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OpenRouterで現在公開されているすべてのモデルを検索し、モダリティ、コンテキスト、料金、設定、プロバイダー情報を比較できます。
- カタログ範囲
- 公開モデルすべて
- モデル
- 534
- プロバイダー
- 75
- 最終同期
- 2026/08/31
minimaxMiniMax: MiniMax M2.7 (free)
MiniMax-M2.7 is a next-generation large language model designed for autonomous, real-world productivity and continuous improvement. Built to actively participate in its own evolution, M2.7 integrates advanced agentic capabilities through multi-agent...
- コンテキスト
- 197K
- 入力
- text
- 出力
- text
openaiOpenAI: GPT-5.4 Nano
GPT-5.4 nano is the most lightweight and cost-efficient variant of the GPT-5.4 family, optimized for speed-critical and high-volume tasks. It supports text and image inputs and is designed for low-latency...
- コンテキスト
- 400K
- 入力
- file · image · text
- 出力
- text
openaiOpenAI: GPT-5.4 Mini
GPT-5.4 mini brings the core capabilities of GPT-5.4 to a faster, more efficient model optimized for high-throughput workloads. It supports text and image inputs with strong performance across reasoning, coding,...
- コンテキスト
- 400K
- 入力
- file · image · text
- 出力
- text
mistralaiMistral: Mistral Small 4
Mistral Small 4 is the next major release in the Mistral Small family, unifying the capabilities of several flagship Mistral models into a single system. It combines strong reasoning from...
- コンテキスト
- 262K
- 入力
- text · image
- 出力
- text
mistralaiMistral: Mistral Small 4 (batch)
Mistral Small 4 is the next major release in the Mistral Small family, unifying the capabilities of several flagship Mistral models into a single system. It combines strong reasoning from...
- コンテキスト
- 262K
- 入力
- text · image
- 出力
- text
perplexityPerplexity: Embed V1 4B
pplx-embed-v1 -4B is one of Perplexity's state-of-the-art text embedding models built for real-world, web-scale retrieval. pplx-embed-v1 is optimized for standard dense text retrieval with the 4B parameter model maximizing retrieval...
- コンテキスト
- 32K
- 入力
- text
- 出力
- embeddings
perplexityPerplexity: Embed V1 0.6B
pplx-embed-v1-0.6B is one of Perplexity's state-of-the-art text embedding models built for real-world, web-scale retrieval. pplx-embed-v1 is optimized for standard dense text retrieval with the 0.6B parameter model targeting lightweight, low-latency...
- コンテキスト
- 32K
- 入力
- text
- 出力
- embeddings
nvidiaNVIDIA: Nemotron 3 Super
NVIDIA Nemotron 3 Super is a 120B-parameter open hybrid MoE model, activating just 12B parameters for maximum compute efficiency and accuracy in complex multi-agent applications. Built on a hybrid Mamba-Transformer...
- コンテキスト
- 1M
- 入力
- text
- 出力
- text
nvidiaNVIDIA: Nemotron 3 Super (free)
NVIDIA Nemotron 3 Super is a 120B-parameter open hybrid MoE model, activating just 12B parameters for maximum compute efficiency and accuracy in complex multi-agent applications. Built on a hybrid Mamba-Transformer...
- コンテキスト
- 262K
- 入力
- text
- 出力
- text
bytedance-seedByteDance Seed: Seed-2.0-Lite
Seed-2.0-Lite is a versatile, cost‑efficient enterprise workhorse that delivers strong multimodal and agent capabilities while offering noticeably lower latency, making it a practical default choice for most production workloads across...
- コンテキスト
- 262K
- 入力
- text · image · video
- 出力
- text
qwenQwen: Qwen3.5-9B
Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, designed to deliver strong reasoning, coding, and visual understanding in an efficient 9B-parameter architecture. It uses a unified vision-language design...
- コンテキスト
- 262K
- 入力
- text · image · video
- 出力
- text
qwenQwen: Qwen3.5-9B (batch)
Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, designed to deliver strong reasoning, coding, and visual understanding in an efficient 9B-parameter architecture. It uses a unified vision-language design...
- コンテキスト
- 262K
- 入力
- text · image · video
- 出力
- text
openaiOpenAI: GPT-5.4 Pro
GPT-5.4 Pro is OpenAI's most advanced model, building on GPT-5.4's unified architecture with enhanced reasoning capabilities for complex, high-stakes tasks. It features a 1M+ token context window (922K input, 128K...
- コンテキスト
- 1.1M
- 入力
- text · image · file
- 出力
- text
openaiOpenAI: GPT-5.4
GPT-5.4 is OpenAI’s latest frontier model, unifying the Codex and GPT lines into a single system. It features a 1M+ token context window (922K input, 128K output) with support for...
- コンテキスト
- 1.1M
- 入力
- text · image · file
- 出力
- text
inceptionInception: Mercury 2
Mercury 2 is an extremely fast reasoning LLM, and the first reasoning diffusion LLM (dLLM). Instead of generating tokens sequentially, Mercury 2 produces and refines multiple tokens in parallel, achieving...
- コンテキスト
- 128K
- 入力
- text
- 出力
- text
googleGoogle: Gemini 3.1 Flash Lite Preview
Gemini 3.1 Flash Lite Preview is Google's high-efficiency model optimized for high-volume use cases. It outperforms Gemini 2.5 Flash Lite on overall quality and approaches Gemini 2.5 Flash performance across...
- コンテキスト
- 1.0M
- 入力
- text · image · video · file · audio
- 出力
- text
bytedance-seedByteDance Seed: Seed-2.0-Mini
Seed-2.0-mini targets latency-sensitive, high-concurrency, and cost-sensitive scenarios, emphasizing fast response and flexible inference deployment. It delivers performance comparable to ByteDance-Seed-1.6, supports 256k context, four reasoning effort modes (minimal/low/medium/high), multimodal understanding,...
- コンテキスト
- 262K
- 入力
- text · image · video
- 出力
- text
googleGoogle: Nano Banana 2 (Gemini 3.1 Flash Image Preview)
Gemini 3.1 Flash Image Preview, a.k.a. "Nano Banana 2," is Google’s latest state of the art image generation and editing model, delivering Pro-level visual quality at Flash speed. It combines...
- コンテキスト
- 66K
- 入力
- image · text
- 出力
- image · text
qwenQwen: Qwen3.5-35B-A3B
The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model, achieving higher inference efficiency. Its overall...
- コンテキスト
- 262K
- 入力
- text · image · video
- 出力
- text
qwenQwen: Qwen3.5-27B
The Qwen3.5 27B native vision-language Dense model incorporates a linear attention mechanism, delivering fast response times while balancing inference speed and performance. Its overall capabilities are comparable to those of...
- コンテキスト
- 262K
- 入力
- text · image · video
- 出力
- text
qwenQwen: Qwen3.5-122B-A10B
The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. In terms of...
- コンテキスト
- 262K
- 入力
- text · image · video
- 出力
- text
qwenQwen: Qwen3.5-Flash
The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. Compared to the...
- コンテキスト
- 1M
- 入力
- text · image · video
- 出力
- text
googleGoogle: Gemini 3.1 Pro Preview Custom Tools
Gemini 3.1 Pro Preview Custom Tools is a variant of Gemini 3.1 Pro that improves tool selection behavior by preventing overuse of a general bash tool when more efficient third-party...
- コンテキスト
- 1.0M
- 入力
- text · audio · image · video · file
- 出力
- text
nvidiaNVIDIA: Llama Nemotron Embed VL 1B V2 (free)
The Llama Nemotron Embed VL 1B V2 embedding model is optimized for multimodal question-answering retrieval. The model can embed 'documents' in the form of image, text, or image and text...
- コンテキスト
- 131K
- 入力
- text · image
- 出力
- embeddings