Claude Haiku 4.5 is Anthropic’s fastest and most efficient model, delivering near-frontier intelligence at a fraction of the cost and latency of larger Claude models. Matching Claude Sonnet 4’s performance...
Models
Every model in the catalog with source-linked pricing, context limits, provider availability, and published benchmark results.
Qwen3-VL-8B-Thinking is the reasoning-optimized variant of the Qwen3-VL-8B multimodal model, designed for advanced visual and textual reasoning across complex scenes, documents, and temporal sequences. It integrates enhanced multimodal alignment and...
Qwen3-VL-8B-Instruct is a multimodal vision-language model from the Qwen3-VL series, built for high-fidelity understanding and reasoning across text, images, and video. It features improved multimodal fusion with Interleaved-MRoPE for long-horizon...
Qwen3-VL-30B-A3B-Thinking is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Thinking variant enhances reasoning in STEM, math, and complex tasks. It excels...
Qwen3-VL-30B-A3B-Instruct is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Instruct variant optimizes instruction-following for general multimodal tasks. It excels in perception...
Claude Sonnet 4.5 is Anthropic’s most advanced Sonnet model to date, optimized for real-world agents and coding workflows. It delivers state-of-the-art performance on coding benchmarks such as SWE-bench Verified, with...
Qwen3-VL-235B-A22B Thinking is a multimodal model that unifies strong text generation with visual understanding across images and video. The Thinking model is optimized for multimodal reasoning in STEM and math....
Qwen3-VL-235B-A22B Instruct is an open-weight multimodal model that unifies strong text generation with visual understanding across images and video. The Instruct model targets general vision-language use (VQA, document parsing, chart/table...
Mistral Medium 3.1 is an updated version of Mistral Medium 3, which is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost. It balances...
GLM-4.5V is a vision-language foundation model for multimodal agent applications. Built on a Mixture-of-Experts (MoE) architecture with 106B parameters and 12B activated parameters, it achieves state-of-the-art results in video understanding,...
GPT-5 Chat is designed for advanced, natural, multimodal, and context-aware conversations for enterprise applications.
Claude Opus 4.1 is an updated version of Anthropic’s flagship model, offering improved performance in coding, reasoning, and agentic tasks. It achieves 74.5% on SWE-bench Verified and shows notable gains...
UI-TARS-1.5 is a multimodal vision-language agent optimized for GUI-based environments, including desktop interfaces, web browsers, mobile systems, and games. Built by ByteDance, it builds upon the UI-TARS framework with reinforcement...
Mistral-Small-3.2-24B-Instruct-2506 is an updated 24B parameter model from Mistral optimized for instruction following, repetition reduction, and improved function calling. Compared to the 3.1 release, version 3.2 significantly improves accuracy on...
Claude Opus 4 is benchmarked as the world’s best coding model, at time of release, bringing sustained performance on complex, long-running tasks and agent workflows. It sets new benchmarks in...
Claude Sonnet 4 significantly enhances the capabilities of its predecessor, Sonnet 3.7, excelling in both coding and reasoning tasks with improved precision and controllability. Achieving state-of-the-art performance on SWE-bench (72.7%),...
Mistral Medium 3 is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost. It balances state-of-the-art reasoning and multimodal performance with 8× lower cost...
Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy...
Llama Guard 4 is a Llama 4 Scout-derived multimodal pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM...
OpenAI o4-mini-high is the same model as [o4-mini](/openai/o4-mini) with reasoning_effort set to high. OpenAI o4-mini is a compact reasoning model in the o-series, optimized for fast, cost-efficient performance while retaining...
Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward...
Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...
Mistral Small 3.1 24B Instruct is an upgraded variant of Mistral Small 3 (2501), featuring 24 billion parameters with advanced multimodal capabilities. It provides state-of-the-art performance in text-based reasoning and...
Qwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts within images.
MiniMax-01 is a combines MiniMax-Text-01 for text generation and MiniMax-VL-01 for image understanding. It has 456 billion parameters, with 45.9 billion parameters activated per inference, and can handle a context...
Llama 3.2 11B Vision is a multimodal model with 11 billion parameters, designed to handle tasks combining visual and textual data. It excels in tasks such as image captioning and...
GPT-4o mini is OpenAI's newest model after [GPT-4 Omni](/models/openai/gpt-4o), supporting both text and image inputs with text outputs. As their most advanced small model, it is many multiples more affordable...
Claude 3 Haiku is Anthropic's fastest and most compact model for near-instant responsiveness. Quick and accurate targeted performance. See the launch announcement and benchmark results [here](https://www.anthropic.com/news/claude-3-haiku) #multimodal
The Auto Router automatically selects the best model for your prompt, powered by the wisdom of the market. It routes you based on what the OpenRouter community collectively spends on...
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| Model | Creator | Inputs | Context | Input | Output | Released | Compare |
|---|---|---|---|---|---|---|---|
| Anthropic: Claude Haiku 4.5anthropic/claude-haiku-4.5 | 200K | $1 | $5 | — | |||
| Qwen: Qwen3 VL 8B Thinkingqwen/qwen3-vl-8b-thinking | 131.072K | $0.18 | $2.1 | — | |||
| Qwen: Qwen3 VL 8B Instructqwen/qwen3-vl-8b-instruct | 262.144K | $0.25 | $0.75 | — | |||
| Qwen: Qwen3 VL 30B A3B Thinkingqwen/qwen3-vl-30b-a3b-thinking | 262.144K | $0.29 | $1 | — | |||
| Qwen: Qwen3 VL 30B A3B Instructqwen/qwen3-vl-30b-a3b-instruct | 262.144K | $0.15 | $0.6 | — | |||
| Anthropic: Claude Sonnet 4.5anthropic/claude-sonnet-4.5 | 1M | $3 | $15 | — | |||
| Qwen: Qwen3 VL 235B A22B Thinkingqwen/qwen3-vl-235b-a22b-thinking | 131.072K | $0.4 | $4 | — | |||
| Qwen: Qwen3 VL 235B A22B Instructqwen/qwen3-vl-235b-a22b-instruct | 131.072K | $0.21 | $1.9 | — | |||
| Mistral: Mistral Medium 3.1mistralai/mistral-medium-3.1 | 131.072K | $0.4 | $2 | — | |||
| Z.ai: GLM 4.5Vz-ai/glm-4.5v | 65.536K | $0.6 | $1.8 | — | |||
| OpenAI: GPT-5 Chatopenai/gpt-5-chat | 128K | $1.25 | $10 | — | |||
| Anthropic: Claude Opus 4.1anthropic/claude-opus-4.1 | 200K | $15 | $75 | — | |||
| ByteDance: UI-TARS 7B bytedance/ui-tars-1.5-7b | 128K | $0.1 | $0.2 | — | |||
| Mistral: Mistral Small 3.2 24Bmistralai/mistral-small-3.2-24b-instruct | 128K | $0.075 | $0.2 | — | |||
| Anthropic: Claude Opus 4anthropic/claude-opus-4 | 200K | $15 | $75 | — | |||
| Anthropic: Claude Sonnet 4anthropic/claude-sonnet-4 | 200K | $3 | $15 | — | |||
| Mistral: Mistral Medium 3mistralai/mistral-medium-3 | 131.072K | $0.4 | $2 | — | |||
| Google: Gemini 2.5 Pro Preview 05-06google/gemini-2.5-pro-preview-05-06 | 1.04858M | $1.25 | $10 | — | |||
| Meta: Llama Guard 4 12Bmeta-llama/llama-guard-4-12b | 163.84K | $0.18 | $0.18 | — | |||
| OpenAI: o4 Mini Highopenai/o4-mini-high | 200K | $1.1 | $4.4 | — | |||
| Meta: Llama 4 Maverickmeta-llama/llama-4-maverick | 128K | $0.188 | $0.652 | — | |||
| Meta: Llama 4 Scoutmeta-llama/llama-4-scout | 327.68K | $0.1 | $0.3 | — | |||
| Mistral: Mistral Small 3.1 24Bmistralai/mistral-small-3.1-24b-instruct | 128K | $0.351 | $0.555 | — | |||
| Qwen: Qwen2.5 VL 72B Instructqwen/qwen2.5-vl-72b-instruct | 128K | $0.8 | $1 | — | |||
| MiniMax: MiniMax-01minimax/minimax-01 | 1.00019M | $0.2 | $1.1 | — | |||
| Meta: Llama 3.2 11B Vision Instructmeta-llama/llama-3.2-11b-vision-instruct | 131.072K | $0.345 | $0.345 | — | |||
| OpenAI: GPT-4o-mini (2024-07-18)openai/gpt-4o-mini-2024-07-18 | 128K | $0.15 | $0.6 | — | |||
| Anthropic: Claude 3 Haikuanthropic/claude-3-haiku | 200K | $0.25 | $1.25 | — | |||
| Auto Routeropenrouter/auto | 2M | — | — | — | |||
| meta-llama/Llama-4-Maverick-17B-128E-Instructmeta-llama/Llama-4-Maverick-17B-128E-Instruct | Not documented | — | — | — | |||
| meta-llama/Llama-4-Scout-17B-16E-Instructmeta-llama/Llama-4-Scout-17B-16E-Instruct | Not documented | — | — | — | |||
| meta-llama/Llama-Guard-4-12Bmeta-llama/Llama-Guard-4-12B | Not documented | — | — | — | |||
| meta-llama/Llama-4-Maverick-17B-128Emeta-llama/Llama-4-Maverick-17B-128E | Not documented | — | — | — | |||
| meta-llama/Llama-4-Scout-17B-16Emeta-llama/Llama-4-Scout-17B-16E | Not documented | — | — | — | |||
| meta-llama/Llama-3.2-90B-Vision-Instructmeta-llama/Llama-3.2-90B-Vision-Instruct | Not documented | — | — | — | |||
| meta-llama/Llama-3.2-11B-Vision-Instructmeta-llama/Llama-3.2-11B-Vision-Instruct | Not documented | — | — | — | |||
| meta-llama/Llama-Guard-3-11B-Visionmeta-llama/Llama-Guard-3-11B-Vision | Not documented | — | — | — | |||
| meta-llama/Llama-3.2-90B-Visionmeta-llama/Llama-3.2-90B-Vision | Not documented | — | — | — | |||
| meta-llama/Llama-3.2-11B-Visionmeta-llama/Llama-3.2-11B-Vision | Not documented | — | — | — | |||
| google/gemma-4-26B-A4Bgoogle/gemma-4-26B-A4B | Not documented | — | — | — | |||
| google/gemma-4-26B-A4B-itgoogle/gemma-4-26B-A4B-it | Not documented | — | — | — | |||
| google/gemma-4-31Bgoogle/gemma-4-31B | Not documented | — | — | — | |||
| google/gemma-4-31B-itgoogle/gemma-4-31B-it | Not documented | — | — | — | |||
| google/diffusiongemma-26B-A4B-itgoogle/diffusiongemma-26B-A4B-it | Not documented | — | — | — | |||
| google/medgemma-1.5-4b-itgoogle/medgemma-1.5-4b-it | Not documented | — | — | — | |||
| google/translategemma-4b-itgoogle/translategemma-4b-it | Not documented | — | — | — | |||
| google/translategemma-12b-itgoogle/translategemma-12b-it | Not documented | — | — | — | |||
| google/translategemma-27b-itgoogle/translategemma-27b-it | Not documented | — | — | — | |||
| google/t5gemma-2-4b-4bgoogle/t5gemma-2-4b-4b | Not documented | — | — | — | |||
| google/t5gemma-2-1b-1bgoogle/t5gemma-2-1b-1b | Not documented | — | — | — |