Llama 3.2 Instruct 90B (Vision) logo
Meta
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Open weights model

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Released September 2024

Llama 3.2 Instruct 90B (Vision) APIプロバイダーのベンチマークと分析

モデル比較

Llama 3.2 Instruct 90B (Vision)のAPIプロバイダーを、遅延（最初のトークンまでの時間）、出力速度（1秒あたりの出力トークン数）、料金などの性能指標で分析します。ベンチマーク対象のAPIプロバイダーにはが含まれます。

最速

出力速度

プロバイダー合計：0社

最短の遅延

最初のトークンまでの時間

プロバイダー合計：0社

最安料金

ブレンド料金（100万トークンあたり）

プロバイダー合計：0社

現在、Llama 3.2 90B (Vision)を利用できるAPIプロバイダーはありません。

このモデルではプロバイダーのベンチマークを利用できません。
モデルの詳細と他のモデルとの知能比較は、Llama 3.2 Instruct 90B (Vision)のモデルページをご覧ください。

ハイライト

速度

Output tokens per second · Higher is better
No data available

エンドツーエンド応答時間

Seconds · Lower is better
No data available

料金

USD per 1M tokens () · Lower is better
No data available

更新：本番環境のユースケースをより適切に反映するため、性能ベンチマークのデフォルトワークロードを入力10kトークンに変更しました。上部で別のワークロードを引き続き選択できます。

料金

Pricing: Cache Hit, Input, and Output

Price (USD per M Tokens) · Lower is better · 10,000 input tokens
No data available

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

Pricing: Blended Price

Blended at 7:2:1 (cache-input-output) · USD per 1M tokens (blended) · Lower is better
No data available

Price per token, shown in USD per million tokens. Price is a blend of cache hit, input, and output token prices using the selected ratio (default 7:2:1 cache-input-output).

Output Speed vs. Price

Blended at 7:2:1 (cache-input-output) · Output speed: output tokens per second · USD per 1M tokens (blended)
Most attractive quadrant
No data available

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

速度

出力速度（1秒あたりのトークン数）で測定

Output Speed: Llama 3.2 Instruct 90B (Vision)

Output speed: output tokens per second · 10,000 input tokens
No data available

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Latency vs. Output Speed

Latency: seconds to first token received · Output speed: output tokens per second · 10,000 input tokens
Most attractive quadrant
No data available

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

遅延

最初のトークンまでの時間（秒）で測定

最初のトークンまでの時間

Seconds to first token received · Lower is better · 10,000 input tokens
No data available

Time to first token received, in seconds, after API request sent. For reasoning models which share reasoning tokens, this will be the first reasoning token. For models which do not support streaming, this represents time to receive the completion.

Figures represent median (P50) measurement over the past 72 hours to reflect sustained changes in performance.

エンドツーエンド応答時間

Seconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed

エンドツーエンド応答時間

Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better · 10,000 input tokens
No data available

Seconds to receive a 500 token response. Key components:

  • Input time: Time to receive the first response token
  • Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details).
  • Answer time: Time to generate 500 output tokens, based on output speed

主要な比較指標とAPI機能

No results.

よくある質問

Llama 3.2 Instruct 90B (Vision)のプロバイダーに関するよくある質問

現在、ベンチマーク対象のAPIプロバイダーでLlama 3.2 Instruct 90B (Vision)を利用することはできません。オープンウェイトモデルのため、セルフホストできます。