Claude Sonnet 4.6 Anthropic 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.001875
Output: $0.001875
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Multimodal Input Details
Resolution: Medium
Tokens: 516,000
Cost: $0.000000
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 500 output tokens:
- Input Cost: $1.137000 (rounded ~ $1.14)
- Output Cost: $0.001875
- Total Cost: $0.934215 (rounded ~ $0.93)
- Cost per 1K tokens: $0.000616
- Tokens per dollar: 1,623,288 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: 57 minutes, 17.58 seconds
- Latency: 200 milliseconds to first token
- Base Throughput: 450 tokens/second
- Effective Throughput: 441 tokens/second (temperature-adjusted)
Best Use Cases
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Get my instant AI audit — $39 →Gemini 3.1 Flash Google 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.001500
Output: $0.001500
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Multimodal Input Details
Resolution: Medium
Tokens: 516,000
Cost: $0.000000
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 500 output tokens:
- Input Cost: $0.758000 (rounded ~ $0.76)
- Output Cost: $0.001500
- Total Cost: $0.623060 (rounded ~ $0.62)
- Cost per 1K tokens: $0.000411
- Tokens per dollar: 2,433,955 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 800 tokens per second and 100ms time to first token:
- Processing Time: 32 minutes, 13.72 seconds
- Latency: 100 milliseconds to first token
- Base Throughput: 800 tokens/second
- Effective Throughput: 784 tokens/second (temperature-adjusted)
Best Use Cases
Want this applied to YOUR actual stack?
This calculator shows the math for Gemini 3.1 Flash. 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 Flash |
|---|---|---|---|---|
| 🏆 |
Gemini 3.5 Flash-Lite
Google
|
$0.093547 (rounded ~ $0.09) Best Value | ↓ 90% cheaper | ↓ 85% cheaper |
| 🥈 |
Gemini 3.8 Flash
Google
|
$0.233554 (rounded ~ $0.23) | ↓ 75% cheaper | ↓ 62.5% cheaper |
| 🥉 |
Gemini 3.6 Flash
Google
|
$0.467108 (rounded ~ $0.47) | ↓ 50% cheaper | ↓ 25% cheaper |
| #4 |
Gemini 2.5 Pro
Google
|
$1.557650 (rounded ~ $1.56) | ↑ 66.7% more | ↑ 150% more |
| #5 |
GPT-5.4
OpenAI
|
$3.113425 (rounded ~ $3.11) | ↑ 233.3% more | ↑ 399.7% more |
| #6 |
GPT-5.4 Thinking
OpenAI
|
$3.113425 (rounded ~ $3.11) | ↑ 233.3% more | ↑ 399.7% more |
| #7 |
GPT-6 Astra
OpenAI
|
$12.456200 (rounded ~ $12.46) | ↑ 1233.3% more | ↑ 1899.2% more |
| #8 |
GPT-6 Astra
OpenAI
|
$12.456200 (rounded ~ $12.46) | ↑ 1233.3% more | ↑ 1899.2% more |
Gemini 3.5 Flash-Lite Google
Gemini 3.8 Flash Google
Gemini 3.6 Flash Google
Gemini 2.5 Pro Google
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
GPT-6 Astra OpenAI
GPT-6 Astra OpenAI
For research teams and SaaS companies handling high-volume document extraction, selecting the right model requires balancing reasoning depth with throughput efficiency. When processing 1,000 invoices monthly, the choice between Claude Sonnet 4.6 and Gemini 3.1 Flash often hinges on the specific nature of your data and your architectural requirements.
Claude Sonnet 4.6 is frequently the preferred choice for complex, high-stakes invoice extraction where instruction following and structured output reliability are paramount. Its architectural strengths lie in nuanced reasoning and superior handling of multi-step extraction logic, which is critical when dealing with diverse invoice formats—some simple, others laden with idiosyncratic data fields. If your extraction pipeline requires the model to perform validation checks, handle OCR errors, or reconcile data across multiple pages, Sonnet 4.6 offers a robust, developer-friendly experience that minimizes the need for iterative prompting.
Conversely, Gemini 3.1 Flash is engineered for scale and speed. In production environments where latency is a bottleneck or where invoices are relatively standardized, Flash delivers exceptional performance at a high throughput. It excels at rapid, multimodal ingestion, making it a strong contender for pipelines that require constant, low-latency processing. While it may require slightly more robust post-processing for complex edge cases compared to Sonnet, its efficiency makes it highly cost-effective for large-scale, automated workflows. For teams building autonomous agents or RAG systems that ingest massive amounts of raw document data, Flash provides the necessary performance to maintain system responsiveness without sacrificing accuracy on standard document layouts.