Gemini 3.8 Flash Google 1048576
💰 Total Cost Calculation (from Plugin)
Output: $0.007500 (rounded ~ $0.01)
Output: $0.007500 (rounded ~ $0.01)
Unit: $0.000000
Fees: $0.000000
Detailed Cost Analysis (from Plugin)
For 500,000 input tokens and 2,000 output tokens:
- Input Cost: $0.375000 (rounded ~ $0.38)
- Output Cost: $0.007500 (rounded ~ $0.01)
- Total Cost: $0.213750 (rounded ~ $0.21)
- Cost per 1K tokens: $0.000426
- Tokens per dollar: 2,348,538 tokens
- Context Window: 1048576 tokens
Speed & Performance Analysis
With a processing speed of 340 tokens per second and 105ms time to first token:
- Processing Time: 26 minutes, 20.00 seconds
- Latency: 105 milliseconds to first token
- Base Throughput: 340 tokens/second
- Effective Throughput: 318 tokens/second (temperature-adjusted)
Best Use Cases
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💰 Total Cost Calculation (from Plugin)
Output: $0.100000
Output: $0.100000
Unit: $0.000000
Fees: $0.000000
Detailed Cost Analysis (from Plugin)
For 500,000 input tokens and 2,000 output tokens:
- Input Cost: $5.000000
- Output Cost: $0.100000
- Total Cost: $2.662500 (rounded ~ $2.66)
- Cost per 1K tokens: $0.005304 (rounded ~ $0.01)
- Tokens per dollar: 188,545 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 210 tokens per second and 430ms time to first token:
- Processing Time: 42 minutes, 37.99 seconds
- Latency: 430 milliseconds to first token
- Base Throughput: 210 tokens/second
- Effective Throughput: 196 tokens/second (temperature-adjusted)
Best Use Cases
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This calculator shows the math for Claude Fable 5.1. 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 Gemini 3.8 Flash| Rank | AI Model & Provider | Total Cost | vs Gemini 3.8 Flash | vs Claude Fable 5.1 |
|---|---|---|---|---|
| 🏆 |
Gemini 3.1 Flash Lite
Google
|
$0.071750 (rounded ~ $0.07) Best Value | ↓ 66.4% cheaper | ↓ 97.3% cheaper |
| 🥈 |
Gemini 3.5 Flash-Lite
Google
|
$0.087500 (rounded ~ $0.09) | ↓ 59.1% cheaper | ↓ 96.7% cheaper |
| 🥉 |
Gemini 2.5 Flash
Google
|
$0.087500 (rounded ~ $0.09) | ↓ 59.1% cheaper | ↓ 96.7% cheaper |
| #4 |
Gemini 3.1 Flash
Google
|
$0.287000 (rounded ~ $0.29) | ↑ 34.3% more | ↓ 89.2% cheaper |
| #5 |
GPT-5.6 Luna
OpenAI
|
$0.287000 (rounded ~ $0.29) | ↑ 34.3% more | ↓ 89.2% cheaper |
| #6 |
Gemini 3.6 Flash
Google
|
$0.427500 (rounded ~ $0.43) | ↑ 100% more | ↓ 83.9% cheaper |
| #7 |
Gemini 3.5 Flash
Google
|
$0.430500 | ↑ 101.4% more | ↓ 83.8% cheaper |
| #8 |
Claude Sonnet 5
Anthropic
|
$0.570000 | ↑ 166.7% more | ↓ 78.6% cheaper |
| #9 |
Grok 4.3
xAI
|
$0.697500 (rounded ~ $0.70) | ↑ 226.3% more | ↓ 73.8% cheaper |
| #10 |
Grok 4.20 Beta
xAI
|
$0.697500 (rounded ~ $0.70) | ↑ 226.3% more | ↓ 73.8% cheaper |
| #11 |
Gemini 2.5 Pro
Google
|
$0.717500 (rounded ~ $0.72) | ↑ 235.7% more | ↓ 73.1% cheaper |
| #12 |
GPT-5.6 Terra
OpenAI
|
$0.717500 (rounded ~ $0.72) | ↑ 235.7% more | ↓ 73.1% cheaper |
| #13 |
Claude Sonnet 4.6
Anthropic
|
$0.855000 (rounded ~ $0.86) | ↑ 300% more | ↓ 67.9% cheaper |
| #14 |
Gemini 3.1 Pro
Google
|
$1.136000 (rounded ~ $1.14) | ↑ 431.5% more | ↓ 57.3% cheaper |
| #15 |
GPT-5.4
OpenAI
|
$1.420000 | ↑ 564.3% more | ↓ 46.7% cheaper |
| #16 |
GPT-5.4 Thinking
OpenAI
|
$1.420000 | ↑ 564.3% more | ↓ 46.7% cheaper |
| #17 |
Claude Opus 4.7
Anthropic
|
$1.425000 (rounded ~ $1.43) | ↑ 566.7% more | ↓ 46.5% cheaper |
| #18 |
Claude Opus 5
Anthropic
|
$1.425000 (rounded ~ $1.43) | ↑ 566.7% more | ↓ 46.5% cheaper |
| #19 |
Claude Opus 4.8
Anthropic
|
$1.425000 (rounded ~ $1.43) | ↑ 566.7% more | ↓ 46.5% cheaper |
| #20 |
Claude Opus 4.6
Anthropic
|
$1.425000 (rounded ~ $1.43) | ↑ 566.7% more | ↓ 46.5% cheaper |
| #21 |
GPT-5.6 Sol
OpenAI
|
$1.435000 (rounded ~ $1.44) | ↑ 571.3% more | ↓ 46.1% cheaper |
| #22 |
Claude Fable 5.1
Anthropic
|
$2.662500 (rounded ~ $2.66) | ↑ 1145.6% more | Same price |
| #23 |
Claude Mythos 5.1
Anthropic
|
$2.662500 (rounded ~ $2.66) | ↑ 1145.6% more | Same price |
| #24 |
GPT-5.5
OpenAI
|
$2.840000 | ↑ 1228.7% more | ↑ 6.7% more |
| #25 |
Claude Fable 5
Anthropic
|
$2.850000 | ↑ 1233.3% more | ↑ 7% more |
| #26 |
Claude Mythos 5
Anthropic
|
$2.850000 | ↑ 1233.3% more | ↑ 7% more |
| #27 |
GPT-6 Astra
OpenAI
|
$5.700000 | ↑ 2566.7% more | ↑ 114.1% more |
| #28 |
GPT-6 Astra
OpenAI
|
$5.700000 | ↑ 2566.7% more | ↑ 114.1% more |
Gemini 3.1 Flash Lite Google
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Gemini 3.1 Flash Google
GPT-5.6 Luna OpenAI
Gemini 3.6 Flash Google
Gemini 3.5 Flash Google
Claude Sonnet 5 Anthropic
Grok 4.3 xAI
Grok 4.20 Beta xAI
Gemini 2.5 Pro Google
GPT-5.6 Terra OpenAI
Claude Sonnet 4.6 Anthropic
Gemini 3.1 Pro Google
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
Claude Opus 4.7 Anthropic
Claude Opus 5 Anthropic
Claude Opus 4.8 Anthropic
Claude Opus 4.6 Anthropic
GPT-5.6 Sol OpenAI
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
GPT-5.5 OpenAI
Claude Fable 5 Anthropic
Claude Mythos 5 Anthropic
GPT-6 Astra OpenAI
GPT-6 Astra OpenAI
Optimizing Agentic Workflows for Legal Research
Legal tech teams managing autonomous browser agents for tasks like due diligence or e-discovery require models that can reliably handle tool calling alongside complex document analysis. When processing 500,000 tokens per agentic session, selecting the right model often comes down to balancing raw throughput and reasoning reliability.
Gemini 3.8 Flash stands out for agentic browser automation where speed and multi-modal integration are critical. Its ability to navigate live web environments and rapidly synthesize visual information makes it a strong candidate for broad research queries where the agent must evaluate multiple page elements simultaneously. The model is optimized for high-volume tasks, allowing for efficient execution of 20-50 tool calls without significant latency bottlenecks.
Conversely, Claude Fable 5.1 excels in scenarios requiring deeper, nuanced reasoning. In legal contexts, an agent must often distinguish between relevant and irrelevant information across dense, regulatory-heavy websites. If your browser automation pipeline prioritizes high-accuracy extraction or requires complex multi-step decision-making where logic errors are costly, this model provides a more robust reasoning architecture.
For engineering teams, the choice rests on the specific nature of your browser agents. If the workload is primarily data retrieval and summarization, leverage the performance efficiencies of Gemini. If the objective is precise, high-stakes analysis where the agent must perform sophisticated logic checks on every web-sourced finding, prioritize the reasoning depth of the Claude family. Both models are capable of managing long-context inputs, ensuring consistent performance during protracted research sessions.