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: $0.028500 (rounded ~ $0.03)
- Cost per 1K tokens: $0.000279
- Tokens per dollar: 3,578,947 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: 4 minutes, 2.71 seconds
- Latency: 200 milliseconds to first token
- Base Throughput: 450 tokens/second
- Effective Throughput: 421 tokens/second (temperature-adjusted)
Best Use Cases
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This calculator shows the math for Claude Sonnet 4.6. Your decision needs more — current infrastructure, compliance requirements, actual workload patterns, volume tiers — that change which model is right for you.
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💰 Total Cost Calculation (from Plugin)
Output: $0.012000 (rounded ~ $0.01)
Output: $0.012000 (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: $0.100000
- Output Cost: $0.012000 (rounded ~ $0.01)
- Total Cost: $0.040000
- Cost per 1K tokens: $0.000392
- Tokens per dollar: 2,550,000 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 400 tokens per second and 220ms time to first token:
- Processing Time: 4 minutes, 33.03 seconds
- Latency: 220 milliseconds to first token
- Base Throughput: 400 tokens/second
- Effective Throughput: 374 tokens/second (temperature-adjusted)
Best Use Cases
Want this applied to YOUR actual stack?
This calculator shows the math for Gemini 3.1 Pro. Your decision needs more — current infrastructure, compliance requirements, actual workload patterns, volume tiers — that change which model is right for you.
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Get my instant AI audit — $39 →✨ Market Recommendations AI Model Registry
← Back to Claude Sonnet 4.6| Rank | AI Model & Provider | Total Cost | vs Claude Sonnet 4.6 | vs Gemini 3.1 Pro |
|---|---|---|---|---|
| 🏆 |
Mistral Small 3
Mistral AI
|
$0.000850 Best Value | ↓ 97% cheaper | ↓ 97.9% cheaper |
| 🥈 |
Gemini 3.1 Flash Lite
Google
|
$0.002500 | ↓ 91.2% cheaper | ↓ 93.8% cheaper |
| 🥉 |
Gemini 3.5 Flash-Lite
Google
|
$0.003350 | ↓ 88.2% cheaper | ↓ 91.6% cheaper |
| #4 |
Gemini 2.5 Flash
Google
|
$0.003350 | ↓ 88.2% cheaper | ↓ 91.6% cheaper |
| #5 |
Mistral Large 3
Mistral AI
|
$0.004250 | ↓ 85.1% cheaper | ↓ 89.4% cheaper |
| #6 |
Gemini 3.8 Flash
Google
|
$0.007125 (rounded ~ $0.01) | ↓ 75% cheaper | ↓ 82.2% cheaper |
| #7 |
GPT-5.4 mini
OpenAI
|
$0.007500 (rounded ~ $0.01) | ↓ 73.7% cheaper | ↓ 81.3% cheaper |
| #8 |
o4-mini Deep Research
OpenAI
|
$0.009000 (rounded ~ $0.01) | ↓ 68.4% cheaper | ↓ 77.5% cheaper |
| #9 |
Claude Haiku 4.5
Anthropic
|
$0.009500 | ↓ 66.7% cheaper | ↓ 76.3% cheaper |
| #10 |
o4-mini
OpenAI
|
$0.009900 | ↓ 65.3% cheaper | ↓ 75.3% cheaper |
| #11 |
Gemini 3.1 Flash
Google
|
$0.010000 | ↓ 64.9% cheaper | ↓ 75% cheaper |
| #12 |
GPT-5.6 Luna
OpenAI
|
$0.010000 | ↓ 64.9% cheaper | ↓ 75% cheaper |
| #13 |
Gemini 3.6 Flash
Google
|
$0.014250 (rounded ~ $0.01) | ↓ 50% cheaper | ↓ 64.4% cheaper |
| #14 |
Gemini 3.5 Flash
Google
|
$0.015000 (rounded ~ $0.02) | ↓ 47.4% cheaper | ↓ 62.5% cheaper |
| #15 |
Claude Sonnet 5
Anthropic
|
$0.019000 (rounded ~ $0.02) | ↓ 33.3% cheaper | ↓ 52.5% cheaper |
| #16 |
GPT-5.3 Codex Spark
OpenAI
|
$0.019250 | ↓ 32.5% cheaper | ↓ 51.9% cheaper |
| #17 |
GPT-5.3 Instant
OpenAI
|
$0.019250 | ↓ 32.5% cheaper | ↓ 51.9% cheaper |
| #18 |
GPT-5.6 Terra
OpenAI
|
$0.025000 (rounded ~ $0.03) | ↓ 12.3% cheaper | ↓ 37.5% cheaper |
| #19 |
Gemini 2.5 Pro
Google
|
$0.027500 (rounded ~ $0.03) | ↓ 3.5% cheaper | ↓ 31.3% cheaper |
| #20 |
Grok 4.3
xAI
|
$0.032000 (rounded ~ $0.03) | ↑ 12.3% more | ↓ 20% cheaper |
| #21 |
Grok 4.20 Beta
xAI
|
$0.032000 (rounded ~ $0.03) | ↑ 12.3% more | ↓ 20% cheaper |
| #22 |
Gemini 3.1 Pro
Google
|
$0.040000 | ↑ 40.4% more | Same price |
| #23 |
Claude Opus 4.7
Anthropic
|
$0.047500 (rounded ~ $0.05) | ↑ 66.7% more | ↑ 18.8% more |
| #24 |
Claude Opus 5
Anthropic
|
$0.047500 (rounded ~ $0.05) | ↑ 66.7% more | ↑ 18.8% more |
| #25 |
Claude Opus 4.8
Anthropic
|
$0.047500 (rounded ~ $0.05) | ↑ 66.7% more | ↑ 18.8% more |
| #26 |
Claude Opus 4.6
Anthropic
|
$0.047500 (rounded ~ $0.05) | ↑ 66.7% more | ↑ 18.8% more |
| #27 |
GPT-5.4
OpenAI
|
$0.050000 | ↑ 75.4% more | ↑ 25% more |
| #28 |
GPT-5.4 Thinking
OpenAI
|
$0.050000 | ↑ 75.4% more | ↑ 25% more |
| #29 |
GPT-5.5 Instant
OpenAI
|
$0.050000 | ↑ 75.4% more | ↑ 25% more |
| #30 |
GPT-5.6 Sol
OpenAI
|
$0.050000 | ↑ 75.4% more | ↑ 25% more |
| #31 |
Claude Fable 5.1
Anthropic
|
$0.080000 | ↑ 180.7% more | ↑ 100% more |
| #32 |
Claude Mythos 5.1
Anthropic
|
$0.080000 | ↑ 180.7% more | ↑ 100% more |
| #33 |
o3 Deep Research
OpenAI
|
$0.090000 | ↑ 215.8% more | ↑ 125% more |
| #34 |
Claude Fable 5
Anthropic
|
$0.095000 (rounded ~ $0.10) | ↑ 233.3% more | ↑ 137.5% more |
| #35 |
Claude Mythos 5
Anthropic
|
$0.095000 (rounded ~ $0.10) | ↑ 233.3% more | ↑ 137.5% more |
| #36 |
GPT-5.5
OpenAI
|
$0.100000 | ↑ 250.9% more | ↑ 150% more |
| #37 |
o3 Pro
OpenAI
|
$0.180000 | ↑ 531.6% more | ↑ 350% more |
| #38 |
GPT-6 Astra
OpenAI
|
$0.190000 | ↑ 566.7% more | ↑ 375% more |
| #39 |
GPT-5.2 Pro
OpenAI
|
$0.231000 | ↑ 710.5% more | ↑ 477.5% more |
| #40 |
GPT-5.2 Pro
OpenAI
|
$0.231000 | ↑ 710.5% more | ↑ 477.5% 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
o4-mini OpenAI
Gemini 3.1 Flash Google
GPT-5.6 Luna OpenAI
Gemini 3.6 Flash Google
Gemini 3.5 Flash Google
Claude Sonnet 5 Anthropic
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
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
Optimizing Enterprise Support Architectures
For high-volume customer support operations handling millions of interactions monthly, selecting the right model depends on the balance between structured reasoning and multimodal retrieval. Claude Sonnet 4.6 is frequently favored for multi-turn conversations where strict adherence to brand voice and complex, multi-step instructions is paramount. Its capability to maintain logical consistency across long threads makes it a staple for automated ticket resolution.
Conversely, Gemini 3.1 Pro provides a significant advantage in pipelines that require deep multimodal integration. If your support workflow involves analyzing user-uploaded documents, screenshots of billing issues, or even video troubleshooting guides, its native multimodal architecture reduces the complexity of downstream orchestration. While Claude excels in pure textual reasoning, Gemini handles diverse data inputs with lower friction.
Decision Factors for Large-Scale Deployments
- Orchestration Complexity: Claude Sonnet 4.6 is often easier to integrate into existing agentic frameworks that rely on specific, deterministic output formats.
- Multimodal RAG: Gemini 3.1 Pro is the preferred choice when your RAG system must index and retrieve information from non-textual assets like PDF manuals or diagnostic imagery.
- System Latency: In 100M+ token monthly environments, evaluating the time-to-first-token is critical. Both models perform competitively, but your choice should align with whether your pipeline is bound by reasoning tasks or information retrieval.
For enterprises managing 100M to 1B tokens monthly, testing both in a canary deployment is essential to measure how each handles your specific RAG retrieval patterns.