Claude Sonnet 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 100,000 input tokens and 2,000 output tokens:
- Input Cost: $0.050000
- Output Cost: $0.005000 (rounded ~ $0.01)
- Total Cost: $0.031250 (rounded ~ $0.03)
- Cost per 1K tokens: $0.000306
- Tokens per dollar: 3,264,000 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: 6 minutes, 3.98 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
|
$0.003250 Best Value | ↓ 89.6% cheaper |
| 🥈 |
GPT-6 Luna
OpenAI
|
$0.003250 | ↓ 89.6% cheaper |
| 🥉 |
GPT-5.6 Luna
OpenAI
|
$0.003350 | ↓ 89.3% cheaper |
| #4 |
Gemini 3.1 Flash Lite
Google
|
$0.004188 | ↓ 86.6% cheaper |
| #5 |
Gemini 3.5 Flash-Lite
Google
|
$0.005375 (rounded ~ $0.01) | ↓ 82.8% cheaper |
| #6 |
Gemini 2.5 Flash
Google
|
$0.005375 (rounded ~ $0.01) | ↓ 82.8% cheaper |
| #7 |
Mistral Large 3
Mistral AI
|
$0.007625 (rounded ~ $0.01) | ↓ 75.6% cheaper |
| #8 |
Gemini 3.8 Flash
Google
|
$0.012188 (rounded ~ $0.01) | ↓ 61% cheaper |
| #9 |
Gemini 3.6 Flash
Google
|
$0.012188 (rounded ~ $0.01) | ↓ 61% cheaper |
| #10 |
GPT-5.4 mini
OpenAI
|
$0.012563 (rounded ~ $0.01) | ↓ 59.8% cheaper |
| #11 |
Claude Haiku 4.5
Anthropic
|
$0.016250 (rounded ~ $0.02) | ↓ 48% cheaper |
| #12 |
Gemini 3.1 Flash
Google
|
$0.016750 (rounded ~ $0.02) | ↓ 46.4% cheaper |
| #13 |
Mistral Large 4
Mistral AI
|
$0.020790 | ↓ 33.5% cheaper |
| #14 |
GPT-5.3 Codex Spark
OpenAI
|
$0.031063 (rounded ~ $0.03) | ↓ 0.6% cheaper |
| #15 |
GPT-5.3 Instant
OpenAI
|
$0.031063 (rounded ~ $0.03) | ↓ 0.6% cheaper |
| #16 |
Claude Sonnet 5
Anthropic
|
$0.032500 (rounded ~ $0.03) | ↑ 4% more |
| #17 |
Gemini 4 Argon
Google
|
$0.032500 (rounded ~ $0.03) | ↑ 4% more |
| #18 |
GPT-5.6 Terra
OpenAI
|
$0.033500 (rounded ~ $0.03) | ↑ 7.2% more |
| #19 |
Gemini 2.5 Pro
Google
|
$0.044375 (rounded ~ $0.04) | ↑ 42% more |
| #20 |
Claude Sonnet 4.6
Anthropic
|
$0.048750 (rounded ~ $0.05) | ↑ 56% more |
| #21 |
Grok 4.3
xAI
|
$0.059000 (rounded ~ $0.06) | ↑ 88.8% more |
| #22 |
Grok 4.20 Beta
xAI
|
$0.059000 (rounded ~ $0.06) | ↑ 88.8% more |
| #23 |
Claude Opus 5.5
Anthropic
|
$0.062500 (rounded ~ $0.06) | ↑ 100% more |
| #24 |
GPT-6.1 Sol
OpenAI
|
$0.065000 (rounded ~ $0.07) | ↑ 108% more |
| #25 |
GPT-6 Sol
OpenAI
|
$0.065000 (rounded ~ $0.07) | ↑ 108% more |
| #26 |
GPT-5.6 Sol
OpenAI
|
$0.065000 (rounded ~ $0.07) | ↑ 108% more |
| #27 |
Gemini 3.1 Pro
Google
|
$0.067000 (rounded ~ $0.07) | ↑ 114.4% more |
| #28 |
Claude Opus 4.7
Anthropic
|
$0.081250 (rounded ~ $0.08) | ↑ 160% more |
| #29 |
Claude Opus 5
Anthropic
|
$0.081250 (rounded ~ $0.08) | ↑ 160% more |
| #30 |
Claude Opus 4.8
Anthropic
|
$0.081250 (rounded ~ $0.08) | ↑ 160% more |
| #31 |
Claude Opus 4.6
Anthropic
|
$0.081250 (rounded ~ $0.08) | ↑ 160% more |
| #32 |
GPT-5.4
OpenAI
|
$0.083750 (rounded ~ $0.08) | ↑ 168% more |
| #33 |
GPT-5.4 Thinking
OpenAI
|
$0.083750 (rounded ~ $0.08) | ↑ 168% more |
| #34 |
GPT-5.5 Instant
OpenAI
|
$0.083750 (rounded ~ $0.08) | ↑ 168% more |
| #35 |
Claude Fable 5.1
Anthropic
|
$0.153125 (rounded ~ $0.15) | ↑ 390% more |
| #36 |
Claude Mythos 5.1
Anthropic
|
$0.153125 (rounded ~ $0.15) | ↑ 390% more |
| #37 |
Claude Fable 5
Anthropic
|
$0.162500 (rounded ~ $0.16) | ↑ 420% more |
| #38 |
Claude Mythos 5
Anthropic
|
$0.162500 (rounded ~ $0.16) | ↑ 420% more |
| #39 |
GPT-5.5
OpenAI
|
$0.167500 (rounded ~ $0.17) | ↑ 436% more |
| #40 |
GPT-6 Astra
OpenAI
|
$0.325000 (rounded ~ $0.33) | ↑ 940% more |
| #41 |
GPT-5.2 Pro
OpenAI
|
$0.372750 (rounded ~ $0.37) | ↑ 1092.8% more |
| #42 |
GPT-5.2 Pro
OpenAI
|
$0.372750 (rounded ~ $0.37) | ↑ 1092.8% 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
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
Claude Opus 5.5 Anthropic
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
Orchestrating multi-agent systems requires a balance of high-level reasoning and reliable instruction following. When deploying an orchestrator-worker pattern, the orchestrator model must interpret complex, multi-step goals and delegate tasks effectively to specialized workers. Claude Sonnet 5.5 excels in these scenarios due to its refined reasoning capabilities, which significantly reduce the rate of misclassification or poor task delegation—a common pain point in production agentic loops.
For teams scaling these agentic architectures, the ability to maintain context across 100K-token tasks is vital. You need a model that doesn’t just process the prompt but understands the dependencies between successive worker outputs. Sonnet 5.5 provides a robust framework for handling these structured workflows without requiring frequent re-prompting or complex error-handling chains.
Furthermore, the latency profile of Sonnet 5.5 allows for snappier agent interactions, improving the user experience in live, conversational platforms. While some workloads might lean toward cheaper, lighter models, the orchestrator tier benefits immensely from the higher intelligence threshold provided by this architecture. It handles nuanced instructions better than previous iterations, making it a reliable choice for production environments where agent reliability is paramount. By offloading complex logic to the orchestrator, dev teams can often keep worker agents simpler and more focused, creating a more maintainable and modular codebase. This approach simplifies debugging and auditing agent logs, as the orchestrator acts as a clear, logical pivot point for all automated activities.