Grok 4 Fast Reasoning API | AIMLAPI (original) (raw)
Ideal for applications requiring large-scale text comprehension, strategic analysis, and real-time autonomous decision-making.
Grok 4 Fast Reasoning
Grok 4 Fast Reasoning combines fast inference with advanced multi-step reasoning capabilities, enabling deep understanding and processing of extensive documents.
Grok 4 Fast Reasoning is an advanced variant of xAI’s Grok 4 model, optimized for ultra-fast reasoning and extensive context handling. It supports a massive 2,000,000 token context window, enabling sophisticated long-horizon text understanding and multi-step inference with high efficiency. This version balances speed and reasoning depth, making it ideal for large-scale, real-time applications.
Technical Specification
Performance Benchmarks
- Context Window: 2,000,000 tokens
- Max Output: ~4,096 tokens
- Training Regime: Enhanced for fast inference with optimized compute pathways
- Tool Use: Integrated native support with streamlined multi-step execution
Performance Metrics
- Superior performance in long-context tasks requiring rapid comprehension
- High accuracy in complex text-to-text scenarios with intricate dependencies
Key Capabilities
- Ultra-long context understanding up to 2 million tokens for deep document comprehension
- Accelerated reasoning providing faster turnaround on multi-step tasks
- Deterministic outputs optimized for stable responses over very large input sizes
API Pricing
- Input: 0–128k: 0.26;128k+:0.26; 128k+: 0.26;128k+:0.52 1M tokens
- Output: 0–128k: 0.65;128k+:0.65; 128k+: 0.65;128k+:1.3 per 1M tokens
Optimal Use Cases
- Large-scale document analysis and synthesis where extended context is crucial
- Real-time autonomous agents requiring fast, reliable multi-step reasoning
- Complex strategic planning involving API orchestration and extended logic chains
- Advanced research evaluation for datasets with vast textual dependencies
- Text-to-text transformations including summarization, Q&A, and content generation across extensive inputs
Code Sample
Comparison with Other Models
- vs. GPT-4o: Grok 4 Fast Reasoning supports a vastly larger context window of 2 million tokens compared to GPT-4o, enabling deeper long-form understanding. While GPT-4o excels in multimodal inputs and web browsing, Grok 4 Fast offers faster inference and superior reasoning over extended texts.
- vs. Claude 4 Opus: Claude 4 Opus is known for exceptional language safety and alignment features. Grok 4 Fast outperforms Claude 4 in handling ultra-long context tasks and delivers higher throughput in multi-step reasoning scenarios.
- vs. Gemini 2.5 Pro: Gemini 2.5 Pro provides strong instruction following and speed for typical text tasks. Grok 4 Fast surpasses Gemini in zero-shot reasoning with very long inputs, leveraging its 2 million token context for complex planning and inference.
- vs. Grok 4: Grok 4 Fast Reasoning builds on Grok 4 by dramatically increasing the context window from 256K to 2 million tokens, supporting larger and more complex documents. It also features optimized compute pathways for faster execution while maintaining advanced tool integration and reasoning capabilities.
Limitations
- Text-only model without vision or audio modalities
- Tool use remains sequential, limited compositionality
- Closed-weight approach with no offline or local inference support
- Stream determinism may vary under certain high-throughput conditions
Grok 4 Fast Reasoning is an advanced variant of xAI’s Grok 4 model, optimized for ultra-fast reasoning and extensive context handling. It supports a massive 2,000,000 token context window, enabling sophisticated long-horizon text understanding and multi-step inference with high efficiency. This version balances speed and reasoning depth, making it ideal for large-scale, real-time applications.
Technical Specification
Performance Benchmarks
- Context Window: 2,000,000 tokens
- Max Output: ~4,096 tokens
- Training Regime: Enhanced for fast inference with optimized compute pathways
- Tool Use: Integrated native support with streamlined multi-step execution
Performance Metrics
- Superior performance in long-context tasks requiring rapid comprehension
- High accuracy in complex text-to-text scenarios with intricate dependencies
Key Capabilities
- Ultra-long context understanding up to 2 million tokens for deep document comprehension
- Accelerated reasoning providing faster turnaround on multi-step tasks
- Deterministic outputs optimized for stable responses over very large input sizes
API Pricing
- Input: 0–128k: 0.26;128k+:0.26; 128k+: 0.26;128k+:0.52 1M tokens
- Output: 0–128k: 0.65;128k+:0.65; 128k+: 0.65;128k+:1.3 per 1M tokens
Optimal Use Cases
- Large-scale document analysis and synthesis where extended context is crucial
- Real-time autonomous agents requiring fast, reliable multi-step reasoning
- Complex strategic planning involving API orchestration and extended logic chains
- Advanced research evaluation for datasets with vast textual dependencies
- Text-to-text transformations including summarization, Q&A, and content generation across extensive inputs
Code Sample
Comparison with Other Models
- vs. GPT-4o: Grok 4 Fast Reasoning supports a vastly larger context window of 2 million tokens compared to GPT-4o, enabling deeper long-form understanding. While GPT-4o excels in multimodal inputs and web browsing, Grok 4 Fast offers faster inference and superior reasoning over extended texts.
- vs. Claude 4 Opus: Claude 4 Opus is known for exceptional language safety and alignment features. Grok 4 Fast outperforms Claude 4 in handling ultra-long context tasks and delivers higher throughput in multi-step reasoning scenarios.
- vs. Gemini 2.5 Pro: Gemini 2.5 Pro provides strong instruction following and speed for typical text tasks. Grok 4 Fast surpasses Gemini in zero-shot reasoning with very long inputs, leveraging its 2 million token context for complex planning and inference.
- vs. Grok 4: Grok 4 Fast Reasoning builds on Grok 4 by dramatically increasing the context window from 256K to 2 million tokens, supporting larger and more complex documents. It also features optimized compute pathways for faster execution while maintaining advanced tool integration and reasoning capabilities.
Limitations
- Text-only model without vision or audio modalities
- Tool use remains sequential, limited compositionality
- Closed-weight approach with no offline or local inference support
- Stream determinism may vary under certain high-throughput conditions
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