Gemini 3.1 Pro Google 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.090000
Output: $0.090000
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 50,000 input tokens and 15,000 output tokens:
- Input Cost: $0.050000
- Output Cost: $0.090000
- Total Cost: $0.117500 (rounded ~ $0.12)
- Cost per 1K tokens: $0.001808
- Tokens per dollar: 553,191 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: 2 minutes, 54.06 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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← Back to Gemini 3.1 Pro| Rank | AI Model & Provider | Total Cost | vs Gemini 3.1 Pro |
|---|---|---|---|
| 🏆 |
Claude Haiku 5.5
Anthropic
|
$0.005125 (rounded ~ $0.01) Best Value | ↓ 95.6% cheaper |
| 🥈 |
GPT-6 Luna
OpenAI
|
$0.005125 (rounded ~ $0.01) | ↓ 95.6% cheaper |
| 🥉 |
GPT-5.6 Luna
OpenAI
|
$0.005875 (rounded ~ $0.01) | ↓ 95% cheaper |
| #4 |
Gemini 3.1 Flash Lite
Google
|
$0.007344 (rounded ~ $0.01) | ↓ 93.8% cheaper |
| #5 |
Mistral Large 3
Mistral AI
|
$0.009063 | ↓ 92.3% cheaper |
| #6 |
Gemini 3.5 Flash-Lite
Google
|
$0.011438 (rounded ~ $0.01) | ↓ 90.3% cheaper |
| #7 |
Gemini 2.5 Flash
Google
|
$0.011438 (rounded ~ $0.01) | ↓ 90.3% cheaper |
| #8 |
Gemini 3.8 Flash
Google
|
$0.019219 | ↓ 83.6% cheaper |
| #9 |
Gemini 3.6 Flash
Google
|
$0.019219 | ↓ 83.6% cheaper |
| #10 |
GPT-5.4 mini
OpenAI
|
$0.022031 (rounded ~ $0.02) | ↓ 81.3% cheaper |
| #11 |
Mistral Large 4
Mistral AI
|
$0.025025 (rounded ~ $0.03) | ↓ 78.7% cheaper |
| #12 |
Claude Haiku 4.5
Anthropic
|
$0.025625 (rounded ~ $0.03) | ↓ 78.2% cheaper |
| #13 |
Gemini 3.1 Flash
Google
|
$0.029375 | ↓ 75% cheaper |
| #14 |
Claude Sonnet 5.5
Anthropic
|
$0.050625 | ↓ 56.9% cheaper |
| #15 |
Gemini 4 Argon
Google
|
$0.051250 (rounded ~ $0.05) | ↓ 56.4% cheaper |
| #16 |
Claude Sonnet 5
Anthropic
|
$0.051250 (rounded ~ $0.05) | ↓ 56.4% cheaper |
| #17 |
Grok 4.3
xAI
|
$0.057500 (rounded ~ $0.06) | ↓ 51.1% cheaper |
| #18 |
Grok 4.20 Beta
xAI
|
$0.057500 (rounded ~ $0.06) | ↓ 51.1% cheaper |
| #19 |
GPT-5.6 Terra
OpenAI
|
$0.058750 (rounded ~ $0.06) | ↓ 50% cheaper |
| #20 |
GPT-5.3 Codex Spark
OpenAI
|
$0.064531 (rounded ~ $0.06) | ↓ 45.1% cheaper |
| #21 |
GPT-5.3 Instant
OpenAI
|
$0.064531 (rounded ~ $0.06) | ↓ 45.1% cheaper |
| #22 |
Claude Sonnet 4.6
Anthropic
|
$0.076875 (rounded ~ $0.08) | ↓ 34.6% cheaper |
| #23 |
Gemini 2.5 Pro
Google
|
$0.092188 (rounded ~ $0.09) | ↓ 21.5% cheaper |
| #24 |
Claude Opus 5.5
Anthropic
|
$0.101250 (rounded ~ $0.10) | ↓ 13.8% cheaper |
| #25 |
GPT-6.1 Sol
OpenAI
|
$0.102500 (rounded ~ $0.10) | ↓ 12.8% cheaper |
| #26 |
GPT-6 Sol
OpenAI
|
$0.102500 (rounded ~ $0.10) | ↓ 12.8% cheaper |
| #27 |
GPT-5.6 Sol
OpenAI
|
$0.102500 (rounded ~ $0.10) | ↓ 12.8% cheaper |
| #28 |
Claude Opus 4.7
Anthropic
|
$0.128125 (rounded ~ $0.13) | ↑ 9% more |
| #29 |
Claude Opus 5
Anthropic
|
$0.128125 (rounded ~ $0.13) | ↑ 9% more |
| #30 |
Claude Opus 4.8
Anthropic
|
$0.128125 (rounded ~ $0.13) | ↑ 9% more |
| #31 |
Claude Opus 4.6
Anthropic
|
$0.128125 (rounded ~ $0.13) | ↑ 9% more |
| #32 |
GPT-5.4
OpenAI
|
$0.146875 (rounded ~ $0.15) | ↑ 25% more |
| #33 |
GPT-5.4 Thinking
OpenAI
|
$0.146875 (rounded ~ $0.15) | ↑ 25% more |
| #34 |
GPT-5.5 Instant
OpenAI
|
$0.146875 (rounded ~ $0.15) | ↑ 25% more |
| #35 |
Claude Fable 5.1
Anthropic
|
$0.251563 (rounded ~ $0.25) | ↑ 114.1% more |
| #36 |
Claude Mythos 5.1
Anthropic
|
$0.251563 (rounded ~ $0.25) | ↑ 114.1% more |
| #37 |
Claude Fable 5
Anthropic
|
$0.256250 (rounded ~ $0.26) | ↑ 118.1% more |
| #38 |
Claude Mythos 5
Anthropic
|
$0.256250 (rounded ~ $0.26) | ↑ 118.1% more |
| #39 |
GPT-5.5
OpenAI
|
$0.293750 (rounded ~ $0.29) | ↑ 150% more |
| #40 |
GPT-6 Astra
OpenAI
|
$0.512500 (rounded ~ $0.51) | ↑ 336.2% more |
| #41 |
GPT-5.2 Pro
OpenAI
|
$0.774375 (rounded ~ $0.77) | ↑ 559% more |
| #42 |
GPT-5.2 Pro
OpenAI
|
$0.774375 (rounded ~ $0.77) | ↑ 559% more |
Claude Haiku 5.5 Anthropic
GPT-6 Luna OpenAI
GPT-5.6 Luna OpenAI
Gemini 3.1 Flash Lite Google
Mistral Large 3 Mistral AI
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Gemini 3.8 Flash Google
Gemini 3.6 Flash Google
GPT-5.4 mini OpenAI
Mistral Large 4 Mistral AI
Claude Haiku 4.5 Anthropic
Gemini 3.1 Flash Google
Claude Sonnet 5.5 Anthropic
Gemini 4 Argon Google
Claude Sonnet 5 Anthropic
Grok 4.3 xAI
Grok 4.20 Beta xAI
GPT-5.6 Terra OpenAI
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
Claude Sonnet 4.6 Anthropic
Gemini 2.5 Pro Google
Claude Opus 5.5 Anthropic
GPT-6.1 Sol OpenAI
GPT-6 Sol OpenAI
GPT-5.6 Sol OpenAI
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
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
Claude Fable 5 Anthropic
Claude Mythos 5 Anthropic
GPT-5.5 OpenAI
GPT-6 Astra OpenAI
GPT-5.2 Pro OpenAI
GPT-5.2 Pro OpenAI
Conducting a comprehensive literature review typically involves juggling dozens of PDFs, dense research papers, and scattered notes. Gemini 3.1 Pro distinguishes itself in this environment through its massive context window and native multimodal support, which allows you to ingest entire research corpora in a single pass. Unlike models that require you to break your data into small, disconnected chunks—potentially losing the thread of an argument—Gemini 3.1 Pro maintains a coherent view across hundreds of thousands of tokens. This is invaluable when you need the model to cross-reference findings between different papers or identify trends across a wide range of academic sources. Beyond raw capacity, its reasoning capabilities are highly effective at synthesizing disparate data points into a cohesive summary. For research producers, this means you can provide the model with all your background material at once and ask for high-level synthesis, comparative analysis, or identifying gaps in current literature. Because it handles both text and visual inputs—such as complex charts or scientific diagrams—you can also include visual data directly in your review. This approach minimizes the ‘fragmentation tax’ often associated with research workflows, letting you focus on the creative synthesis of ideas rather than the mechanics of data management and manual file stitching.