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 50,000 input tokens and 1,000 output tokens:
- Input Cost: $0.050000
- Output Cost: $0.005000 (rounded ~ $0.01)
- Total Cost: $0.036000 (rounded ~ $0.04)
- Cost per 1K tokens: $0.000706
- Tokens per dollar: 1,416,667 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: 3 minutes, 5.97 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
|
$0.001850 Best Value | ↓ 94.9% cheaper |
| 🥈 |
GPT-6 Luna
OpenAI
|
$0.001850 | ↓ 94.9% cheaper |
| 🥉 |
GPT-5.6 Luna
OpenAI
|
$0.001900 | ↓ 94.7% cheaper |
| #4 |
Gemini 3.1 Flash Lite
Google
|
$0.002375 | ↓ 93.4% cheaper |
| #5 |
Gemini 3.5 Flash-Lite
Google
|
$0.003025 | ↓ 91.6% cheaper |
| #6 |
Gemini 2.5 Flash
Google
|
$0.003025 | ↓ 91.6% cheaper |
| #7 |
Mistral Large 3
Mistral AI
|
$0.004375 | ↓ 87.8% cheaper |
| #8 |
Gemini 3.8 Flash
Google
|
$0.006938 (rounded ~ $0.01) | ↓ 80.7% cheaper |
| #9 |
Gemini 3.6 Flash
Google
|
$0.006938 (rounded ~ $0.01) | ↓ 80.7% cheaper |
| #10 |
GPT-5.4 mini
OpenAI
|
$0.007125 (rounded ~ $0.01) | ↓ 80.2% cheaper |
| #11 |
Claude Haiku 4.5
Anthropic
|
$0.009250 | ↓ 74.3% cheaper |
| #12 |
Gemini 3.1 Flash
Google
|
$0.009500 | ↓ 73.6% cheaper |
| #13 |
Mistral Large 4
Mistral AI
|
$0.011925 (rounded ~ $0.01) | ↓ 66.9% cheaper |
| #14 |
GPT-5.3 Codex Spark
OpenAI
|
$0.017500 (rounded ~ $0.02) | ↓ 51.4% cheaper |
| #15 |
GPT-5.3 Instant
OpenAI
|
$0.017500 (rounded ~ $0.02) | ↓ 51.4% cheaper |
| #16 |
Claude Sonnet 5.5
Anthropic
|
$0.018000 (rounded ~ $0.02) | ↓ 50% cheaper |
| #17 |
Claude Sonnet 5
Anthropic
|
$0.018500 (rounded ~ $0.02) | ↓ 48.6% cheaper |
| #18 |
Gemini 4 Argon
Google
|
$0.018500 (rounded ~ $0.02) | ↓ 48.6% cheaper |
| #19 |
GPT-5.6 Terra
OpenAI
|
$0.019000 (rounded ~ $0.02) | ↓ 47.2% cheaper |
| #20 |
Gemini 2.5 Pro
Google
|
$0.025000 (rounded ~ $0.03) | ↓ 30.6% cheaper |
| #21 |
Claude Sonnet 4.6
Anthropic
|
$0.027750 (rounded ~ $0.03) | ↓ 22.9% cheaper |
| #22 |
Grok 4.3
xAI
|
$0.034000 (rounded ~ $0.03) | ↓ 5.6% cheaper |
| #23 |
Grok 4.20 Beta
xAI
|
$0.034000 (rounded ~ $0.03) | ↓ 5.6% cheaper |
| #24 |
GPT-6.1 Sol
OpenAI
|
$0.037000 (rounded ~ $0.04) | ↑ 2.8% more |
| #25 |
GPT-6 Sol
OpenAI
|
$0.037000 (rounded ~ $0.04) | ↑ 2.8% more |
| #26 |
GPT-5.6 Sol
OpenAI
|
$0.037000 (rounded ~ $0.04) | ↑ 2.8% more |
| #27 |
Gemini 3.1 Pro
Google
|
$0.038000 (rounded ~ $0.04) | ↑ 5.6% more |
| #28 |
Claude Opus 4.7
Anthropic
|
$0.046250 (rounded ~ $0.05) | ↑ 28.5% more |
| #29 |
Claude Opus 5
Anthropic
|
$0.046250 (rounded ~ $0.05) | ↑ 28.5% more |
| #30 |
Claude Opus 4.8
Anthropic
|
$0.046250 (rounded ~ $0.05) | ↑ 28.5% more |
| #31 |
Claude Opus 4.6
Anthropic
|
$0.046250 (rounded ~ $0.05) | ↑ 28.5% more |
| #32 |
GPT-5.4
OpenAI
|
$0.047500 (rounded ~ $0.05) | ↑ 31.9% more |
| #33 |
GPT-5.4 Thinking
OpenAI
|
$0.047500 (rounded ~ $0.05) | ↑ 31.9% more |
| #34 |
GPT-5.5 Instant
OpenAI
|
$0.047500 (rounded ~ $0.05) | ↑ 31.9% more |
| #35 |
Claude Fable 5.1
Anthropic
|
$0.088750 (rounded ~ $0.09) | ↑ 146.5% more |
| #36 |
Claude Mythos 5.1
Anthropic
|
$0.088750 (rounded ~ $0.09) | ↑ 146.5% more |
| #37 |
Claude Fable 5
Anthropic
|
$0.092500 (rounded ~ $0.09) | ↑ 156.9% more |
| #38 |
Claude Mythos 5
Anthropic
|
$0.092500 (rounded ~ $0.09) | ↑ 156.9% more |
| #39 |
GPT-5.5
OpenAI
|
$0.095000 (rounded ~ $0.10) | ↑ 163.9% more |
| #40 |
GPT-6 Astra
OpenAI
|
$0.185000 (rounded ~ $0.19) | ↑ 413.9% more |
| #41 |
GPT-5.2 Pro
OpenAI
|
$0.210000 | ↑ 483.3% more |
| #42 |
GPT-5.2 Pro
OpenAI
|
$0.210000 | ↑ 483.3% 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
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
Claude Sonnet 5.5 Anthropic
Claude Sonnet 5 Anthropic
Gemini 4 Argon Google
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
Optimizing Legal Contract Analysis
For legal professionals handling sensitive, long-form documentation, the primary challenge is maintaining contextual accuracy across fifty-page agreements. Claude Opus 5.5 offers a sophisticated reasoning architecture that is particularly well-suited for identifying nuanced clauses, liability shifts, and non-standard terminology that standard summarization models might overlook. When processing contracts at the 50K-token scale, this model excels at maintaining coherence, ensuring that dependencies between disparate clauses are preserved throughout the review process.
Choosing this model is often the correct strategy for high-stakes due diligence where the cost of a missed oversight outweighs the incremental efficiency gains of smaller, faster alternatives. Its capacity to handle complex, multi-step instructions allows legal teams to move beyond simple extraction and into interpretative analysis, such as flagging specific compliance risks against a firm’s proprietary playbook. While it operates at a premium, the reduction in human-in-the-loop verification time often justifies the investment. Teams should consider its performance in structured output generation, which simplifies integration with downstream case management systems or document repositories. For firms aiming to automate initial contract triage while preserving the nuance required for high-level legal review, this model provides the necessary depth for complex, multi-party agreements.