Cost per 1M Tokens: GPT-5.5 vs Gemini 3.1 Pro for Literature Reviews

GPT-5.5 vs Gemini 3.1 Pro
Complete Comparison: 1,000,000 input tokens × 10,000 output tokens
Comparison Mode
⚡ 50% Cached

Complete comparison of pricing, performance, and capabilities for 2 leading AI models with 50% Cached.

⚡ Caching Optimized (up to 90% savings) 📊 Batch API
Comparison Criteria GPT-5.5
OpenAI
Gemini 3.1 Pro
Google
Calculation Results (Current Inputs) (50% cached)
Input Tokens 1,000,000 1,000,000
Output Tokens 10,000 10,000
Cost Breakdown
Input Cost $5.000000Worst $2.000000Best
Output Cost $0.225000 (rounded ~ $0.23)Worst $0.090000Best
Unit Cost (Audio/OCR) $0.000000 $0.000000
Service Fees $0.000000 $0.000000
Total Cost $2.975000 (rounded ~ $2.98) Most Expensive $1.190000 Best Value
Processing Time 42 minutes, 53.28 seconds Fastest 45 minutes, 1.93 seconds Slowest
Tokens per Second 420Fastest 400Slowest
Time to First Token 210ms Best 220ms Worst
Cost per 1K tokens $0.002946Worst $0.001178Best
Tokens per Dollar 339,496Worst Value 848,739Best Value
Cost per 1 Million Tokens (Informational)
Input Cost / 1M (Base) $5.000000Worst $2.000000Best
Output Cost / 1M (Base) $22.500000Worst $9.000000Best
Input Cost / 1M (Optimized) $2.500000Worst
Optimizations: 50.0% batch
$1.000000Best
Optimizations: 50.0% batch
Output Cost / 1M (Optimized) $11.250000Worst
Optimizations: 50.0% batch
$4.500000Best
Optimizations: 50.0% batch
Capabilities & Advanced Features
Images Support ✓ Supported ✓ Supported
Video Support ✗ Not Supported ✓ Supported
Audio Support ✗ Not Supported ✓ Supported
Caching Support
50
✓ Supported ✓ Supported
Batch API Support ✓ Supported ✓ Supported
Tool Usage Support ✓ Supported ✓ Supported
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ℹ️ Bulk Calculation: Total volume exceeds single-request limit of 1,000,000 tokens. Budgeting mode active.

Select AI Model

GPT-5.5
OpenAIMax Context: 1,000,000 tokens
$5 / $30 per 1M tokens (Standard)
State-dependent pricing active. Current tier: Standard
▼
▼
Use Batch API (50% discount)
50%
Provider-specific multipliers applied after all calculations
Enable for cache discounts
Select platform to enforce context limits
Number of requests (max 1M). Summary view auto-enabled >10k.

Calculate Token Costs

$2.975000 Total Cost
$2.500000 Input Cost
$0.225000 Output Cost
$0.000000 Unit Cost
$0.000000 Search Cost
$0.000000 Request Fee
$0.000000 Tool Fee
$0.000000 Code Execution
1,010,000Total Tokens
$0.002946Cost per 1K
339,496Tokens per $
🔄 Cliff Pricing Active: Using Premium pricing (premium) based on token volume.
📊 Advanced Cost Breakdown

Processing Speed

42m 53s Processing Time
420 Tokens/Second
210ms Time to First Token
393 Effective Speed

Model Comparison

Select a model to see comparisons with competitors.

Model Information

Select a model to see detailed information.

🔄 Advanced Options

⚡ Optimization
Flat fee per session (e.g., $0.03 for Code Interpreter)
Hourly storage fee for cached data
First 50 hours free, $0.05/hour after
🧠 Reasoning & Thinking
Manual thinking tokens (billed at output rate)
🔧 Special Modes
Enable 6.0x Fast Mode multiplier
📚 Research & Citations
Enable $1.00/$4.00 rates + $10.00/1k search
Enable research tier pricing
Fee per source cited
🎤 Realtime Audio & Video
Session length for billing
📊 Multiple Models Detected: This page contains data for 2 models. See the detailed comparison table above, and switch between models using tabs below.

GPT-5.5 OpenAI 1000000 🏔️ Context Cliff

$2.975000 (rounded ~ $2.98)
Total Cost
⚠️ Bulk Calculation: Total volume exceeds single-request limit of 1,000,000 tokens. Budgeting mode active.
⚡ 50% Cached 📊 Batch API 🔧 Tools
👁️
Vision/Images
✓ Available
🎧
Audio Processing
✗ Not Available
🎥
Video Analysis
✗ Not Available
🔧
Tool Usage
✓ Available
📄
OCR Support
✗ Not Available
📊
Batch API
✓ Available
⚡
Caching
✓ Available
90% savings

💰 Total Cost Calculation (from Plugin)

Base Cost (No Optimizations) $5.225000 (rounded ~ $5.23) Input: $5.000000
Output: $0.225000 (rounded ~ $0.23)
Optimized Cost $2.975000 (rounded ~ $2.98) Input: $5.000000
Output: $0.225000 (rounded ~ $0.23)
Unit: $0.000000
Fees: $0.000000
Total Savings $2.250000 43.1% discount

Advanced Cost Breakdown (from Plugin)

📊 Batch API
50.0% off
Asynchronous processing discount
🏔️ Context Cliff
Premium Tier
>272,000 tokens triggered premium pricing
📊 Cliff Pricing
Premium
premium pricing (threshold: 272,000)

Detailed Cost Analysis (from Plugin)

For 1,000,000 input tokens and 10,000 output tokens:

  • Input Cost: $5.000000
  • Output Cost: $0.225000 (rounded ~ $0.23)
  • Total Cost: $2.975000 (rounded ~ $2.98)
  • Cost per 1K tokens: $0.002946
  • Tokens per dollar: 339,496 tokens
  • Context Window: 1000000 tokens

Speed & Performance Analysis

With a processing speed of 420 tokens per second and 210ms time to first token:

  • Processing Time: 42 minutes, 53.28 seconds
  • Latency: 210 milliseconds to first token
  • Base Throughput: 420 tokens/second
  • Effective Throughput: 393 tokens/second (temperature-adjusted)

Best Use Cases

Choose GPT-5.5 for agentic research tasks and rigorous citation verification; use Gemini 3.1 Pro for massive-context ingestion and multimodal data retrieval.

Want this applied to YOUR actual stack?

This calculator shows the math for GPT-5.5. 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 2000000

$1.190000
Total Cost
⚡ 50% Cached 📊 Batch API 🔧 Tools
👁️
Vision/Images
✓ Available
🎧
Audio Processing
✓ Available
🎥
Video Analysis
✓ Available
🔧
Tool Usage
✓ Available
📄
OCR Support
✗ Not Available
📊
Batch API
✓ Available
⚡
Caching
✓ Available
90% savings

💰 Total Cost Calculation (from Plugin)

Base Cost (No Optimizations) $2.090000 Input: $2.000000
Output: $0.090000
Optimized Cost $1.190000 Input: $2.000000
Output: $0.090000
Unit: $0.000000
Fees: $0.000000
Total Savings $0.900000 43.1% discount

Advanced Cost Breakdown (from Plugin)

📊 Batch API
50.0% off
Asynchronous processing discount
📊 Dynamic Tier
Premium
tier2 pricing based on 0 tokens

Detailed Cost Analysis (from Plugin)

For 1,000,000 input tokens and 10,000 output tokens:

  • Input Cost: $2.000000
  • Output Cost: $0.090000
  • Total Cost: $1.190000
  • Cost per 1K tokens: $0.001178
  • Tokens per dollar: 848,739 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: 45 minutes, 1.93 seconds
  • Latency: 220 milliseconds to first token
  • Base Throughput: 400 tokens/second
  • Effective Throughput: 374 tokens/second (temperature-adjusted)

Best Use Cases

Choose GPT-5.5 for agentic research tasks and rigorous citation verification; use Gemini 3.1 Pro for massive-context ingestion and multimodal data retrieval.

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 GPT-5.5
📋 Active Input Parameters
Input Tokens: 1,000,000
Output Tokens: 10,000
Batch API: Enabled (50% discount)
Cached Tokens: 50%
Tools: Enabled
Rank AI Model & Provider Total Cost vs GPT-5.5 vs Gemini 3.1 Pro
🏆 Grok 4.20 Beta
xAI
 $0.290000 Best Value ↓ 90.3% cheaper ↓ 75.6% cheaper
🥈 Gemini 2.5 Pro
Google
 $0.762500 (rounded ~ $0.76) ↓ 74.4% cheaper ↓ 35.9% cheaper
🥉 Gemini 3.1 Pro
Google
 $1.190000 ↓ 60% cheaper Same price
#4 GPT-5.4
OpenAI
 $1.487500 (rounded ~ $1.49) ↓ 50% cheaper ↑ 25% more
#5 GPT-5.4 Thinking
OpenAI
 $1.487500 (rounded ~ $1.49) ↓ 50% cheaper ↑ 25% more
#6 GPT-5.4 Thinking
OpenAI
 $1.487500 (rounded ~ $1.49) ↓ 50% cheaper ↑ 25% more
🏆

Grok 4.20 Beta
xAI

$0.290000
vs GPT-5.5: ↓ 90.3%
vs Gemini 3.1 Pro: ↓ 75.6%
🥈

Gemini 2.5 Pro
Google

$0.762500 (rounded ~ $0.76)
vs GPT-5.5: ↓ 74.4%
vs Gemini 3.1 Pro: ↓ 35.9%
🥉

Gemini 3.1 Pro
Google

$1.190000
vs GPT-5.5: ↓ 60%
vs Gemini 3.1 Pro: Same
#4

GPT-5.4
OpenAI

$1.487500 (rounded ~ $1.49)
vs GPT-5.5: ↓ 50%
vs Gemini 3.1 Pro: ↑ 25%
#5

GPT-5.4 Thinking
OpenAI

$1.487500 (rounded ~ $1.49)
vs GPT-5.5: ↓ 50%
vs Gemini 3.1 Pro: ↑ 25%
#6

GPT-5.4 Thinking
OpenAI

$1.487500 (rounded ~ $1.49)
vs GPT-5.5: ↓ 50%
vs Gemini 3.1 Pro: ↑ 25%
✨ How recommendations work (v8.6.0): We scan all active models in the registry and only include those that support ALL your current inputs. For token-based models, we check if they can handle your token counts. For special pricing models (OCR, video, audio), we verify they have the correct pricing structure. Features marked requested were in your inputs but not supported by that model. Now using official provider pricing without reseller markups.

Deep Context Management for Large-Scale Lit Reviews

In the domain of systematic literature reviews, the ability to maintain coherence across massive volumes of source material is critical for hypothesis generation and citation accuracy. Comparing GPT-5.5 and Gemini 3.1 Pro reveals distinct architectural approaches to long-context reasoning. GPT-5.5 is designed for high-stakes agency and complex multi-step reasoning, making it particularly adept at identifying subtle contradictions across a 1-million-token dataset. Its advanced thinking capabilities allow researchers to perform deep synthesis that moves beyond simple summarization into active knowledge discovery and trend forecasting.

Gemini 3.1 Pro, however, offers a significantly larger context window that simplifies the processing of extremely large document sets in a single pass. This reduces the architectural complexity for research teams by allowing them to fit entire sub-fields of study into the model’s active memory without relying on fragmented retrieval systems. While GPT-5.5 often provides more rigorous logical verification, Gemini excels at multimodal retrieval, such as finding specific data points hidden within both text and embedded graphics. The decision between these models often rests on whether the researcher values the sophisticated reasoning and agentic workflows of OpenAI or the expansive context and multimodal integration of Google. Both models support the high-volume throughput required for processing 1M tokens of academic text in a production environment.

Frequently Asked Questions

How accurate are these AI model cost calculations?
Our calculations are based on official pricing from each provider (Google, OpenAI, Anthropic, Meta, xAI, Perplexity, DeepSeek, Mistral) and are updated regularly. We account for all factors including multimodal inputs, caching discounts, batch API pricing, tool usage multipliers, OCR processing, audio minutes, silence fees, and research mode pricing. Note: Reseller markups and dedicated instance multipliers have been removed to reflect official provider pricing.
How does prompt caching work?
Caching discounts vary by provider: Google and OpenAI offer 90% discounts on cached input tokens. Anthropic uses write (1.25x) and read (0.10x) multipliers. Savings are applied to the token portion only, not unit-based fees.
How do Market Recommendations work (v8.6.0)?
Our recommendation engine scans the entire model registry and only includes models that support ALL your current input parameters (tokens, images, video, audio, OCR, tools, batch API, etc.). It calculates exact costs with your settings and sorts by price, showing you the best value options that can handle your complete workflow. Special pricing models (OCR, video, audio, image generation) are properly handled and only appear when their specific input types are requested. v8.6.0 removes reseller markups (20% buffer) and dedicated instance multipliers to reflect official provider pricing.
What is the YemHub AI Calculator Tool?
The YemHub AI Calculator is the most comprehensive tool for estimating costs and comparing performance metrics across 50+ AI models. It calculates token-based pricing, analyzes multimodal processing, accounts for state-dependent pricing (context cliffs, tiered tunnels), provides optimization recommendations, and now offers intelligent market matching to find the best alternatives for your specific needs.