Claude Sonnet 5.5 Anthropic 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.010000
Output: $0.010000
Unit: $0.000000
Fees: $0.000000
Detailed Cost Analysis (from Plugin)
For 15,000 input tokens and 1,000 output tokens:
- Input Cost: $0.030000
- Output Cost: $0.010000
- Total Cost: $0.025750 (rounded ~ $0.03)
- Cost per 1K tokens: $0.001609
- Tokens per dollar: 621,359 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 300 tokens per second and 300ms time to first token:
- Processing Time: 54.58 seconds
- Latency: 300 milliseconds to first token
- Base Throughput: 300 tokens/second
- Effective Throughput: 294 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to Claude Sonnet 5.5| Rank | AI Model & Provider | Total Cost | vs Claude Sonnet 5.5 |
|---|---|---|---|
| 🏆 |
Claude Haiku 5.5
Anthropic
|
$0.001325 Best Value | ↓ 94.9% cheaper |
| 🥈 |
GPT-6 Luna
OpenAI
|
$0.001325 | ↓ 94.9% cheaper |
| 🥉 |
GPT-5.6 Luna
OpenAI
|
$0.002850 | ↓ 88.9% cheaper |
| #4 |
Grok Code Fast 1
xAI
|
$0.003150 | ↓ 87.8% cheaper |
| #5 |
Gemini 3.1 Flash Lite
Google
|
$0.003563 | ↓ 86.2% cheaper |
| #6 |
Gemini 3.5 Flash-Lite
Google
|
$0.004975 | ↓ 80.7% cheaper |
| #7 |
Gemini 2.5 Flash
Google
|
$0.004975 | ↓ 80.7% cheaper |
| #8 |
Mistral Large 3
Mistral AI
|
$0.005625 (rounded ~ $0.01) | ↓ 78.2% cheaper |
| #9 |
Gemini 3.1 Flash
Google
|
$0.007125 (rounded ~ $0.01) | ↓ 72.3% cheaper |
| #10 |
Gemini 3.8 Flash
Google
|
$0.009938 | ↓ 61.4% cheaper |
| #11 |
Gemini 3.6 Flash
Google
|
$0.009938 | ↓ 61.4% cheaper |
| #12 |
Grok Build 0.1
xAI
|
$0.010250 | ↓ 60.2% cheaper |
| #13 |
GPT-5.4 mini
OpenAI
|
$0.010688 | ↓ 58.5% cheaper |
| #14 |
Grok 4.3
xAI
|
$0.012813 (rounded ~ $0.01) | ↓ 50.2% cheaper |
| #15 |
Grok 4.20 Beta
xAI
|
$0.012813 (rounded ~ $0.01) | ↓ 50.2% cheaper |
| #16 |
Claude Haiku 4.5
Anthropic
|
$0.013250 (rounded ~ $0.01) | ↓ 48.5% cheaper |
| #17 |
Mistral Large 4
Mistral AI
|
$0.015400 (rounded ~ $0.02) | ↓ 40.2% cheaper |
| #18 |
Gemini 2.5 Pro
Google
|
$0.020313 | ↓ 21.1% cheaper |
| #19 |
Grok 4.7
xAI
|
$0.022500 (rounded ~ $0.02) | ↓ 12.6% cheaper |
| #20 |
Grok 4.6
xAI
|
$0.022500 (rounded ~ $0.02) | ↓ 12.6% cheaper |
| #21 |
Grok 4.5
xAI
|
$0.022500 (rounded ~ $0.02) | ↓ 12.6% cheaper |
| #22 |
Claude Sonnet 5
Anthropic
|
$0.026500 (rounded ~ $0.03) | ↑ 2.9% more |
| #23 |
Gemini 4 Argon
Google
|
$0.026500 (rounded ~ $0.03) | ↑ 2.9% more |
| #24 |
GPT-6.1 Sol
OpenAI
|
$0.026500 (rounded ~ $0.03) | ↑ 2.9% more |
| #25 |
GPT-6 Sol
OpenAI
|
$0.026500 (rounded ~ $0.03) | ↑ 2.9% more |
| #26 |
Kimi K3.1 Pro
Moonshot AI
|
$0.027550 (rounded ~ $0.03) | ↑ 7% more |
| #27 |
GPT-5.3 Codex Spark
OpenAI
|
$0.028438 (rounded ~ $0.03) | ↑ 10.4% more |
| #28 |
GPT-5.3 Instant
OpenAI
|
$0.028438 (rounded ~ $0.03) | ↑ 10.4% more |
| #29 |
Gemini 3.1 Pro
Google
|
$0.028500 (rounded ~ $0.03) | ↑ 10.7% more |
| #30 |
GPT-5.6 Terra
OpenAI
|
$0.028500 (rounded ~ $0.03) | ↑ 10.7% more |
| #31 |
GPT-5.4
OpenAI
|
$0.035625 (rounded ~ $0.04) | ↑ 38.3% more |
| #32 |
GPT-5.4 Thinking
OpenAI
|
$0.035625 (rounded ~ $0.04) | ↑ 38.3% more |
| #33 |
Claude Sonnet 4.6
Anthropic
|
$0.039750 | ↑ 54.4% more |
| #34 |
Kimi K3
Moonshot AI
|
$0.041325 (rounded ~ $0.04) | ↑ 60.5% more |
| #35 |
Claude Opus 5.5
Anthropic
|
$0.051500 (rounded ~ $0.05) | ↑ 100% more |
| #36 |
GPT-5.6 Sol
OpenAI
|
$0.053000 (rounded ~ $0.05) | ↑ 105.8% more |
| #37 |
Claude Opus 4.7
Anthropic
|
$0.066250 (rounded ~ $0.07) | ↑ 157.3% more |
| #38 |
Claude Opus 5
Anthropic
|
$0.066250 (rounded ~ $0.07) | ↑ 157.3% more |
| #39 |
Claude Opus 4.8
Anthropic
|
$0.066250 (rounded ~ $0.07) | ↑ 157.3% more |
| #40 |
Claude Opus 4.6
Anthropic
|
$0.066250 (rounded ~ $0.07) | ↑ 157.3% more |
| #41 |
GPT-5.5
OpenAI
|
$0.071250 (rounded ~ $0.07) | ↑ 176.7% more |
| #42 |
GPT-5.5 Instant
OpenAI
|
$0.071250 (rounded ~ $0.07) | ↑ 176.7% more |
| #43 |
Muse Wonder 1.0
Meta AI
|
$0.080000 | ↑ 210.7% more |
| #44 |
Claude Fable 5.1
Anthropic
|
$0.126875 (rounded ~ $0.13) | ↑ 392.7% more |
| #45 |
Claude Mythos 5.1
Anthropic
|
$0.126875 (rounded ~ $0.13) | ↑ 392.7% more |
| #46 |
Claude Fable 5
Anthropic
|
$0.132500 (rounded ~ $0.13) | ↑ 414.6% more |
| #47 |
Claude Mythos 5
Anthropic
|
$0.132500 (rounded ~ $0.13) | ↑ 414.6% more |
| #48 |
GPT-6 Astra
OpenAI
|
$0.132500 (rounded ~ $0.13) | ↑ 414.6% more |
| #49 |
GPT-5.2 Pro
OpenAI
|
$0.341250 (rounded ~ $0.34) | ↑ 1225.2% more |
| #50 |
GPT-5.2 Pro
OpenAI
|
$0.341250 (rounded ~ $0.34) | ↑ 1225.2% more |
Claude Haiku 5.5 Anthropic
GPT-6 Luna OpenAI
GPT-5.6 Luna OpenAI
Grok Code Fast 1 xAI
Gemini 3.1 Flash Lite Google
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Mistral Large 3 Mistral AI
Gemini 3.1 Flash Google
Gemini 3.8 Flash Google
Gemini 3.6 Flash Google
Grok Build 0.1 xAI
GPT-5.4 mini OpenAI
Grok 4.3 xAI
Grok 4.20 Beta xAI
Claude Haiku 4.5 Anthropic
Mistral Large 4 Mistral AI
Gemini 2.5 Pro Google
Grok 4.7 xAI
Grok 4.6 xAI
Grok 4.5 xAI
Claude Sonnet 5 Anthropic
Gemini 4 Argon Google
GPT-6.1 Sol OpenAI
GPT-6 Sol OpenAI
Kimi K3.1 Pro Moonshot AI
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
Gemini 3.1 Pro Google
GPT-5.6 Terra OpenAI
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
Claude Sonnet 4.6 Anthropic
Kimi K3 Moonshot AI
Claude Opus 5.5 Anthropic
GPT-5.6 Sol OpenAI
Claude Opus 4.7 Anthropic
Claude Opus 5 Anthropic
Claude Opus 4.8 Anthropic
Claude Opus 4.6 Anthropic
GPT-5.5 OpenAI
GPT-5.5 Instant OpenAI
Muse Wonder 1.0 Meta AI
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
Claude Fable 5 Anthropic
Claude Mythos 5 Anthropic
GPT-6 Astra OpenAI
GPT-5.2 Pro OpenAI
GPT-5.2 Pro OpenAI
For healthcare administrators implementing Retrieval-Augmented Generation (RAG) within clinical settings, selecting a model that balances reasoning depth with operational efficiency is critical. Claude Sonnet 5.5 has emerged as a high-performing option for document-heavy environments where handling patient histories and clinical notes is the primary workload. The model is well-suited for RAG pipelines that require high adherence to specific medical schemas, as it demonstrates strong instruction-following capabilities when navigating complex, semi-structured data like Electronic Health Record (EHR) extracts.
When deploying this model, consider the balance between context window utilization and the speed of response. For 15K-token input workloads, Sonnet 5.5 provides a stable output, minimizing the hallucination risks often associated with smaller or less-reasoned models. In healthcare, audit trails are essential; the model’s ability to cite sources within a RAG architecture helps maintain the transparency required for clinical documentation reviews. While larger models might offer deeper reasoning for rare diagnoses, Sonnet 5.5 is often the optimal choice for high-volume, standard documentation tasks where latency and consistent formatting are priorities. Administrators should weigh the model’s throughput requirements against the need for rapid patient triage responses, ensuring that the infrastructure supports the desired concurrency levels without compromising data integrity or HIPAA-aligned data processing standards.