AIモデルレーダー
AIモデルを 同じ基準で比較
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
- 691
- プロバイダー
- 96
- 最終同期
- 2026/10/26
minimaxMiniMax: MiniMax M1
MiniMax-M1 is a large-scale, open-weight reasoning model designed for extended context and high-efficiency inference. It leverages a hybrid Mixture-of-Experts (MoE) architecture paired with a custom "lightning attention" mechanism, allowing it...
- コンテキスト
- 1M
- 入力
- text
- 出力
- text
googleGoogle: Gemini 2.5 Flash
Gemini 2.5 Flash is Google's state-of-the-art workhorse model, specifically designed for advanced reasoning, coding, mathematics, and scientific tasks. It includes built-in "thinking" capabilities, enabling it to provide responses with greater...
- コンテキスト
- 1.0M
- 入力
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- 出力
- text
googleGoogle: Gemini 2.5 Pro
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...
- コンテキスト
- 1.0M
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openaiOpenAI: o3 Pro
The o-series of models are trained with reinforcement learning to think before they answer and perform complex reasoning. The o3-pro model uses more compute to think harder and provide consistently...
- コンテキスト
- 200K
- 入力
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googleGoogle: Gemini 2.5 Pro Preview 06-05
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...
- コンテキスト
- 1.0M
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deepseekDeepSeek: R1 0528
May 28th update to the original DeepSeek R1 Performance on par with OpenAI o1, but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active...
- コンテキスト
- 164K
- 入力
- text
- 出力
- text
mistralaiMistral: Mistral Medium 3
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...
- コンテキスト
- 131K
- 入力
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meta-llamaMeta: Llama Guard 4 12B
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...
- コンテキスト
- 164K
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qwenQwen: Qwen3 30B A3B
Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks. Its unique...
- コンテキスト
- 41K
- 入力
- text
- 出力
- text
qwenQwen: Qwen3 14B
Qwen3-14B is a dense 14.8B parameter causal language model from the Qwen3 series, designed for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for...
- コンテキスト
- 41K
- 入力
- text
- 出力
- text
qwenQwen: Qwen3 32B
Qwen3-32B is a dense 32.8B parameter causal language model from the Qwen3 series, optimized for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for...
- コンテキスト
- 131K
- 入力
- text
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openaiOpenAI: o4 Mini High
OpenAI o4-mini-high is the same model as o4-mini with reasoningeffort set to high. OpenAI o4-mini is a compact reasoning model in the o-series, optimized for fast, cost-efficient performance while retaining...
- コンテキスト
- 200K
- 入力
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openaiOpenAI: o3
o3 is a well-rounded and powerful model across domains. It sets a new standard for math, science, coding, and visual reasoning tasks. It also excels at technical writing and instruction-following....
- コンテキスト
- 200K
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openaiOpenAI: o3 (batch)
o3 is a well-rounded and powerful model across domains. It sets a new standard for math, science, coding, and visual reasoning tasks. It also excels at technical writing and instruction-following....
- コンテキスト
- 200K
- 入力
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openaiOpenAI: o4 Mini
OpenAI o4-mini is a compact reasoning model in the o-series, optimized for fast, cost-efficient performance while retaining strong multimodal and agentic capabilities. It supports tool use and demonstrates competitive reasoning...
- コンテキスト
- 200K
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openaiOpenAI: o4 Mini (batch)
OpenAI o4-mini is a compact reasoning model in the o-series, optimized for fast, cost-efficient performance while retaining strong multimodal and agentic capabilities. It supports tool use and demonstrates competitive reasoning...
- コンテキスト
- 200K
- 入力
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- text
openaiOpenAI: GPT-4.1
GPT-4.1 is a flagship large language model optimized for advanced instruction following, real-world software engineering, and long-context reasoning. It supports a 1 million token context window and outperforms GPT-4o and...
- コンテキスト
- 1.0M
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openaiOpenAI: GPT-4.1 (batch)
GPT-4.1 is a flagship large language model optimized for advanced instruction following, real-world software engineering, and long-context reasoning. It supports a 1 million token context window and outperforms GPT-4o and...
- コンテキスト
- 1.0M
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openaiOpenAI: GPT-4.1 Mini
GPT-4.1 Mini is a mid-sized model delivering performance competitive with GPT-4o at substantially lower latency and cost. It retains a 1 million token context window and scores 45.1% on hard...
- コンテキスト
- 1.0M
- 入力
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openaiOpenAI: GPT-4.1 Mini (batch)
GPT-4.1 Mini is a mid-sized model delivering performance competitive with GPT-4o at substantially lower latency and cost. It retains a 1 million token context window and scores 45.1% on hard...
- コンテキスト
- 1.0M
- 入力
- image · text · file
- 出力
- text
openaiOpenAI: GPT-4.1 Nano
For tasks that demand low latency, GPT‑4.1 nano is the fastest and cheapest model in the GPT-4.1 series. It delivers exceptional performance at a small size with its 1 million...
- コンテキスト
- 1.0M
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openaiOpenAI: GPT-4.1 Nano (batch)
For tasks that demand low latency, GPT‑4.1 nano is the fastest and cheapest model in the GPT-4.1 series. It delivers exceptional performance at a small size with its 1 million...
- コンテキスト
- 1.0M
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meta-llamaMeta: Llama 4 Maverick
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...
- コンテキスト
- 524K
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meta-llamaMeta: Llama 4 Scout
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...
- コンテキスト
- 1.3M
- 入力
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