Claude Opus 4.7 Anthropic 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.050000
Output: $0.050000
Unit: $0.000000
Fees: $0.000000
Detailed Cost Analysis (from Plugin)
For 500,000 input tokens and 2,000 output tokens:
- Input Cost: $2.500000
- Output Cost: $0.050000
- Total Cost: $0.750000
- Cost per 1K tokens: $0.001494
- Tokens per dollar: 669,333 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 260 tokens per second and 400ms time to first token:
- Processing Time: 32 minutes, 49.56 seconds
- Latency: 400 milliseconds to first token
- Base Throughput: 260 tokens/second
- Effective Throughput: 255 tokens/second (temperature-adjusted)
Best Use Cases
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💰 Total Cost Calculation (from Plugin)
Output: $0.036000 (rounded ~ $0.04)
Output: $0.036000 (rounded ~ $0.04)
Unit: $0.000000
Fees: $0.000000
Detailed Cost Analysis (from Plugin)
For 500,000 input tokens and 2,000 output tokens:
- Input Cost: $2.000000
- Output Cost: $0.036000 (rounded ~ $0.04)
- Total Cost: $0.596000 (rounded ~ $0.60)
- Cost per 1K tokens: $0.001187
- Tokens per dollar: 842,282 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: 21 minutes, 20.28 seconds
- Latency: 220 milliseconds to first token
- Base Throughput: 400 tokens/second
- Effective Throughput: 392 tokens/second (temperature-adjusted)
Best Use Cases
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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 Opus 4.7| Rank | AI Model & Provider | Total Cost | vs Claude Opus 4.7 | vs Gemini 3.1 Pro |
|---|---|---|---|---|
| 🏆 |
Gemini 3.1 Flash Lite
Google
|
$0.038000 (rounded ~ $0.04) Best Value | ↓ 94.9% cheaper | ↓ 93.6% cheaper |
| 🥈 |
Gemini 3.5 Flash-Lite
Google
|
$0.047000 (rounded ~ $0.05) | ↓ 93.7% cheaper | ↓ 92.1% cheaper |
| 🥉 |
Gemini 2.5 Flash
Google
|
$0.047000 (rounded ~ $0.05) | ↓ 93.7% cheaper | ↓ 92.1% cheaper |
| #4 |
Gemini 3.8 Flash
Google
|
$0.112500 (rounded ~ $0.11) | ↓ 85% cheaper | ↓ 81.1% cheaper |
| #5 |
Gemini 3.1 Flash
Google
|
$0.152000 (rounded ~ $0.15) | ↓ 79.7% cheaper | ↓ 74.5% cheaper |
| #6 |
GPT-5.6 Luna
OpenAI
|
$0.152000 (rounded ~ $0.15) | ↓ 79.7% cheaper | ↓ 74.5% cheaper |
| #7 |
Gemini 3.6 Flash
Google
|
$0.225000 (rounded ~ $0.23) | ↓ 70% cheaper | ↓ 62.2% cheaper |
| #8 |
Gemini 3.5 Flash
Google
|
$0.228000 (rounded ~ $0.23) | ↓ 69.6% cheaper | ↓ 61.7% cheaper |
| #9 |
Claude Sonnet 5
Anthropic
|
$0.300000 | ↓ 60% cheaper | ↓ 49.7% cheaper |
| #10 |
Grok 4.3
xAI
|
$0.360000 | ↓ 52% cheaper | ↓ 39.6% cheaper |
| #11 |
Grok 4.20 Beta
xAI
|
$0.360000 | ↓ 52% cheaper | ↓ 39.6% cheaper |
| #12 |
Gemini 2.5 Pro
Google
|
$0.380000 | ↓ 49.3% cheaper | ↓ 36.2% cheaper |
| #13 |
GPT-5.6 Terra
OpenAI
|
$0.380000 | ↓ 49.3% cheaper | ↓ 36.2% cheaper |
| #14 |
Claude Sonnet 4.6
Anthropic
|
$0.450000 | ↓ 40% cheaper | ↓ 24.5% cheaper |
| #15 |
Gemini 3.1 Pro
Google
|
$0.596000 (rounded ~ $0.60) | ↓ 20.5% cheaper | Same price |
| #16 |
GPT-5.4
OpenAI
|
$0.745000 (rounded ~ $0.75) | ↓ 0.7% cheaper | ↑ 25% more |
| #17 |
GPT-5.4 Thinking
OpenAI
|
$0.745000 (rounded ~ $0.75) | ↓ 0.7% cheaper | ↑ 25% more |
| #18 |
Claude Opus 5
Anthropic
|
$0.750000 | Same price | ↑ 25.8% more |
| #19 |
Claude Opus 4.8
Anthropic
|
$0.750000 | Same price | ↑ 25.8% more |
| #20 |
Claude Opus 4.6
Anthropic
|
$0.750000 | Same price | ↑ 25.8% more |
| #21 |
GPT-5.6 Sol
OpenAI
|
$0.760000 | ↑ 1.3% more | ↑ 27.5% more |
| #22 |
Claude Fable 5.1
Anthropic
|
$1.200000 | ↑ 60% more | ↑ 101.3% more |
| #23 |
Claude Mythos 5.1
Anthropic
|
$1.200000 | ↑ 60% more | ↑ 101.3% more |
| #24 |
GPT-5.5
OpenAI
|
$1.490000 | ↑ 98.7% more | ↑ 150% more |
| #25 |
Claude Fable 5
Anthropic
|
$1.500000 | ↑ 100% more | ↑ 151.7% more |
| #26 |
Claude Mythos 5
Anthropic
|
$1.500000 | ↑ 100% more | ↑ 151.7% more |
| #27 |
GPT-6 Astra
OpenAI
|
$3.000000 | ↑ 300% more | ↑ 403.4% more |
| #28 |
GPT-6 Astra
OpenAI
|
$3.000000 | ↑ 300% more | ↑ 403.4% more |
Gemini 3.1 Flash Lite Google
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Gemini 3.8 Flash Google
Gemini 3.1 Flash Google
GPT-5.6 Luna OpenAI
Gemini 3.6 Flash Google
Gemini 3.5 Flash Google
Claude Sonnet 5 Anthropic
Grok 4.3 xAI
Grok 4.20 Beta xAI
Gemini 2.5 Pro Google
GPT-5.6 Terra OpenAI
Claude Sonnet 4.6 Anthropic
Gemini 3.1 Pro Google
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
Claude Opus 5 Anthropic
Claude Opus 4.8 Anthropic
Claude Opus 4.6 Anthropic
GPT-5.6 Sol OpenAI
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
GPT-5.5 OpenAI
Claude Fable 5 Anthropic
Claude Mythos 5 Anthropic
GPT-6 Astra OpenAI
GPT-6 Astra OpenAI
Choosing the Right Engine for High-Volume RAG
In 2026, the battle for enterprise-grade Retrieval-Augmented Generation (RAG) is shifting from basic context size to retrieval fidelity and reasoning depth. When managing large knowledge bases—such as internal employee Q&A systems with dozens of documents—the architecture you choose for your pipeline is just as critical as the model itself.
Claude Opus 4.7 offers a distinct advantage in structured reasoning and complex instruction following. For RAG pipelines that require the model to synthesize information across 50+ documents while strictly adhering to citation rules, Opus provides a level of architectural reliability that is difficult to replicate. Its ability to navigate long-form context without losing the thread is a cornerstone for professional-grade implementations where hallucination is not an option.
Conversely, Gemini 3.1 Pro shines in multimodal breadth and native integration capabilities. If your knowledge base includes more than just text—such as embedded diagrams, internal training videos, or call transcripts—Gemini’s native multimodal understanding allows you to ingest these sources directly without complex pre-processing or separate vision-to-text pipelines. This can significantly reduce the technical debt of your ingestion layer.
For a consistent 500K-token per-call workload, the decision often comes down to your primary data type. Choose Claude if your internal Q&A is text-heavy and requires high-fidelity, nuanced synthesis. Choose Gemini if your source material is diverse, spanning multiple formats, and requires a unified, natively multimodal retrieval strategy.