DeepSeek V4 Pro DeepSeek 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.004350
Output: $0.004350
Unit: $0.000000
Fees: $0.000000
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 5,000 output tokens:
- Input Cost: $0.435000 (rounded ~ $0.44)
- Output Cost: $0.004350
- Total Cost: $0.354090 (rounded ~ $0.35)
- Cost per 1K tokens: $0.000352
- Tokens per dollar: 2,838,261 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 300 tokens per second and 180ms time to first token:
- Processing Time: 56 minutes, 57.18 seconds
- Latency: 180 milliseconds to first token
- Base Throughput: 300 tokens/second
- Effective Throughput: 294 tokens/second (temperature-adjusted)
Best Use Cases
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This calculator shows the math for DeepSeek V4 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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💰 Total Cost Calculation (from Plugin)
Output: $0.045000 (rounded ~ $0.05)
Output: $0.045000 (rounded ~ $0.05)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 5,000 output tokens:
- Input Cost: $2.000000
- Output Cost: $0.045000 (rounded ~ $0.05)
- Total Cost: $1.685000 (rounded ~ $1.69)
- Cost per 1K tokens: $0.001677
- Tokens per dollar: 596,439 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 400 tokens per second and 220ms time to first token:
- Processing Time: 42 minutes, 42.93 seconds
- Latency: 220 milliseconds to first token
- Base Throughput: 400 tokens/second
- Effective Throughput: 392 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 DeepSeek V4 Pro| Rank | AI Model & Provider | Total Cost | vs DeepSeek V4 Pro | vs Gemini 3.1 Pro |
|---|---|---|---|---|
| 🏆 |
Gemini 3.5 Flash-Lite
Google
|
$0.064625 (rounded ~ $0.06) Best Value | ↓ 81.7% cheaper | ↓ 96.2% cheaper |
| 🥈 |
Gemini 3.8 Flash
Google
|
$0.158438 (rounded ~ $0.16) | ↓ 55.3% cheaper | ↓ 90.6% cheaper |
| 🥉 |
Gemini 3.6 Flash
Google
|
$0.316875 (rounded ~ $0.32) | ↓ 10.5% cheaper | ↓ 81.2% cheaper |
| #4 |
Gemini 2.5 Pro
Google
|
$1.062500 (rounded ~ $1.06) | ↑ 200.1% more | ↓ 36.9% cheaper |
| #5 |
GPT-5.4
OpenAI
|
$2.106250 (rounded ~ $2.11) | ↑ 494.8% more | ↑ 25% more |
| #6 |
GPT-5.4 Thinking
OpenAI
|
$2.106250 (rounded ~ $2.11) | ↑ 494.8% more | ↑ 25% more |
| #7 |
GPT-6 Astra
OpenAI
|
$8.450000 | ↑ 2286.4% more | ↑ 401.5% more |
| #8 |
GPT-6 Astra
OpenAI
|
$8.450000 | ↑ 2286.4% more | ↑ 401.5% 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
Evaluating Reasoning Models for Clause Analysis
When legal contract review moves beyond simple keyword extraction into the territory of risk assessment and liability analysis, the requirements for the underlying AI shift toward deep reasoning. In these high-stakes environments, DeepSeek V4 Pro and Gemini 3.1 Pro represent two distinct approaches to handling massive context windows for RAG-driven pipelines. Both models are designed to ingest entire libraries of precedents and contracts, but they offer different qualitative advantages for the legal analyst.
Gemini 3.1 Pro stands out for its expansive context window, which is particularly useful when comparing a current contract against a vast repository of historical agreements to find inconsistencies. Its multimodal capabilities allow it to reason through charts and visual addendums often found in commercial leases or construction contracts. In contrast, DeepSeek V4 Pro is optimized for structured reasoning and logical consistency, often proving more reliable for generating code-like extractions of legal logic that can be fed directly into downstream compliance software.
Choosing between these two often comes down to the specific nature of the retrieval-augmented generation. If the workload involves searching through millions of tokens to find a specific needle in a haystack, the larger context ceiling of the Google ecosystem provides a safety net. However, for teams focused on the internal logic of a single, dense document where every clause must be cross-checked for logical circularity, the specialized reasoning focus of the DeepSeek architecture offers a compelling alternative.