Models

Gemini Omni 1.2 Flash is Google's native multimodal video generation and editing model, the successor to Gemini Omni 1.1 Flash. It generates clips up to 15 seconds long with native audio in 16:9 or 9:16, from 360p drafts up to 4K, and supports text-to-video, image-to-video, first and last frame interpolation, subject and video references with improved reference management, and editing or extending an uploaded video. Multi-turn editing and extension of a prior generation is supported through previous_job_id. Note: video generation is only supported through the Video Generation API. See the guide: https://openrouter.ai/docs/guides/overview/multimodal/video-generation

Auto is OpenRouter’s flagship model router, built for general-purpose usage. It adapts to each request, choosing cost-optimal models and adjusting reasoning effort. It balances task difficulty, benchmark results, live prices, and prompt caching. Use it for apps, chat, and general agents. For agent sessions, it follows progress across turns, keeps the same model while work is going well, and adjusts reasoning effort or switches models when the task gets stuck. It respects all your governance settings, including guardrails, ZDR, and budgets.

Auto Code is OpenRouter’s router built specifically for coding agents and harnesses. It uses the same routing engine as the Auto Router, with every request treated as part of an agent session and model choices guided by coding-task pass rate, completion time, and cost per solved task. It follows edits, test results, and recurring errors across turns, keeping the model while work is going well and trying more reasoning effort before switching when trouble persists. Its default coding tier starts from stronger models and weighs failed code changes more heavily. Your model and provider restrictions still apply.

GPT Image 2.6 Sunburst is OpenAI's image generation and editing model optimized for detail: dense compositions, text-heavy layouts, and edits that keep details consistent from one edit to the next at medium or high quality, generating about 50% faster than GPT Image 2.5. It takes text and image inputs and produces images, with quality tiers from low to max, transparent backgrounds, and custom sizes in multiples of 16 pixels at aspect ratios from 1:3 to 3:1. GPT Image 2.6 introduces a complexity setting for how much the model plans before rendering, markup-guided edits, animated GIF output, and SVG output. Choose Flare instead when speed matters more than fine detail.

GPT Image 2.6 Flare is OpenAI's image generation and editing model optimized for speed: fast iteration and interactive text-to-image and image-to-image workflows, generating about 50% faster than GPT Image 2.5. It takes text and image inputs and produces images, with quality tiers from low to max, transparent backgrounds, and custom sizes in multiples of 16 pixels at aspect ratios from 1:3 to 3:1. GPT Image 2.6 introduces a complexity setting for how much the model plans before rendering, markup-guided edits, animated GIF output, and SVG output. Choose Sunburst instead when detail and edit-to-edit consistency matter more than latency.

Gemini Omni 1.1 Flash is Google's native multimodal video generation and editing model, the generally available successor to Gemini Omni Flash Preview. It is suited for text-to-video, image-to-video, first and last frame interpolation, subject and video references (up to three reference clips), and editing or extending an uploaded video, and generates clips from 360p drafts up to 4K with native audio in 16:9 or 9:16. Multi-turn editing and extension of a prior generation is supported through previous_job_id, up to 40 seconds of total video. Note: video generation is only supported through the Video Generation API. See the guide: https://openrouter.ai/docs/guides/overview/multimodal/video-generation

Ripple-65B-Preview by ricursiveAI, a 65B model with native tool calling, served in BF16.

The official release of Hy4 delivers comprehensive and substantial improvements over Hy4 Preview, with particularly strong gains in agentic capabilities and performance across a wide range of long-horizon, real-world productivity tasks. It also introduces visual understanding for the first time.

Morph's decision model. Answers typed choice, noul and score questions about a state. The state is prefix-cached across requests, so repeat questions on the same state bill at the cached-input rate. 65k context window.

Embed 5 Fast is the lower-latency, cost-efficient tier of Cohere's Embed 5 family of multimodal embedding models (about 1B parameters). It embeds text, images, and interleaved text and image content into a single vector space and is suited for high-volume query embedding, agent loops, and multilingual semantic search across 100+ languages. It shares its embedding space with Embed 5 Pro, so documents indexed with Pro can be queried with Fast. Output dimensions are selectable from 256 to 2048, and input_type defaults to search_document when not set.

Embed 5 Pro is the higher-accuracy tier of Cohere's Embed 5 family of multimodal embedding models (about 2.4B parameters). It embeds text, images, and interleaved text and image content into a single vector space and is suited for retrieval and RAG over long or domain-specific documents, visual and document search, and multilingual semantic search across 100+ languages. It shares its embedding space with Embed 5 Fast, so a common pattern is to index a corpus with Pro and embed queries with Fast. Output dimensions are selectable from 256 to 2048, and input_type defaults to search_document when not set.

Rerank 5 Fast is the low-latency tier of Cohere's Rerank 5 family of reranking models. It scores and reorders candidate documents against a query and is suited for high-throughput retrieval such as high-volume search, agent tool loops, and first-pass filtering before a more expensive step. Relevance scores are calibrated to a 0-1 range so a fixed threshold behaves consistently across queries. Rerank 5 Pro is the higher-accuracy sibling for complex queries and long or domain-specific documents.

Rerank 5 Pro is the higher-accuracy tier of Cohere's Rerank 5 family of reranking models. It scores and reorders candidate documents against a query and is suited for retrieval pipelines where ranking quality matters most: complex or reasoning-heavy queries, long or domain-specific documents, and RAG systems that feed a small number of passages to a generator. Relevance scores are calibrated to a 0-1 range so a fixed threshold behaves consistently across queries. Rerank 5 Fast is the lower-latency sibling for high-volume search and agent loops.

Clairic is Cloudflare's System One-style extraction model served on Workers AI. Given a text state and optional images plus a schema of typed fields, it extracts, computes, estimates, and forecasts strongly typed values (text spans, numbers, dates, times, durations, lists, estimates, forecasts) in a single pass, returning a confidence score and null probability for every field instead of generating tokens. Served through the Extractions API (/api/alpha/extractions); not available on /api/alpha/decisions.

Parakeet Ultra is Moondream's post-trained version of NVIDIA's Parakeet TDT 0.6B v3 speech-to-text model, with the same architecture, tokenizer, and 0.6B parameters. It lowers word error rate on the Open ASR Leaderboard English sets (5.80% vs 6.26%) and the 25-language FLEURS set (9.55% vs 11.62%), with larger gains on noisy and long-form audio. Supports 25 European languages and returns transcribed text with segment and word timestamps.

Crouton Alpha is a lightweight model built for scalable coding, agentic, and multimodal workflows. It's ideal for executing agentic sub-tasks, tool use, and document analysis in high-volume, repeated work. Developers can combine it with frontier reasoning models to execute the bounded steps that turn plans and information into action. Crouton Alpha is a stealth model. It is developed and operated by a third-party provider who has chosen to remain anonymous during this preview. OpenRouter routes requests to it and is not its developer, owner, or provider. Prompts and completions may be retained by the provider but are not used for training; all other use is governed by the Stealth Model Terms.

GLM-5.3-FlashX-Legal (GLM-Legal) is Z.ai's legal-domain variant of GLM-5.3-FlashX, a native multimodal model that accepts text, image, and video input and produces text. It adds legal-domain mid-training and post-training with reinforcement learning in task environments modeled on real legal workflows, targeting document understanding, legal reasoning, and multi-step task execution such as legal research, contract review, cross-document consistency checks, and drafting or revising legal documents. It keeps the GLM-5.3-FlashX 1M-token context window, mandatory thinking mode, function calling, structured output, and streaming, and is designed to run in agent setups connected to legal data and document-editing tools (MCP services).

TBC mischief 1 is the world's first neuron-derived video model from The Biological Computing Co. It supports image to video and is a fast and affordable way to bring ideas to life. Ideal for ideation, previsualization, moodboarding and variant testing.

Joey 1 is a 2B decision model from Base Labs, Baseten's research team, built on Qwen3.5-2B. Given a state (text or JSON) and typed questions (yes/no, multiple choice, or ordinal score), it returns a calibrated probability for every option in a single forward pass, with no text generation. It is TypeSafe System One compatible and suits high-volume routing, classification, and guardrail checks where latency and cost matter most. Its larger siblings are Elephant 1 and Mammoth 1.

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 multi-step edits and judges candidates before accepting a result, combining that reasoning with a mix of closed and open image diffusion models for accuracy and steerability. Reasoning effort is controllable via the reasoning parameter (low/medium/high/xhigh); higher levels do more editing passes and apply a stricter internal judge. Riverflow 2.6 adds custom output dimensions: pass size as WIDTHxHEIGHT (up to 16,384 px per edge, 16.8 megapixels total, 16:1 aspect ratio) instead of a preset, or keep using 1K/2K/4K with an aspect ratio (now including 5:4 and 4:5). It accepts up to 10 input images for editing. Pricing is dynamic: cost is finalized per job at completion based on billable processing, so it scales with reasoning effort, output size, and editing complexity rather than a fixed per-image rate. Additional features (via image_config): - Custom font rendering via font_inputs (max 2) to match brand lettering, spacing, and weight - Custom scoring via scoring_prompt and scoring_rubric, so the reasoning model evaluates and steers each candidate against the criteria you care about - Background control via background_mode (original, transparent, solid) and background_hex_color See the image generation docs for details: https://openrouter.ai/docs/features/multimodal/image-generation Note: Sourceful imposes a 4.5MB request size limit, therefore it is highly recommended to pass image URLs instead of Base64 data.