Claude Sonnet 4.6 Anthropic 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.007500 (rounded ~ $0.01)
Output: $0.007500 (rounded ~ $0.01)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 2,000 output tokens:
- Input Cost: $0.750000
- Output Cost: $0.007500 (rounded ~ $0.01)
- Total Cost: $0.251250 (rounded ~ $0.25)
- Cost per 1K tokens: $0.000251
- Tokens per dollar: 3,988,060 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 450 tokens per second and 200ms time to first token:
- Processing Time: 38 minutes, 58.18 seconds
- Latency: 200 milliseconds to first token
- Base Throughput: 450 tokens/second
- Effective Throughput: 429 tokens/second (temperature-adjusted)
Best Use Cases
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💰 Total Cost Calculation (from Plugin)
Output: $0.018000 (rounded ~ $0.02)
Output: $0.018000 (rounded ~ $0.02)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 2,000 output tokens:
- Input Cost: $2.000000
- Output Cost: $0.018000 (rounded ~ $0.02)
- Total Cost: $0.668000 (rounded ~ $0.67)
- Cost per 1K tokens: $0.000667
- Tokens per dollar: 1,500,000 tokens
- Context Window: 2000000 tokens
Speed & Performance Analysis
With a processing speed of 400 tokens per second and 220ms time to first token:
- Processing Time: 43 minutes, 50.43 seconds
- Latency: 220 milliseconds to first token
- Base Throughput: 400 tokens/second
- Effective Throughput: 381 tokens/second (temperature-adjusted)
Best Use Cases
Want this applied to YOUR actual stack?
This calculator shows the math for Gemini 3.1 Pro. Your decision needs more — current infrastructure, compliance requirements, actual workload patterns, volume tiers — that change which model is right for you.
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Get my instant AI audit — $39 →✨ Market Recommendations AI Model Registry
← Back to Claude Sonnet 4.6| Rank | AI Model & Provider | Total Cost | vs Claude Sonnet 4.6 | vs Gemini 3.1 Pro |
|---|---|---|---|---|
| 🏆 |
Grok 4.20 Beta
xAI
|
$0.165500 (rounded ~ $0.17) Best Value | ↓ 34.1% cheaper | ↓ 75.2% cheaper |
| 🥈 |
Gemini 2.5 Pro
Google
|
$0.421250 (rounded ~ $0.42) | ↑ 67.7% more | ↓ 36.9% cheaper |
| 🥉 |
Gemini 3.1 Pro
Google
|
$0.668000 (rounded ~ $0.67) | ↑ 165.9% more | Same price |
| #4 |
GPT-5.4
OpenAI
|
$0.835000 (rounded ~ $0.84) | ↑ 232.3% more | ↑ 25% more |
| #5 |
GPT-5.4 Thinking
OpenAI
|
$0.835000 (rounded ~ $0.84) | ↑ 232.3% more | ↑ 25% more |
| #6 |
GPT-5.4 Thinking
OpenAI
|
$0.835000 (rounded ~ $0.84) | ↑ 232.3% more | ↑ 25% more |
Grok 4.20 Beta xAI
Gemini 2.5 Pro Google
Gemini 3.1 Pro Google
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
GPT-5.4 Thinking OpenAI
Contextual Retrieval at Scale
Evaluating Claude Sonnet 4.6 and Gemini 3.1 Pro for enterprise-scale RAG pipelines requires a deep look at how each model handles massive context. For organizations managing 1,000,000 token inputs, the choice often comes down to the trade-off between Anthropic’s prompt caching efficiency and Google’s native multimodal breadth. Claude excels at maintaining high instruction-following accuracy even when the prompt is saturated with complex formatting rules or large knowledge bases, making it ideal for generating thousands of localized product descriptions based on deep inventory data.
- Caching Advantage: Claude’s architecture is optimized for multi-turn interactions where the knowledge base remains static, providing a significant operational benefit for recurring agentic queries.
- Multimodal Scope: Gemini 3.1 Pro offers a massive context ceiling that is beneficial for builders who need to ingest multi-hour video files alongside text documents for comprehensive cross-modal analysis.
- Ecosystem Integration: Gemini’s integration with the Vertex AI ecosystem provides a streamlined path for infrastructure teams already committed to Google Cloud.
In back-office automation, where agents frequently loop through reasoning steps, the latency of initial context ingestion is a critical factor. Architects should consider the frequency of knowledge base updates; if the reference material is dynamic and involves diverse file types, Gemini’s robust performance at the 1M+ token level ensures the agent remains grounded. Conversely, for structured, high-volume text generation, Claude’s precision and reliability in long-context scenarios make it a formidable choice for maintaining brand voice and technical accuracy.