built to answer structured questions about a situation with probabilities rather than generating text. It takes unstructured data such as text, images, or both, along with one or more questions, processes them in a single forward pass, and returns probabilities without generating output tokens. d1 supports three question types: noul for yes-or-no decisions, choice for selecting among labels with a probability for each option, and score for placing an input on an ordered scale using probability-weighted levels. Multiple questions can be evaluated against the same state in one request, making it useful for real-time decision workflows. Liquid AI demonstrates d1 across applications such as filtering support tickets, browsing code repositories, organizing documents into smart folders, operating websites, compacting agent context, inspecting industrial parts, and playing games from screenshots.Starting Price: Free -
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Gemini 3.8 Flash
Google
Gemini 3.8 Flash is Google’s most intelligent Flash workhorse model, delivering significant improvements over 3.7 Flash across software engineering, agentic tasks, and critical multi-step reasoning in specialized domains. Built for long-horizon coding and autonomous agents, it can solve complex engineering problems end to end and delivers the dependability required for critical enterprise autonomy across specialized knowledge domains. The model shows stronger performance in quantitative and professional fields that require advanced analysis and reporting, as well as multi-step reasoning across STEM, humanities, and professional subjects. Its gains stem from a core design choice: Gemini 3.8 Flash works harder on complex tasks, executing additional reasoning steps and calling tools iteratively to maximize performance. At higher effort levels, it may use more tokens to pursue stronger results, while developers can select lower effort levels. -
20
Jev
TypeSafe AI
Jev is a System One Model from TypeSafe AI designed for fast, structured decision-making inside software and automated workflows. Unlike conventional large language models that generate free-form text token by token, Jev accepts unstructured state and returns predefined type-safe values with calibrated probabilities and confidence scores. The model uses a parallel sampling architecture and a training approach called Reinforcement Learning for Calibrated Decisions, which is optimized for producing reliable probabilistic decisions rather than conversational responses. Jev is intended for tasks such as classification, routing, scoring, extraction, branching logic, verification, guardrails, and other AI-powered workflows where structured outputs can plug directly into application code. TypeSafe reports end-to-end response times ranging from about 70 to 500 milliseconds and says Jev can be substantially faster and more efficient than frontier language models on System One-style workloads.Starting Price: Input: $0.042 / 1M tokens -
21
Laya AI
Laya AI
Laya AI is an open source family of non-autoregressive System 1 decision models built for fast, typed decisions instead of free-form text generation. Given a state and structured questions, Laya evaluates them in a single forward pass and returns outputs that application code can use directly. It supports three decision types: choice, which selects among named options and returns probabilities; score, which places an input on an ordered rubric such as urgency or severity; and noul, which estimates the probability that a proposition is true. Laya is designed for workloads such as agent and model routing, support ticket triage, guardrails, intent classification, risk and fraud scoring, workflow escalation, RAG filtering, moderation, and phishing detection. It can route requests by department, score urgency, flag refund or churn signals, detect policy or abuse conditions, and escalate uncertain cases to a larger model or human reviewer.Starting Price: Free -
22
Perplexity Decider
Perplexity
Perplexity Decider is a 27B decision model fine-tuned from Qwen3.8-27B for structured classification and multimodal decision tasks. Rather than generating unrestricted text, it takes an input state and a typed decision schema, then returns the selected result together with calibrated probabilities. The model supports choice decisions for selecting among predefined categories and noul decisions for estimating the probability of a yes-or-no condition. It can also accept images alongside text, allowing applications to make decisions from screenshots or other visual inputs. Example workflows include routing a support request to billing, technical support, or sales, and estimating whether a message expresses urgency. Perplexity reports an overall accuracy of 85.71% across 11 benchmarks, including FinancialPhraseBank, RAGTruth, JudgeBench, TabFact, ContractNLI, Circa, Belebele, and TruthfulQA binary.Starting Price: Free -
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Perplexity Decisions API
Perplexity
Perplexity’s Decisions API is built for fast, structured decisions that software can use directly. Instead of generating free-form text, it reads natural-language text, JSON, or images as shared state and returns typed answers with probabilities. Developers can attach one or more named questions to the same input and choose among three question types. noul handles yes-or-no judgments and returns the probability of yes from 0 to 1. choice selects from developer-defined options and returns the most likely option, the probability of every option, and a confidence score. score evaluates content against an ordered rubric and returns a probability-weighted score, the full probability distribution, and confidence. The API is designed for tasks such as classification, routing, grading against a rubric, severity assessment, sentiment analysis, or any decision that can be compared against a threshold. -
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Microsoft-Decision-1
Microsoft
Microsoft-Decision-1 is a fast decision-scoring model built for structured outputs that software can immediately act on. It is designed for routing, classification, prioritization, verification, and workflow control across applications, agents, and automated systems. The model is post-trained from Qwen3.5-9B for single-pass decision scoring and, when given a fixed set of answer options, returns a calibrated probability for each one. It supports yes/no, multiple-choice, rating, and rubric-based grading of AI responses and agent actions through a structured API. Microsoft evaluated the model across 36 benchmarks covering routing, ranking, long context, multilingual and out-of-distribution tasks, reasoning, and safety, and reports that it achieved the highest accuracy in that comparison while also delivering very low latency. The model is also designed for robustness, with testing focused on keeping decisions stable when prompts are paraphrased.Starting Price: $0.042 per 1M tokens -
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OpenAI Decisions API
OpenAI
The Decisions API turns text and images into typed decisions an application can use, evaluating inputs about 10x faster than the Responses API. Developers provide a model, shared input evidence, and one or more questions describing what should be evaluated, and the API returns structured answers that can be used to classify content, route requests, prioritize work, or make threshold-based decisions. It supports three question types: predicate checks whether a condition is true and returns a probability from 0 to 1; choice selects one value from a fixed set and returns option probabilities and confidence; and score evaluates an input against ordered rubric levels and returns a probability-weighted score. Inputs can be plain text or user messages containing both text and images, allowing applications to evaluate cases such as visible product damage, request intent, document type, issue severity, or answer relevance. -
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Strands Decider
Strands Labs
Strands Decider is a small, fast decision model, or System One model, built for agentic AI workflows. Unlike an LLM that generates arbitrary text, it chooses between predefined options, answers yes-or-no questions, or rates inputs on an ordered scale. Each decision includes calibrated confidence, making it possible to send uncertain cases to a larger model or a human while handling routine decisions quickly. The first model in the family, strands-decider-2B, has 1.9 billion parameters and is based on a Qwen3.5-2B-Base torso with its language-modeling head replaced by a lightweight pointer head. It evaluates answers in one forward pass without text generation or a decoding loop. Supported decision types include choice, noul for probability-of-true questions, and score for ordered scales. The model is designed for tasks such as model routing, tool selection, tool-argument checking, request triage, grounding and safety guardrails, policy classification, and low-cost evaluation.Starting Price: Free -
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Span-01
Respan
Span-01 is a hyper-parallel reasoning classifier built to detect complex behaviors across long production traces in a single forward pass. Rather than generating tokens sequentially, it reasons over natural-language behavior definitions and returns direct probabilities for present, absent, and not_observable. The model is trained for general classification reasoning with RLAIF before being specialized for behavior detection, allowing it to apply previously unseen definitions to new behaviors and domains instead of relying on a fixed taxonomy. At inference time, hyper-parallel definition branches evaluate multiple behaviors against the same trace context simultaneously, while hybrid attention helps preserve reasoning over long, complex inputs. Span-01 is designed for production monitoring across safety, reliability, grounding, agent behavior, and response quality, including jailbreak and prompt injection detection, privacy, and secrets.Starting Price: $199 per month -
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SubQ Snap
Subquadratic
SubQ Snap is a long-context decision model for enterprise and agentic workflows, built to make structured decisions quickly over large amounts of information. It handles tasks such as choosing which files are relevant, selecting the next tool, checking whether a result passes, deciding whether a workflow should continue, and determining when a frontier model is needed. Instead of generating open-ended text, Snap takes context, a question, and a finite set of allowed answers, then returns a probability distribution over those answers. It supports up to 384K tokens of mixed text and image context, allowing it to reason over codebases, contracts, interaction histories, logs, screenshots, charts, diagrams, and scanned pages in a single call. Powered by Subquadratic Sparse Attention, Snap reduces the cost of reading long inputs while a decision head maps contextual representations directly to admissible answers without autoregressive generation.Starting Price: $0.05 per million input tokens -
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Tev1
Together AI
Tev1 is a fast decision and classification model built by Together AI and fine-tuned from Qwen3.5 4B. It is designed around a simple interface: provide a piece of state, a question, and 2 to 24 labeled options, and the model returns exactly one letter that software can map back to a semantic key. This structure supports production tasks such as routing support tickets, evaluating automated returns, applying policy rules, rating sentiment, categorizing documents, and inserting deterministic decision points into larger agent pipelines. The model has 4.7B parameters, a 32.8K-token context window, and runs with non-thinking generation for fast, predictable classification. It was trained on 37,840 examples spanning intent detection, boolean comprehension, sentiment, policy application, routing, news categories, and research-paper taxonomy. Recommended settings use temperature 0 and a very small output budget so responses remain stable and easy to parse.Starting Price: $0.04 per 1M tokens -
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Solar Decide
Upstage
Solar Decide is Upstage’s structured decision model, serving as a System One endpoint on Solar Mini 4. Instead of generating prose, it takes a state together with one or more typed questions and returns a choice, score, or yes/no answer with calibrated probabilities taken directly from the model. Each question is handled in a single forward pass, making the model suited to fast classification, routing, policy checks, prioritization, and other structured decision tasks. The choice type selects among predefined options and returns the selected option, individual probabilities, and confidence. The score type evaluates an input against ordered criteria and returns a probability-weighted score, legend, probability distribution, and confidence. The noul type returns the probability that a yes-or-no proposition is true. Multiple questions can share the same state in one request, and the state can be provided as text or any JSON value serialized into the prompt.Starting Price: $0.1 per 1M tokens -
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Shieldstral
Mistral AI
Shieldstral is a 3B open-weights, policy-adaptive multimodal safety classifier designed to evaluate text, images, and text-plus-image content using policies defined at inference time. Instead of relying on a fixed taxonomy of harm categories, it frames moderation as a binary question-answering task: users provide an instruction describing the evaluation context and strictness, a yes-or-no safety question, and the content to judge. The model reads the “yes” and “no” logits and converts them into a continuous, calibrated safety score, allowing applications to threshold or rank results by confidence rather than depend on a single discrete label. This formulation unifies prompt classification, response moderation, refusal detection, toxicity detection, and multimodal safety in one interface, while letting teams adapt policies without retraining the model. Shieldstral can evaluate prompts, responses, prompt-response pairs, images, and images with accompanying text. -
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Seed1.8
ByteDance
Seed1.8 is ByteDance’s latest generalized agentic AI model designed to bridge understanding and real-world action by combining multimodal perception, agent-like task execution, and wide-ranging reasoning capabilities into a single foundation model that goes beyond simple language generation. It supports multimodal inputs, including text, images, and video, processes very large context windows (hundreds of thousands of tokens at once), and is optimized to handle complex workflows in real environments, such as information retrieval, code generation, GUI interaction, and multi-step decision logic, with efficient, accurate responses suitable for real-world applications. Seed1.8 unifies skills such as search, code understanding, visual context interpretation, and autonomous reasoning so developers and AI systems can build interactive agents and next-generation workflows capable of synthesizing evidence, following instructions deeply, and acting on tasks like automation. -
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Qwen3.8-Omni-Flash
Alibaba
Qwen3.8-Omni-Flash is a next-generation native omnimodal model designed to strengthen agent capabilities in real-world productivity scenarios, advancing from understanding multimodal content to planning tasks, calling tools, and completing creative work. Built on the Qwen3.8-Flash-Next architecture, it accepts text, image, audio, and video inputs with a context window of up to 1 million tokens while maintaining strong text performance. Beyond coding, knowledge work, and GUI interaction, it extends agentic workflows centered on audio and video, including video editing, music video creation, film production and commentary, audiovisual summarization, and real-time conversations. The model improves long-form audio and audiovisual understanding through controllable descriptions, agentic evidence gathering, meeting understanding, and video-centered deep research. Users can specify the subject, time range, level of detail, and output format for video analysis, enabling overviews, etc. -
34
GLM-4.6V
Z.ai
GLM-4.6V is a state-of-the-art open source multimodal vision-language model from the Z.ai (GLM-V) family designed for reasoning, perception, and action. It ships in two variants: a full-scale version (106B parameters) for cloud or high-performance clusters, and a lightweight “Flash” variant (9B) optimized for local deployment or low-latency use. GLM-4.6V supports a native context window of up to 128K tokens during training, enabling it to process very long documents or multimodal inputs. Crucially, it integrates native Function Calling, meaning the model can take images, screenshots, documents, or other visual media as input directly (without manual text conversion), reason about them, and trigger tool calls, bridging “visual perception” with “executable action.” This enables a wide spectrum of capabilities; interleaved image-and-text content generation (for example, combining document understanding with text summarization or generation of image-annotated responses).Starting Price: Free -
35
Qwen3-VL
Alibaba
Qwen3-VL is the newest vision-language model in the Qwen family (by Alibaba Cloud), designed to fuse powerful text understanding/generation with advanced visual and video comprehension into one unified multimodal model. It accepts inputs in mixed modalities, text, images, and video, and handles long, interleaved contexts natively (up to 256 K tokens, with extensibility beyond). Qwen3-VL delivers major advances in spatial reasoning, visual perception, and multimodal reasoning; the model architecture incorporates several innovations such as Interleaved-MRoPE (for robust spatio-temporal positional encoding), DeepStack (to leverage multi-level features from its Vision Transformer backbone for refined image-text alignment), and text–timestamp alignment (for precise reasoning over video content and temporal events). These upgrades enable Qwen3-VL to interpret complex scenes, follow dynamic video sequences, read and reason about visual layouts.Starting Price: Free -
36
Mistral OCR 4
Mistral AI
Mistral OCR 4 is a document extraction and understanding model built for enterprise search, RAG, domain-specific retrieval pipelines, and production-grade document intelligence. It extracts and structures content from a wide range of documents, moving beyond clean text and tables to return a structured representation of each page. Alongside extracted text, OCR 4 provides bounding boxes, typed-block classification, and inline confidence scores, helping downstream systems understand not only what the document says, but where each element sits, what role it plays, and how confident the model is in each region. Bounding boxes make in-context highlighting and reliable data pipelines possible, while block types and confidence scores support source-grounded citations, redactions, and human-in-the-loop verification. OCR 4 accepts common enterprise formats, including PDF, DOC, PPT, and OpenDocument, and supports 170 languages across 10 language groups.Starting Price: $2 per 1000 pages -
37
TASKIN
SoftLab
TASKIN is developing a shared workspace for people and AI agents to keep work requests connected to their sources, owners, context, acceptance criteria, reviews and outcomes. Its product direction includes carrying relevant context between conversations, preparing bounded work for agents, making results reviewable by people, and keeping open questions visible. Example workflows show a human owner, an agent preparing a draft and a reviewer checking evidence before a decision. The public site currently presents fictional illustrations rather than a live workspace: it does not connect accounts, run agents or accept payments. Connected mail, planning, native experiences and broader context continuity are described as in development or future concepts, with availability and release timing not confirmed. It is aimed at founders, operators and teams coordinating work across people and AI tools. -
38
GPT-4o mini
OpenAI
A small model with superior textual intelligence and multimodal reasoning. GPT-4o mini enables a broad range of tasks with its low cost and latency, such as applications that chain or parallelize multiple model calls (e.g., calling multiple APIs), pass a large volume of context to the model (e.g., full code base or conversation history), or interact with customers through fast, real-time text responses (e.g., customer support chatbots). Today, GPT-4o mini supports text and vision in the API, with support for text, image, video and audio inputs and outputs coming in the future. The model has a context window of 128K tokens, supports up to 16K output tokens per request, and has knowledge up to October 2023. Thanks to the improved tokenizer shared with GPT-4o, handling non-English text is now even more cost effective. -
39
Kimi K2.5
Moonshot AI
Kimi K2.5 is a next-generation multimodal AI model designed for advanced reasoning, coding, and visual understanding tasks. It features a native multimodal architecture that supports both text and visual inputs, enabling image and video comprehension alongside natural language processing. Kimi K2.5 delivers open-source state-of-the-art performance in agent workflows, software development, and general intelligence tasks. The model offers ultra-long context support with a 256K token window, making it suitable for large documents and complex conversations. It includes long-thinking capabilities that allow multi-step reasoning and tool invocation for solving challenging problems. Kimi K2.5 is fully compatible with the OpenAI API format, allowing developers to switch seamlessly with minimal changes. With strong performance, flexibility, and developer-focused tooling, Kimi K2.5 is built for production-grade AI applications.Starting Price: Free -
40
GPT-4 Turbo
OpenAI
GPT-4 is a large multimodal model (accepting text or image inputs and outputting text) that can solve difficult problems with greater accuracy than any of our previous models, thanks to its broader general knowledge and advanced reasoning capabilities. GPT-4 is available in the OpenAI API to paying customers. Like gpt-3.5-turbo, GPT-4 is optimized for chat but works well for traditional completions tasks using the Chat Completions API. GPT-4 is the latest GPT-4 model with improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more. Returns a maximum of 4,096 output tokens. This preview model is not yet suited for production traffic.Starting Price: $0.0200 per 1000 tokens -
41
HunyuanOCR
Tencent
Tencent Hunyuan is a large-scale, multimodal AI model family developed by Tencent that spans text, image, video, and 3D modalities, designed for general-purpose AI tasks like content generation, visual reasoning, and business automation. Its model lineup includes variants optimized for natural language understanding, multimodal vision-language comprehension (e.g., image & video understanding), text-to-image creation, video generation, and 3D content generation. Hunyuan models leverage a mixture-of-experts architecture and other innovations (like hybrid “mamba-transformer” designs) to deliver strong performance on reasoning, long-context understanding, cross-modal tasks, and efficient inference. For example, the vision-language model Hunyuan-Vision-1.5 supports “thinking-on-image”, enabling deep multimodal understanding and reasoning on images, video frames, diagrams, or spatial data. -
42
Pioneer
Pioneer.ai
Pioneer is an inference API built for developers who would rather ship than babysit a GPU cluster. It lets teams point an existing OpenAI, Anthropic, or other client at Pioneer, keep the same API and code, and run inference like normal while Pioneer finds where the current model falls short. It clusters production traffic by use case, surfaces where accuracy, latency, or cost can improve, then builds and routes to small specialist models automatically. Its continuous improvement loop, Adaptive Inference, mines live production failures for high-signal examples, retrains a specialist model, evaluates the new checkpoint, and promotes improvements behind the same endpoint without requiring redeployment. Pioneer supports encoder models for structured extraction tasks such as named entity recognition, text classification, structured JSON extraction, privacy filtering, and safety classification, as well as decoder models for text generation, classification, open-ended prompting, etc. -
43
EmbeddingGemma 2
Google
EmbeddingGemma 2 is an open, lightweight multimodal embedding model designed to map text, code, images, video, and audio into a shared embedding space for search, retrieval, classification, routing, and RAG applications. Built on the Gemma 4 architecture and released under the Apache 2.0 license, it has 740 million parameters and is optimized for on-device inference. Its modular design can use as little as 270M parameters for text-only workloads, with optional vision and audio encoders for full multimodal support. Matryoshka Representation Learning lets developers reduce output vectors from 768 dimensions to 512, 256, or 128, lowering storage and memory requirements for local vector databases. The model supports an 8K-token context window and can process up to 5.5 minutes of audio, 29 images, 58 video frames, or interleaved combinations on local hardware. -
44
Cohere Embed
Cohere
Cohere's Embed is a leading multimodal embedding platform designed to transform text, images, or a combination of both into high-quality vector representations. These embeddings are optimized for semantic search, retrieval-augmented generation, classification, clustering, and agentic AI applications. The latest model, embed-v4.0, supports mixed-modality inputs, allowing users to combine text and images into a single embedding. It offers Matryoshka embeddings with configurable dimensions of 256, 512, 1024, or 1536, enabling flexibility in balancing performance and resource usage. With a context length of up to 128,000 tokens, embed-v4.0 is well-suited for processing large documents and complex data structures. It also supports compressed embedding types, including float, int8, uint8, binary, and ubinary, facilitating efficient storage and faster retrieval in vector databases. Multilingual support spans over 100 languages, making it a versatile tool for global applications.Starting Price: $0.47 per image -
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Sup AI
Sup AI
Sup AI is a multi-LLM platform that merges outputs from several top large language models, such as GPT, Claude, Llama, and more, to generate richer, more accurate, and better-validated answers than any single model could provide. It applies real-time “logprob confidence scoring,” analyzing each token’s probability to detect uncertainty or hallucination; when a model’s confidence falls below a threshold, the response is halted, helping ensure that delivered answers remain high-quality and trustworthy. Sup’s “multi-model fusion” then compares, contrasts, and consolidates outputs from different models, cross-verifying and synthesizing the best parts into a final result. Sup also supports “multimodal RAG” (retrieval-augmented generation) to incorporate external data (text, PDFs, images) into context-aware responses, giving the AI access to factual sources and helping it “never forget” relevant information.Starting Price: $20 per month -
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Qwen3.5-Plus
Alibaba
Qwen3.5-Plus is a high-performance native vision-language model designed for efficient text generation, deep reasoning, and multimodal understanding. Built on a hybrid architecture that combines linear attention with a sparse mixture-of-experts design, it delivers strong performance while optimizing inference efficiency. The model supports text, image, and video inputs and produces text outputs, making it suitable for complex multimodal workflows. With a massive 1 million token context window and up to 64K output tokens, Qwen3.5-Plus enables long-form reasoning and large-scale document analysis. It includes advanced capabilities such as structured outputs, function calling, web search, and tool integration via the Responses API. The model supports prefix continuation, caching, batch processing, and fine-tuning for flexible deployment. Designed for developers and enterprises, Qwen3.5-Plus provides scalable, high-throughput AI performance with OpenAI-compatible API access.Starting Price: $0.4 per 1M tokens -
47
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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GLM-5V-Turbo
Z.ai
GLM-5V-Turbo is a multimodal coding foundation model designed for vision-based coding tasks, capable of natively processing inputs such as images, video, text, and files while producing text outputs. It is optimized for agent workflows, enabling a full loop of understanding environments, planning actions, and executing tasks, and integrates seamlessly with agent frameworks like Claude Code and OpenClaw. It supports long-context interactions with a context length of 200K tokens and up to 128K output tokens, making it suitable for complex, long-horizon tasks. It offers multiple thinking modes for different scenarios, strong vision comprehension across images and video, real-time streaming output for improved interaction, and advanced function-calling capabilities for integrating external tools. It also includes context caching to enhance performance in extended conversations. In practical use, it can reconstruct frontend projects from design mockups. -
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GPT-5 nano
OpenAI
GPT-5 nano is OpenAI’s fastest and most affordable version of the GPT-5 family, designed for high-speed text processing tasks like summarization and classification. It supports text and image inputs, generating high-quality text outputs with a large 400,000-token context window and up to 128,000 output tokens. GPT-5 nano offers very fast response times, making it ideal for applications requiring quick turnaround without sacrificing quality. Pricing is extremely competitive, with input tokens costing $0.05 per million and output tokens $0.40 per million, making it accessible for budget-conscious projects. The model supports advanced API features such as streaming, function calling, structured outputs, and fine-tuning. While it supports image input, it does not handle audio input or web search, focusing on core text tasks efficiently.Starting Price: $0.05 per 1M tokens -
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Llama 4 Scout
Meta
Llama 4 Scout is a powerful 17 billion active parameter multimodal AI model that excels in both text and image processing. With an industry-leading context length of 10 million tokens, it outperforms its predecessors, including Llama 3, in tasks such as multi-document summarization and parsing large codebases. Llama 4 Scout is designed to handle complex reasoning tasks while maintaining high efficiency, making it perfect for use cases requiring long-context comprehension and image grounding. It offers cutting-edge performance in image-related tasks and is particularly well-suited for applications requiring both text and visual understanding.Starting Price: Free
