Claude Sonnet 4.6 Anthropic 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.007500 (rounded ~ $0.01)
Output: $0.007500 (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: $0.075000 (rounded ~ $0.08)
- Output Cost: $0.007500 (rounded ~ $0.01)
- Total Cost: $0.048750 (rounded ~ $0.05)
- Cost per 1K tokens: $0.000478
- Tokens per dollar: 2,092,308 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 450 tokens per second and 200ms time to first token:
- Processing Time: 3 minutes, 58.18 seconds
- Latency: 200 milliseconds to first token
- Base Throughput: 450 tokens/second
- Effective Throughput: 429 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to Claude Sonnet 4.6| Rank | AI Model & Provider | Total Cost | vs Claude Sonnet 4.6 |
|---|---|---|---|
| 🏆 |
Mistral Small 3
Mistral AI
|
$0.001525 Best Value | ↓ 96.9% cheaper |
| 🥈 |
Gemini 3.1 Flash Lite
Google
|
$0.004188 | ↓ 91.4% cheaper |
| 🥉 |
Gemini 3.5 Flash-Lite
Google
|
$0.005375 (rounded ~ $0.01) | ↓ 89% cheaper |
| #4 |
Gemini 2.5 Flash
Google
|
$0.005375 (rounded ~ $0.01) | ↓ 89% cheaper |
| #5 |
Mistral Large 3
Mistral AI
|
$0.007625 (rounded ~ $0.01) | ↓ 84.4% cheaper |
| #6 |
Gemini 3.8 Flash
Google
|
$0.012188 (rounded ~ $0.01) | ↓ 75% cheaper |
| #7 |
GPT-5.4 mini
OpenAI
|
$0.012563 (rounded ~ $0.01) | ↓ 74.2% cheaper |
| #8 |
o4-mini Deep Research
OpenAI
|
$0.015750 (rounded ~ $0.02) | ↓ 67.7% cheaper |
| #9 |
Claude Haiku 4.5
Anthropic
|
$0.016250 (rounded ~ $0.02) | ↓ 66.7% cheaper |
| #10 |
Gemini 3.1 Flash
Google
|
$0.016750 (rounded ~ $0.02) | ↓ 65.6% cheaper |
| #11 |
GPT-5.6 Luna
OpenAI
|
$0.016750 (rounded ~ $0.02) | ↓ 65.6% cheaper |
| #12 |
o4-mini
OpenAI
|
$0.017325 (rounded ~ $0.02) | ↓ 64.5% cheaper |
| #13 |
Gemini 3.6 Flash
Google
|
$0.024375 (rounded ~ $0.02) | ↓ 50% cheaper |
| #14 |
Gemini 3.5 Flash
Google
|
$0.025125 (rounded ~ $0.03) | ↓ 48.5% cheaper |
| #15 |
GPT-5.3 Codex Spark
OpenAI
|
$0.031063 (rounded ~ $0.03) | ↓ 36.3% cheaper |
| #16 |
GPT-5.3 Instant
OpenAI
|
$0.031063 (rounded ~ $0.03) | ↓ 36.3% cheaper |
| #17 |
Claude Sonnet 5
Anthropic
|
$0.032500 (rounded ~ $0.03) | ↓ 33.3% cheaper |
| #18 |
GPT-5.6 Terra
OpenAI
|
$0.041875 (rounded ~ $0.04) | ↓ 14.1% cheaper |
| #19 |
Gemini 2.5 Pro
Google
|
$0.044375 (rounded ~ $0.04) | ↓ 9% cheaper |
| #20 |
Grok 4.3
xAI
|
$0.059000 (rounded ~ $0.06) | ↑ 21% more |
| #21 |
Grok 4.20 Beta
xAI
|
$0.059000 (rounded ~ $0.06) | ↑ 21% more |
| #22 |
Gemini 3.1 Pro
Google
|
$0.067000 (rounded ~ $0.07) | ↑ 37.4% more |
| #23 |
Claude Opus 4.7
Anthropic
|
$0.081250 (rounded ~ $0.08) | ↑ 66.7% more |
| #24 |
Claude Opus 5
Anthropic
|
$0.081250 (rounded ~ $0.08) | ↑ 66.7% more |
| #25 |
Claude Opus 4.8
Anthropic
|
$0.081250 (rounded ~ $0.08) | ↑ 66.7% more |
| #26 |
Claude Opus 4.6
Anthropic
|
$0.081250 (rounded ~ $0.08) | ↑ 66.7% more |
| #27 |
GPT-5.4
OpenAI
|
$0.083750 (rounded ~ $0.08) | ↑ 71.8% more |
| #28 |
GPT-5.4 Thinking
OpenAI
|
$0.083750 (rounded ~ $0.08) | ↑ 71.8% more |
| #29 |
GPT-5.5 Instant
OpenAI
|
$0.083750 (rounded ~ $0.08) | ↑ 71.8% more |
| #30 |
GPT-5.6 Sol
OpenAI
|
$0.083750 (rounded ~ $0.08) | ↑ 71.8% more |
| #31 |
Claude Fable 5.1
Anthropic
|
$0.153125 (rounded ~ $0.15) | ↑ 214.1% more |
| #32 |
Claude Mythos 5.1
Anthropic
|
$0.153125 (rounded ~ $0.15) | ↑ 214.1% more |
| #33 |
o3 Deep Research
OpenAI
|
$0.157500 (rounded ~ $0.16) | ↑ 223.1% more |
| #34 |
Claude Fable 5
Anthropic
|
$0.162500 (rounded ~ $0.16) | ↑ 233.3% more |
| #35 |
Claude Mythos 5
Anthropic
|
$0.162500 (rounded ~ $0.16) | ↑ 233.3% more |
| #36 |
GPT-5.5
OpenAI
|
$0.167500 (rounded ~ $0.17) | ↑ 243.6% more |
| #37 |
o3 Pro
OpenAI
|
$0.315000 (rounded ~ $0.32) | ↑ 546.2% more |
| #38 |
GPT-6 Astra
OpenAI
|
$0.325000 (rounded ~ $0.33) | ↑ 566.7% more |
| #39 |
GPT-5.2 Pro
OpenAI
|
$0.372750 (rounded ~ $0.37) | ↑ 664.6% more |
| #40 |
GPT-5.2 Pro
OpenAI
|
$0.372750 (rounded ~ $0.37) | ↑ 664.6% more |
Mistral Small 3 Mistral AI
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
GPT-5.4 mini OpenAI
o4-mini Deep Research OpenAI
Claude Haiku 4.5 Anthropic
Gemini 3.1 Flash Google
GPT-5.6 Luna OpenAI
o4-mini OpenAI
Gemini 3.6 Flash Google
Gemini 3.5 Flash Google
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
Claude Sonnet 5 Anthropic
GPT-5.6 Terra OpenAI
Gemini 2.5 Pro Google
Grok 4.3 xAI
Grok 4.20 Beta xAI
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
GPT-5.6 Sol OpenAI
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
o3 Deep Research OpenAI
Claude Fable 5 Anthropic
Claude Mythos 5 Anthropic
GPT-5.5 OpenAI
o3 Pro OpenAI
GPT-6 Astra OpenAI
GPT-5.2 Pro OpenAI
GPT-5.2 Pro OpenAI
Balancing Intelligence and Speed in Voice Agents
Voice agents are moving beyond simple Q&A toward complex, multi-step orchestration. When an agent needs to perform deep reasoning—perhaps cross-referencing customer data, checking inventory, and formulating a nuanced response—Claude Sonnet 4.6 stands out as the primary workhorse. Its architecture is tuned for reliability in tool-calling scenarios, which is critical when a voice assistant must interact with internal APIs reliably to avoid awkward silences or incorrect actions.
The advantage of using Sonnet 4.6 in this context is its ability to handle long-context reasoning without degrading performance. Unlike smaller, faster models that might lose the thread of a conversation or misinterpret complex instructions, Sonnet 4.6 maintains high adherence to system prompts throughout extended calls. This makes it ideal for enterprise-grade voice agents where task completion accuracy is paramount. While it may not match the sub-second latency of specialized real-time models, its capability to process 100,000-token contexts allows for sophisticated logic that differentiates a premium, highly capable assistant from a basic script-follower. For teams focused on building high-value autonomous agents, the trade-off in raw latency is often offset by the reduction in error rates and the ability to handle complex, unstructured user requests on the first attempt.