Claude Opus 5.5 Anthropic 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.005000 (rounded ~ $0.01)
Output: $0.005000 (rounded ~ $0.01)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 25,000 input tokens and 1,000 output tokens:
- Input Cost: $0.025000 (rounded ~ $0.03)
- Output Cost: $0.005000 (rounded ~ $0.01)
- Total Cost: $0.018125 (rounded ~ $0.02)
- Cost per 1K tokens: $0.000697
- Tokens per dollar: 1,434,483 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 280 tokens per second and 380ms time to first token:
- Processing Time: 1 minute, 34.89 seconds
- Latency: 380 milliseconds to first token
- Base Throughput: 280 tokens/second
- Effective Throughput: 275 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to Claude Opus 5.5| Rank | AI Model & Provider | Total Cost | vs Claude Opus 5.5 |
|---|---|---|---|
| 🏆 |
Claude Haiku 5.5
Anthropic
|
$0.000938 Best Value | ↓ 94.8% cheaper |
| 🥈 |
GPT-6 Luna
OpenAI
|
$0.000938 | ↓ 94.8% cheaper |
| 🥉 |
GPT-5.6 Luna
OpenAI
|
$0.000988 | ↓ 94.6% cheaper |
| #4 |
Gemini 3.1 Flash Lite
Google
|
$0.001234 | ↓ 93.2% cheaper |
| #5 |
Gemini 3.5 Flash-Lite
Google
|
$0.001656 | ↓ 90.9% cheaper |
| #6 |
Gemini 2.5 Flash
Google
|
$0.001656 | ↓ 90.9% cheaper |
| #7 |
Mistral Large 3
Mistral AI
|
$0.002094 | ↓ 88.4% cheaper |
| #8 |
Gemini 3.8 Flash
Google
|
$0.003516 | ↓ 80.6% cheaper |
| #9 |
Gemini 3.6 Flash
Google
|
$0.003516 | ↓ 80.6% cheaper |
| #10 |
GPT-5.4 mini
OpenAI
|
$0.003703 | ↓ 79.6% cheaper |
| #11 |
Claude Haiku 4.5
Anthropic
|
$0.004688 | ↓ 74.1% cheaper |
| #12 |
Gemini 3.1 Flash
Google
|
$0.004938 | ↓ 72.8% cheaper |
| #13 |
Mistral Large 4
Mistral AI
|
$0.005720 (rounded ~ $0.01) | ↓ 68.4% cheaper |
| #14 |
Claude Sonnet 5.5
Anthropic
|
$0.009063 | ↓ 50% cheaper |
| #15 |
Gemini 4 Argon
Google
|
$0.009375 | ↓ 48.3% cheaper |
| #16 |
Claude Sonnet 5
Anthropic
|
$0.009375 | ↓ 48.3% cheaper |
| #17 |
GPT-5.3 Codex Spark
OpenAI
|
$0.009516 | ↓ 47.5% cheaper |
| #18 |
GPT-5.3 Instant
OpenAI
|
$0.009516 | ↓ 47.5% cheaper |
| #19 |
GPT-5.6 Terra
OpenAI
|
$0.009875 | ↓ 45.5% cheaper |
| #20 |
Gemini 2.5 Pro
Google
|
$0.013594 (rounded ~ $0.01) | ↓ 25% cheaper |
| #21 |
Claude Sonnet 4.6
Anthropic
|
$0.014063 (rounded ~ $0.01) | ↓ 22.4% cheaper |
| #22 |
Grok 4.3
xAI
|
$0.015750 (rounded ~ $0.02) | ↓ 13.1% cheaper |
| #23 |
Grok 4.20 Beta
xAI
|
$0.015750 (rounded ~ $0.02) | ↓ 13.1% cheaper |
| #24 |
GPT-6.1 Sol
OpenAI
|
$0.018750 (rounded ~ $0.02) | ↑ 3.4% more |
| #25 |
GPT-6 Sol
OpenAI
|
$0.018750 (rounded ~ $0.02) | ↑ 3.4% more |
| #26 |
GPT-5.6 Sol
OpenAI
|
$0.018750 (rounded ~ $0.02) | ↑ 3.4% more |
| #27 |
Gemini 3.1 Pro
Google
|
$0.019750 | ↑ 9% more |
| #28 |
Claude Opus 4.7
Anthropic
|
$0.023438 (rounded ~ $0.02) | ↑ 29.3% more |
| #29 |
Claude Opus 5
Anthropic
|
$0.023438 (rounded ~ $0.02) | ↑ 29.3% more |
| #30 |
Claude Opus 4.8
Anthropic
|
$0.023438 (rounded ~ $0.02) | ↑ 29.3% more |
| #31 |
Claude Opus 4.6
Anthropic
|
$0.023438 (rounded ~ $0.02) | ↑ 29.3% more |
| #32 |
GPT-5.4
OpenAI
|
$0.024688 (rounded ~ $0.02) | ↑ 36.2% more |
| #33 |
GPT-5.4 Thinking
OpenAI
|
$0.024688 (rounded ~ $0.02) | ↑ 36.2% more |
| #34 |
GPT-5.5 Instant
OpenAI
|
$0.024688 (rounded ~ $0.02) | ↑ 36.2% more |
| #35 |
Claude Fable 5.1
Anthropic
|
$0.044531 (rounded ~ $0.04) | ↑ 145.7% more |
| #36 |
Claude Mythos 5.1
Anthropic
|
$0.044531 (rounded ~ $0.04) | ↑ 145.7% more |
| #37 |
Claude Fable 5
Anthropic
|
$0.046875 (rounded ~ $0.05) | ↑ 158.6% more |
| #38 |
Claude Mythos 5
Anthropic
|
$0.046875 (rounded ~ $0.05) | ↑ 158.6% more |
| #39 |
GPT-5.5
OpenAI
|
$0.049375 | ↑ 172.4% more |
| #40 |
GPT-6 Astra
OpenAI
|
$0.093750 (rounded ~ $0.09) | ↑ 417.2% more |
| #41 |
GPT-5.2 Pro
OpenAI
|
$0.114188 (rounded ~ $0.11) | ↑ 530% more |
| #42 |
GPT-5.2 Pro
OpenAI
|
$0.114188 (rounded ~ $0.11) | ↑ 530% more |
Claude Haiku 5.5 Anthropic
GPT-6 Luna OpenAI
GPT-5.6 Luna OpenAI
Gemini 3.1 Flash Lite Google
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Mistral Large 3 Mistral AI
Gemini 3.8 Flash Google
Gemini 3.6 Flash Google
GPT-5.4 mini OpenAI
Claude Haiku 4.5 Anthropic
Gemini 3.1 Flash Google
Mistral Large 4 Mistral AI
Claude Sonnet 5.5 Anthropic
Gemini 4 Argon Google
Claude Sonnet 5 Anthropic
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
GPT-5.6 Terra OpenAI
Gemini 2.5 Pro Google
Claude Sonnet 4.6 Anthropic
Grok 4.3 xAI
Grok 4.20 Beta xAI
GPT-6.1 Sol OpenAI
GPT-6 Sol OpenAI
GPT-5.6 Sol OpenAI
Gemini 3.1 Pro Google
Claude Opus 4.7 Anthropic
Claude Opus 5 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
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
Claude Fable 5 Anthropic
Claude Mythos 5 Anthropic
GPT-5.5 OpenAI
GPT-6 Astra OpenAI
GPT-5.2 Pro OpenAI
GPT-5.2 Pro OpenAI
For legal researchers and contract analysts, evaluating the cost-benefit ratio of AI integration starts with identifying models that balance reasoning depth with token efficiency. Claude Opus 5.5 stands out as a primary candidate for high-stakes document review, where the precision of clause identification is critical. Unlike lighter models that might overlook nuanced liability language or subtle cross-references, this model provides high-fidelity reasoning required for complex, 50-page legal instruments.
When processing a standard 25,000-token contract, researchers must prioritize models that handle long-context windows without loss of focus. Claude Opus 5.5 maintains state-of-the-art performance in structured data extraction, making it highly suitable for turning raw legal text into standardized summaries or compliance reports. For legal teams operating at scale—such as conducting due diligence on hundreds of agreements—leveraging a model with this level of reasoning capability can significantly reduce the hours spent on manual review. Factors such as vendor stability, data privacy commitments, and the ability to handle specific legal taxonomies are paramount. In this context, Claude Opus 5.5 offers a robust architecture for teams that cannot afford the risks associated with hallucinations in contract clause extraction. Its performance justifies the investment for workflows where accuracy is the non-negotiable benchmark.