Grok Beta

高度な推論能力を備えた先進的な言語モデルで、特にコーディングや数学のタスクで優れた性能を発揮します。前代と比較して、128Kトークンのコンテキストウィンドウと向上した問題解決能力を持っています。

Intelligence(Relatively Weak)

Speed(Relatively Slow)

Input Supported Modalities

Is Reasoning Model

128,000

Context Window

128,000

Maximum Output Tokens

Knowledge Cutoff

Pricing

- /M tokens

Input

- /M tokens

Output

￥54 /M tokens

Blended Price

Quick Simple Comparison

Grok 3 Reasoning Beta

￥3

Grok 3

￥3

Grok 3 mini Reasoning (high)

￥0.3

Basic Parameters

GPT-4.1 Technical Parameters

Parameter Count

Not Announced

Context Length

128.00k tokens

Training Data Cutoff

Open Source Category

Proprietary

Multimodal Support

Text Only

Throughput

Release Date

2024-08-13

Response Speed

66.14,914 tokens/s

Benchmark Scores

Below is the performance of claude-monet in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.

Intelligence Index

Large Language Model Intelligence Level

3754

Large Language Model Intelligence Level

Coding Index

Indicator of AI model performance on coding tasks

2682

Indicator of AI model performance on coding tasks

Math Index

Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks

MMLU Pro

Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video

70.3

Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video

GPQA

Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions

47.1

Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions

HLE

The model's comprehensive average score on the Hugging Face Open LLM Leaderboard

4.7

The model's comprehensive average score on the Hugging Face Open LLM Leaderboard

LiveCodeBench

Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems

24.1

Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems

SciCode

The model's capability in code generation for scientific computing or specific scientific domains

29.5

The model's capability in code generation for scientific computing or specific scientific domains

HumanEval

Score achieved by the AI model on the specific HumanEval benchmark test set

86.6

Score achieved by the AI model on the specific HumanEval benchmark test set

Math 500 Score

Score on the first 500 larger, more well-known mathematical benchmark tests

73.7

Score on the first 500 larger, more well-known mathematical benchmark tests

AIME Score

An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)

10.3

An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)

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