Grok 4 Deep Reason xAI 2000000
💰 Total Cost Calculation
Output: $25.000000
Output: $25.000000
Unit: $0.000000
Fees: $0.000000
Detailed Cost Analysis
For 1,000,000 input tokens and 1,000,000 output tokens:
- Input Cost: $5.000000
- Output Cost: $25.000000
- Unit Cost: $0.000000
- Service Fees: $0.000000
- Total Cost: $30.000000
- Cost per 1K tokens: $0.015000 (rounded ~ 0.02)
- Tokens per dollar: 66,667 tokens
- Context Window: 2000000 tokens
Speed & Performance Analysis
With a processing speed of 100 tokens per second and 350ms time to first token:
- Processing Time: 5 hours, 56 minutes, 40.00 seconds
- Latency: 350 milliseconds to first token
- Base Throughput: 100 tokens/second
- Effective Throughput: 93 tokens/second
Best Use Cases
o3 Pro OpenAI
💰 Total Cost Calculation
Output: $80.000000
Output: $80.000000
Unit: $0.000000
Fees: $0.050000
Advanced Cost Breakdown
Detailed Cost Analysis
For 1,000,000 input tokens and 1,000,000 output tokens:
- Input Cost: $20.000000
- Output Cost: $80.000000
- Unit Cost: $0.000000
- Service Fees: $0.050000
- Total Cost: $100.050000
- Cost per 1K tokens: $0.050025
- Tokens per dollar: 19,990 tokens
- Context Window: 200000 tokens
Speed & Performance Analysis
With a processing speed of 350 tokens per second and 300ms time to first token:
- Processing Time: 1 hour, 41 minutes, 54.00 seconds
- Latency: 300 milliseconds to first token
- Base Throughput: 350 tokens/second
- Effective Throughput: 327 tokens/second
Best Use Cases
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Thinking Models for Frontier Science
Comparing the ‘Deep Reason’ tiers of xAI and OpenAI. Grok 4 Deep Reason focuses on real-time data ingestion combined with long-form deliberation, while o3 Pro uses multi-step recursive reasoning for math and STEM tasks. Both are high-cost models, with o3 Pro leading the price at $100.00 total per 1M/1M, and Grok 4 offering a slightly more accessible $60.00 entry point.
Real-Time vs Scientific Depth
Grok is superior for identifying hidden logical patterns in live market data and social trends. o3 Pro is the choice for pharmaceutical research and complex physics simulations where every token must be logically perfect. For developers building autonomous agents that need to solve difficult coding bugs, both models offer internal ‘scratchpad’ reasoning that significantly reduces final error rates.
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