Claude Sonnet 4.6 Anthropic 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.003750
Output: $0.003750
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 100,000 input tokens and 1,000 output tokens:
- Input Cost: $0.075000 (rounded ~ $0.08)
- Output Cost: $0.003750
- Total Cost: $0.045000 (rounded ~ $0.05)
- Cost per 1K tokens: $0.000446
- Tokens per dollar: 2,244,444 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: 3 minutes, 49.11 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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💰 Total Cost Calculation (from Plugin)
Output: $0.001125
Output: $0.001125
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 100,000 input tokens and 1,000 output tokens:
- Input Cost: $0.018750 (rounded ~ $0.02)
- Output Cost: $0.001125
- Total Cost: $0.011438 (rounded ~ $0.01)
- Cost per 1K tokens: $0.000113
- Tokens per dollar: 8,830,601 tokens
- Context Window: 400000 tokens
Speed & Performance Analysis
With a processing speed of 500 tokens per second and 180ms time to first token:
- Processing Time: 3 minutes, 26.22 seconds
- Latency: 180 milliseconds to first token
- Base Throughput: 500 tokens/second
- Effective Throughput: 490 tokens/second (temperature-adjusted)
Best Use Cases
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This calculator shows the math for GPT-5.4 mini. 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 GPT-5.4 mini |
|---|---|---|---|---|
| 🏆 |
Mistral Small 3
Mistral AI
|
$0.001450 Best Value | ↓ 96.8% cheaper | ↓ 87.3% cheaper |
| 🥈 |
Grok Code Fast 1
xAI
|
$0.003125 | ↓ 93.1% cheaper | ↓ 72.7% cheaper |
| 🥉 |
Gemini 3.1 Flash Lite
Google
|
$0.003813 | ↓ 91.5% cheaper | ↓ 66.7% cheaper |
| #4 |
Gemini 2.5 Flash
Google
|
$0.004750 | ↓ 89.4% cheaper | ↓ 58.5% cheaper |
| #5 |
Mistral Large 3
Mistral AI
|
$0.007250 (rounded ~ $0.01) | ↓ 83.9% cheaper | ↓ 36.6% cheaper |
| #6 |
GPT-5.4 mini
OpenAI
|
$0.011438 (rounded ~ $0.01) | ↓ 74.6% cheaper | Same price |
| #7 |
o4-mini Deep Research
OpenAI
|
$0.014750 (rounded ~ $0.01) | ↓ 67.2% cheaper | ↑ 29% more |
| #8 |
Claude Haiku 4.5
Anthropic
|
$0.015000 (rounded ~ $0.02) | ↓ 66.7% cheaper | ↑ 31.1% more |
| #9 |
Gemini 3.1 Flash
Google
|
$0.015250 (rounded ~ $0.02) | ↓ 66.1% cheaper | ↑ 33.3% more |
| #10 |
o4-mini
OpenAI
|
$0.016225 (rounded ~ $0.02) | ↓ 63.9% cheaper | ↑ 41.9% more |
| #11 |
Grok 4.3
xAI
|
$0.017813 (rounded ~ $0.02) | ↓ 60.4% cheaper | ↑ 55.7% more |
| #12 |
Gemini 3.5 Flash
Google
|
$0.022875 (rounded ~ $0.02) | ↓ 49.2% cheaper | ↑ 100% more |
| #13 |
GPT-5.3 Codex Spark
OpenAI
|
$0.027563 (rounded ~ $0.03) | ↓ 38.8% cheaper | ↑ 141% more |
| #14 |
GPT-5.3 Instant
OpenAI
|
$0.027563 (rounded ~ $0.03) | ↓ 38.8% cheaper | ↑ 141% more |
| #15 |
Grok 4.20 Beta
xAI
|
$0.029000 | ↓ 35.6% cheaper | ↑ 153.6% more |
| #16 |
Gemini 2.5 Pro
Google
|
$0.039375 | ↓ 12.5% cheaper | ↑ 244.3% more |
| #17 |
Gemini 3.1 Pro
Google
|
$0.061000 (rounded ~ $0.06) | ↑ 35.6% more | ↑ 433.3% more |
| #18 |
Claude Opus 4.7
Anthropic
|
$0.075000 (rounded ~ $0.08) | ↑ 66.7% more | ↑ 555.7% more |
| #19 |
Claude Opus 4.8
Anthropic
|
$0.075000 (rounded ~ $0.08) | ↑ 66.7% more | ↑ 555.7% more |
| #20 |
Claude Opus 4.6
Anthropic
|
$0.075000 (rounded ~ $0.08) | ↑ 66.7% more | ↑ 555.7% more |
| #21 |
GPT-5.4
OpenAI
|
$0.076250 (rounded ~ $0.08) | ↑ 69.4% more | ↑ 566.7% more |
| #22 |
GPT-5.4 Thinking
OpenAI
|
$0.076250 (rounded ~ $0.08) | ↑ 69.4% more | ↑ 566.7% more |
| #23 |
GPT-5.5 Instant
OpenAI
|
$0.076250 (rounded ~ $0.08) | ↑ 69.4% more | ↑ 566.7% more |
| #24 |
o3 Deep Research
OpenAI
|
$0.147500 (rounded ~ $0.15) | ↑ 227.8% more | ↑ 1189.6% more |
| #25 |
GPT-5.5
OpenAI
|
$0.152500 (rounded ~ $0.15) | ↑ 238.9% more | ↑ 1233.3% more |
| #26 |
o3 Pro
OpenAI
|
$0.295000 (rounded ~ $0.30) | ↑ 555.6% more | ↑ 2479.2% more |
| #27 |
GPT-5.2 Pro
OpenAI
|
$0.330750 | ↑ 635% more | ↑ 2791.8% more |
| #28 |
GPT-5.2 Pro
OpenAI
|
$0.330750 | ↑ 635% more | ↑ 2791.8% more |
Mistral Small 3 Mistral AI
Grok Code Fast 1 xAI
Gemini 3.1 Flash Lite Google
Gemini 2.5 Flash Google
Mistral Large 3 Mistral AI
GPT-5.4 mini OpenAI
o4-mini Deep Research OpenAI
Claude Haiku 4.5 Anthropic
Gemini 3.1 Flash Google
o4-mini OpenAI
Grok 4.3 xAI
Gemini 3.5 Flash Google
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
Grok 4.20 Beta xAI
Gemini 2.5 Pro Google
Gemini 3.1 Pro Google
Claude Opus 4.7 Anthropic
Claude Opus 4.8 Anthropic
Claude Opus 4.6 Anthropic
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
GPT-5.5 Instant OpenAI
o3 Deep Research OpenAI
GPT-5.5 OpenAI
o3 Pro OpenAI
GPT-5.2 Pro OpenAI
GPT-5.2 Pro OpenAI
Choosing the Right Model for Financial Earnings Analysis
Financial analysts parsing 10-K filings require a delicate balance between reasoning depth and cost efficiency. When analyzing 100K-token documents, the choice often hinges on the specific structure of the filing. 10-K reports are dense with tabular data, footnotes, and risk factors that demand high contextual awareness and reliable extraction.
Claude Sonnet 4.6 is frequently prioritized for its ability to handle complex multi-step reasoning tasks. For analysts performing deep-dive sentiment analysis or extracting specific nuances from MD&A sections, it maintains high fidelity to instructions, ensuring that the structured output aligns precisely with downstream financial models. Its strengths lie in minimizing hallucination during long-form document synthesis and its nuanced handling of complex logical relationships within a report.
Conversely, GPT-5.4 mini is built for high-throughput, latency-sensitive applications. If your workflow involves batch-processing hundreds of quarterly filings where you need consistent, rapid extraction of standard balance sheet items, it provides a robust pathway. This model excels in predictable, repetitive data extraction tasks where speed and volume are paramount. It is specifically engineered to handle simpler subtasks efficiently, making it an excellent candidate for the extraction layer of a larger, multi-agent financial pipeline.
Ultimately, the decision depends on your pipeline’s tolerance for reasoning complexity versus speed. Teams conducting one-off forensic investigations may lean toward the reasoning capabilities of Claude, while automated data pipelines ingesting thousands of filings per day will often find greater utility in the efficiency of the GPT mini variant.