Claude Sonnet 5.5 Anthropic 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.001250
Output: $0.001250
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 50,000 input tokens and 500 output tokens:
- Input Cost: $0.025000 (rounded ~ $0.03)
- Output Cost: $0.001250
- Total Cost: $0.004875
- Cost per 1K tokens: $0.000097
- Tokens per dollar: 10,358,974 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: 3 minutes, 0.30 seconds
- Latency: 300 milliseconds to first token
- Base Throughput: 300 tokens/second
- Effective Throughput: 280 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
|
$0.000600 Best Value | ↓ 87.7% cheaper |
| 🥈 |
GPT-6 Luna
OpenAI
|
$0.000600 | ↓ 87.7% cheaper |
| 🥉 |
GPT-5.6 Luna
OpenAI
|
$0.000625 | ↓ 87.2% cheaper |
| #4 |
Gemini 3.1 Flash Lite
Google
|
$0.000781 | ↓ 84% cheaper |
| #5 |
Gemini 3.5 Flash-Lite
Google
|
$0.001025 | ↓ 79% cheaper |
| #6 |
Gemini 2.5 Flash
Google
|
$0.001025 | ↓ 79% cheaper |
| #7 |
Mistral Large 3
Mistral AI
|
$0.001375 | ↓ 71.8% cheaper |
| #8 |
Gemini 3.8 Flash
Google
|
$0.002250 | ↓ 53.8% cheaper |
| #9 |
Gemini 3.6 Flash
Google
|
$0.002250 | ↓ 53.8% cheaper |
| #10 |
GPT-5.4 mini
OpenAI
|
$0.002344 | ↓ 51.9% cheaper |
| #11 |
Claude Haiku 4.5
Anthropic
|
$0.003000 | ↓ 38.5% cheaper |
| #12 |
Gemini 3.1 Flash
Google
|
$0.003125 | ↓ 35.9% cheaper |
| #13 |
Mistral Large 4
Mistral AI
|
$0.003753 | ↓ 23% cheaper |
| #14 |
GPT-5.3 Codex Spark
OpenAI
|
$0.005906 (rounded ~ $0.01) | ↑ 21.2% more |
| #15 |
GPT-5.3 Instant
OpenAI
|
$0.005906 (rounded ~ $0.01) | ↑ 21.2% more |
| #16 |
Gemini 4 Argon
Google
|
$0.006000 (rounded ~ $0.01) | ↑ 23.1% more |
| #17 |
Claude Sonnet 5
Anthropic
|
$0.006000 (rounded ~ $0.01) | ↑ 23.1% more |
| #18 |
GPT-5.6 Terra
OpenAI
|
$0.006250 (rounded ~ $0.01) | ↑ 28.2% more |
| #19 |
Gemini 2.5 Pro
Google
|
$0.008438 (rounded ~ $0.01) | ↑ 73.1% more |
| #20 |
Claude Sonnet 4.6
Anthropic
|
$0.009000 | ↑ 84.6% more |
| #21 |
Claude Opus 5.5
Anthropic
|
$0.009750 | ↑ 100% more |
| #22 |
Grok 4.3
xAI
|
$0.010500 | ↑ 115.4% more |
| #23 |
Grok 4.20 Beta
xAI
|
$0.010500 | ↑ 115.4% more |
| #24 |
GPT-6.1 Sol
OpenAI
|
$0.012000 (rounded ~ $0.01) | ↑ 146.2% more |
| #25 |
GPT-6 Sol
OpenAI
|
$0.012000 (rounded ~ $0.01) | ↑ 146.2% more |
| #26 |
GPT-5.6 Sol
OpenAI
|
$0.012000 (rounded ~ $0.01) | ↑ 146.2% more |
| #27 |
Gemini 3.1 Pro
Google
|
$0.012500 (rounded ~ $0.01) | ↑ 156.4% more |
| #28 |
Claude Opus 4.7
Anthropic
|
$0.015000 (rounded ~ $0.02) | ↑ 207.7% more |
| #29 |
Claude Opus 5
Anthropic
|
$0.015000 (rounded ~ $0.02) | ↑ 207.7% more |
| #30 |
Claude Opus 4.8
Anthropic
|
$0.015000 (rounded ~ $0.02) | ↑ 207.7% more |
| #31 |
Claude Opus 4.6
Anthropic
|
$0.015000 (rounded ~ $0.02) | ↑ 207.7% more |
| #32 |
GPT-5.4
OpenAI
|
$0.015625 (rounded ~ $0.02) | ↑ 220.5% more |
| #33 |
GPT-5.4 Thinking
OpenAI
|
$0.015625 (rounded ~ $0.02) | ↑ 220.5% more |
| #34 |
GPT-5.5 Instant
OpenAI
|
$0.015625 (rounded ~ $0.02) | ↑ 220.5% more |
| #35 |
Claude Fable 5.1
Anthropic
|
$0.021563 (rounded ~ $0.02) | ↑ 342.3% more |
| #36 |
Claude Mythos 5.1
Anthropic
|
$0.021563 (rounded ~ $0.02) | ↑ 342.3% more |
| #37 |
Claude Fable 5
Anthropic
|
$0.030000 | ↑ 515.4% more |
| #38 |
Claude Mythos 5
Anthropic
|
$0.030000 | ↑ 515.4% more |
| #39 |
GPT-5.5
OpenAI
|
$0.031250 (rounded ~ $0.03) | ↑ 541% more |
| #40 |
GPT-6 Astra
OpenAI
|
$0.060000 | ↑ 1130.8% more |
| #41 |
GPT-5.2 Pro
OpenAI
|
$0.070875 | ↑ 1353.8% more |
| #42 |
GPT-5.2 Pro
OpenAI
|
$0.070875 | ↑ 1353.8% 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
Gemini 4 Argon Google
Claude Sonnet 5 Anthropic
GPT-5.6 Terra OpenAI
Gemini 2.5 Pro Google
Claude Sonnet 4.6 Anthropic
Claude Opus 5.5 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 RAG Pipelines with Claude Sonnet 5.5
For engineering teams managing massive RAG pipelines at 1 billion tokens monthly, Claude Sonnet 5.5 offers a balanced trade-off between intelligence and throughput. Its architecture is specifically optimized for well-scoped everyday tasks, allowing for faster response times compared to the more heavyweight Opus series without sacrificing the reasoning capabilities required for complex document synthesis.
In high-volume orchestrator patterns, Sonnet 5.5 excels at minimizing latency while maintaining the accuracy needed for enterprise-grade information retrieval. Its improvements in token efficiency are particularly valuable for voice AI agents that require rapid, context-aware responses to maintain a conversational flow. By reducing the number of steps required to execute multi-step tool calls, Sonnet 5.5 helps lower overall infrastructure latency, which is critical for sub-second production environments.
Engineers should consider Sonnet 5.5 for workloads where consistent, reliable performance is required across diverse document types. Its ability to scope changes and check its own work before final output makes it a robust choice for autonomous agents operating in production. Whether you are building internal Q&A systems or customer-facing discovery tools, this model provides the necessary intelligence to handle complex RAG tasks at scale while keeping operational overhead predictable.