Claude Sonnet 4.6 Anthropic 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.003000
Output: $0.003000
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 50,000 input tokens and 800 output tokens:
- Input Cost: $0.037500 (rounded ~ $0.04)
- Output Cost: $0.003000
- Total Cost: $0.020250
- Cost per 1K tokens: $0.000399
- Tokens per dollar: 2,508,642 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: 2 minutes, 0.97 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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💰 Total Cost Calculation (from Plugin)
Output: $0.004800
Output: $0.004800
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 50,000 input tokens and 800 output tokens:
- Input Cost: $0.050000
- Output Cost: $0.004800
- Total Cost: $0.027800 (rounded ~ $0.03)
- Cost per 1K tokens: $0.000547
- Tokens per dollar: 1,827,338 tokens
- Context Window: 2000000 tokens
Speed & Performance Analysis
With a processing speed of 400 tokens per second and 220ms time to first token:
- Processing Time: 2 minutes, 16.07 seconds
- Latency: 220 milliseconds to first token
- Base Throughput: 400 tokens/second
- Effective Throughput: 374 tokens/second (temperature-adjusted)
Best Use Cases
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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
|
$0.000635 Best Value | ↓ 96.9% cheaper | ↓ 97.7% cheaper |
| 🥈 |
Grok Code Fast 1
xAI
|
$0.001450 | ↓ 92.8% cheaper | ↓ 94.8% cheaper |
| 🥉 |
Gemini 3.1 Flash Lite
Google
|
$0.001738 | ↓ 91.4% cheaper | ↓ 93.8% cheaper |
| #4 |
Gemini 2.5 Flash
Google
|
$0.002225 | ↓ 89% cheaper | ↓ 92% cheaper |
| #5 |
Mistral Large 3
Mistral AI
|
$0.003175 | ↓ 84.3% cheaper | ↓ 88.6% cheaper |
| #6 |
GPT-5.4 mini
OpenAI
|
$0.005213 (rounded ~ $0.01) | ↓ 74.3% cheaper | ↓ 81.3% cheaper |
| #7 |
o4-mini Deep Research
OpenAI
|
$0.006550 (rounded ~ $0.01) | ↓ 67.7% cheaper | ↓ 76.4% cheaper |
| #8 |
Claude Haiku 4.5
Anthropic
|
$0.006750 (rounded ~ $0.01) | ↓ 66.7% cheaper | ↓ 75.7% cheaper |
| #9 |
Gemini 3.1 Flash
Google
|
$0.006950 (rounded ~ $0.01) | ↓ 65.7% cheaper | ↓ 75% cheaper |
| #10 |
o4-mini
OpenAI
|
$0.007205 (rounded ~ $0.01) | ↓ 64.4% cheaper | ↓ 74.1% cheaper |
| #11 |
Grok 4.3
xAI
|
$0.007688 (rounded ~ $0.01) | ↓ 62% cheaper | ↓ 72.3% cheaper |
| #12 |
Gemini 3.5 Flash
Google
|
$0.010425 | ↓ 48.5% cheaper | ↓ 62.5% cheaper |
| #13 |
Grok 4.20 Beta
xAI
|
$0.012700 (rounded ~ $0.01) | ↓ 37.3% cheaper | ↓ 54.3% cheaper |
| #14 |
GPT-5.3 Codex Spark
OpenAI
|
$0.012863 (rounded ~ $0.01) | ↓ 36.5% cheaper | ↓ 53.7% cheaper |
| #15 |
GPT-5.3 Instant
OpenAI
|
$0.012863 (rounded ~ $0.01) | ↓ 36.5% cheaper | ↓ 53.7% cheaper |
| #16 |
Gemini 2.5 Pro
Google
|
$0.018375 (rounded ~ $0.02) | ↓ 9.3% cheaper | ↓ 33.9% cheaper |
| #17 |
Gemini 3.1 Pro
Google
|
$0.027800 (rounded ~ $0.03) | ↑ 37.3% more | Same price |
| #18 |
Claude Opus 4.7
Anthropic
|
$0.033750 (rounded ~ $0.03) | ↑ 66.7% more | ↑ 21.4% more |
| #19 |
Claude Opus 4.8
Anthropic
|
$0.033750 (rounded ~ $0.03) | ↑ 66.7% more | ↑ 21.4% more |
| #20 |
Claude Opus 4.6
Anthropic
|
$0.033750 (rounded ~ $0.03) | ↑ 66.7% more | ↑ 21.4% more |
| #21 |
GPT-5.4
OpenAI
|
$0.034750 (rounded ~ $0.03) | ↑ 71.6% more | ↑ 25% more |
| #22 |
GPT-5.4 Thinking
OpenAI
|
$0.034750 (rounded ~ $0.03) | ↑ 71.6% more | ↑ 25% more |
| #23 |
GPT-5.5 Instant
OpenAI
|
$0.034750 (rounded ~ $0.03) | ↑ 71.6% more | ↑ 25% more |
| #24 |
o3 Deep Research
OpenAI
|
$0.065500 (rounded ~ $0.07) | ↑ 223.5% more | ↑ 135.6% more |
| #25 |
GPT-5.5
OpenAI
|
$0.069500 | ↑ 243.2% more | ↑ 150% more |
| #26 |
o3 Pro
OpenAI
|
$0.131000 (rounded ~ $0.13) | ↑ 546.9% more | ↑ 371.2% more |
| #27 |
GPT-5.2 Pro
OpenAI
|
$0.154350 (rounded ~ $0.15) | ↑ 662.2% more | ↑ 455.2% more |
| #28 |
GPT-5.2 Pro
OpenAI
|
$0.154350 (rounded ~ $0.15) | ↑ 662.2% more | ↑ 455.2% more |
Mistral Small 3 Mistral AI
Grok Code Fast 1 xAI
Gemini 3.1 Flash Lite Google
Gemini 2.5 Flash Google
Mistral Large 3 Mistral AI
GPT-5.4 mini OpenAI
o4-mini Deep Research OpenAI
Claude Haiku 4.5 Anthropic
Gemini 3.1 Flash Google
o4-mini OpenAI
Grok 4.3 xAI
Gemini 3.5 Flash Google
Grok 4.20 Beta xAI
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
Gemini 2.5 Pro Google
Gemini 3.1 Pro Google
Claude Opus 4.7 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
o3 Deep Research OpenAI
GPT-5.5 OpenAI
o3 Pro OpenAI
GPT-5.2 Pro OpenAI
GPT-5.2 Pro OpenAI
As multi-agent systems move from experimental pilots to production, engineers must decide between Claude Sonnet 4.6 and Gemini 3.1 Pro for high-throughput orchestration. Claude Sonnet 4.6 is widely favored for agentic workflows involving complex tool use and reliable instruction following, making it a robust default for implementor agents within a pipeline. Developers often select Sonnet 4.6 when the priority is consistent, high-quality output that minimizes hallucination in multi-step chains. Conversely, Gemini 3.1 Pro stands out for workflows requiring massive context handling or native multimodal ingestion. Its efficiency in large-scale context retrieval makes it a powerful choice for agents that need to process extensive documentation, call logs, or long-running conversation histories in a single pass. For voice teams, Gemini’s ability to process audio and video inputs natively provides a latency advantage by avoiding complex transcoding pipelines. However, teams building modular orchestrators that rely heavily on ‘agent-to-agent’ chatter often find Sonnet’s latency consistency and structured output more predictable for integration. When designing for 50 million tokens, the choice often hinges on whether your agents act more as ‘analysts’ (favoring Gemini’s context) or ‘executors’ (favoring Sonnet’s tool-calling precision). Both models support the 1M-token context windows essential for maintaining long-term agent memory without frequent re-summarization.