Claude Opus 5 Anthropic 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.031250 (rounded ~ $0.03)
Output: $0.031250 (rounded ~ $0.03)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 5,000 output tokens:
- Input Cost: $1.250000
- Output Cost: $0.031250 (rounded ~ $0.03)
- Total Cost: $0.718750 (rounded ~ $0.72)
- Cost per 1K tokens: $0.000715
- Tokens per dollar: 1,398,261 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: 59 minutes, 44.68 seconds
- Latency: 300 milliseconds to first token
- Base Throughput: 300 tokens/second
- Effective Throughput: 280 tokens/second (temperature-adjusted)
Best Use Cases
Want this applied to YOUR actual stack?
This calculator shows the math for Claude Opus 5. Your decision needs more — current infrastructure, compliance requirements, actual workload patterns, volume tiers — that change which model is right for you.
Get a $39 personalized AI Architecture Audit. PDF tailored to your stack, delivered in under 60 seconds. 7-day no-questions-asked refund.
Get my instant AI audit — $39 →Gemini 3.1 Pro Google 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.045000 (rounded ~ $0.05)
Output: $0.045000 (rounded ~ $0.05)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 5,000 output tokens:
- Input Cost: $2.000000
- Output Cost: $0.045000 (rounded ~ $0.05)
- Total Cost: $1.145000 (rounded ~ $1.15)
- Cost per 1K tokens: $0.001139
- Tokens per dollar: 877,729 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: 44 minutes, 48.56 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.
Get a $39 personalized AI Architecture Audit. PDF tailored to your stack, delivered in under 60 seconds. 7-day no-questions-asked refund.
Get my instant AI audit — $39 →✨ Market Recommendations AI Model Registry
← Back to Claude Opus 5| Rank | AI Model & Provider | Total Cost | vs Claude Opus 5 | vs Gemini 3.1 Pro |
|---|---|---|---|---|
| 🏆 |
Gemini 3.5 Flash-Lite
Google
|
$0.044375 (rounded ~ $0.04) Best Value | ↓ 93.8% cheaper | ↓ 96.1% cheaper |
| 🥈 |
Gemini 3.8 Flash
Google
|
$0.107813 (rounded ~ $0.11) | ↓ 85% cheaper | ↓ 90.6% cheaper |
| 🥉 |
Gemini 3.6 Flash
Google
|
$0.215625 (rounded ~ $0.22) | ↓ 70% cheaper | ↓ 81.2% cheaper |
| #4 |
Gemini 2.5 Pro
Google
|
$0.725000 (rounded ~ $0.73) | ↑ 0.9% more | ↓ 36.7% cheaper |
| #5 |
GPT-5.4
OpenAI
|
$1.431250 (rounded ~ $1.43) | ↑ 99.1% more | ↑ 25% more |
| #6 |
GPT-5.4 Thinking
OpenAI
|
$1.431250 (rounded ~ $1.43) | ↑ 99.1% more | ↑ 25% more |
| #7 |
GPT-6 Astra
OpenAI
|
$5.750000 | ↑ 700% more | ↑ 402.2% more |
| #8 |
GPT-6 Astra
OpenAI
|
$5.750000 | ↑ 700% more | ↑ 402.2% more |
Gemini 3.5 Flash-Lite Google
Gemini 3.8 Flash Google
Gemini 3.6 Flash Google
Gemini 2.5 Pro Google
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
GPT-6 Astra OpenAI
GPT-6 Astra OpenAI
For research teams handling high-volume literature reviews and drafting, the choice between Claude Opus 5 and Gemini 3.1 Pro centers on architectural intent. Claude Opus 5 excels in long-horizon reasoning and complex agentic tasks where maintaining a coherent, multi-step chain of thought is critical. Its recent performance on deep-reasoning evaluations makes it the stronger choice for synthesizing disparate research findings into a cohesive draft. The model remains highly effective for tasks requiring strict adherence to complex instructions.
Conversely, Gemini 3.1 Pro provides a significant advantage for teams embedded in the Google ecosystem or those requiring native multimodal ingestion. Its ability to process large-scale inputs—often involving numerous PDFs, audio recordings, or video documentation—is unparalleled due to its refined context mechanics. If your research pipeline involves extracting data from multimodal sources or requires the model to pull context directly from Drive or Workspace files, the Gemini integration provides a smoother operational flow.
Decision factors beyond initial capability should include your current infrastructure. Claude Opus 5 thrives in environments where agentic coding and complex, autonomous research agents are prioritized. Gemini 3.1 Pro is the superior choice for high-volume, enterprise-scale data synthesis where multimodal retrieval and native tool use within an existing productivity stack are the primary bottlenecks. Both models support high-effort reasoning modes that can be tuned to balance output depth against compute expenditure.