Scale Comparison: Llama 4 Scout vs. Gemini 2.5 Flash for 500K Tokens

Llama 4 Scout vs Gemini 2.5 Flash
Complete Comparison: 500,000 input tokens × 800 output tokens
Comparison Mode

Complete comparison of pricing, performance, and capabilities for 2 leading AI models .

📊 Batch API
Comparison Criteria Llama 4 Scout
Meta AI
Gemini 2.5 Flash
Google
Calculation Results (Current Inputs)
Input Tokens 500,000 500,000
Output Tokens 800 800
Cost Breakdown
Input Cost $0.040000Worst $0.037500 (rounded ~ $0.04)Best
Output Cost $0.000240Best $0.000500Worst
Unit Cost (Audio/OCR) $0.000000 $0.000000
Service Fees $0.000000 $0.000000
Total Cost $0.040240 Most Expensive $0.038000 (rounded ~ $0.04) Best Value
Processing Time 14 minutes, 53.27 seconds 14 minutes, 53.27 seconds
Tokens per Second 600 600
Time to First Token 120ms 120ms
Cost per 1K tokens $0.000080Worst $0.000076Best
Tokens per Dollar 12,445,328Worst Value 13,178,947Best Value
Cost per 1 Million Tokens (Informational)
Input Cost / 1M (Base) $0.080000Worst $0.075000 (rounded ~ $0.08)Best
Output Cost / 1M (Base) $0.300000Best $0.625000 (rounded ~ $0.63)Worst
Input Cost / 1M (Optimized) $0.080000Worst
Optimizations: No optimizations applied
$0.037500 (rounded ~ $0.04)Best
Optimizations: 50.0% batch
Output Cost / 1M (Optimized) $0.300000Best
Optimizations: No optimizations applied
$0.312500 (rounded ~ $0.31)Worst
Optimizations: 50.0% batch
Capabilities & Advanced Features
Images Support ✗ Not Supported ✓ Supported
Video Support ✗ Not Supported ✓ Supported
Audio Support ✗ Not Supported ✓ Supported
Caching Support ✗ Not Supported ✓ Supported
Batch API Support ✓ Supported ✓ Supported
Tool Usage Support ✗ Not Supported ✓ Supported
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Select AI Model

Llama 4 Scout
Meta AIMax Context: 10,000,000 tokens
$0.08 / $0.3 per 1M tokens
▼
▼
Use Batch API (50% discount)
0%
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

$0.040240 Total Cost
$0.040000 Input Cost
$0.000240 Output Cost
$0.000000 Unit Cost
$0.000000 Search Cost
$0.000000 Request Fee
$0.000000 Tool Fee
$0.000000 Code Execution
500,800Total Tokens
$0.000080Cost per 1K
12,445,328Tokens per $
📊 Advanced Cost Breakdown

Processing Speed

14m 53s Processing Time
600 Tokens/Second
120ms Time to First Token
561 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.

Llama 4 Scout Meta AI 10000000

$0.040240
Total Cost
📊 Batch API
👁️
Vision/Images
✗ Not Available
🎧
Audio Processing
✗ Not Available
🎥
Video Analysis
✗ Not Available
🔧
Tool Usage
✗ Not Available
📄
OCR Support
✗ Not Available
📊
Batch API
✓ Available
⚡
Caching
✗ Not Available

💰 Total Cost Calculation (from Plugin)

Base Cost (No Optimizations) $0.040240 Input: $0.040000
Output: $0.000240
Optimized Cost $0.040240 Input: $0.040000
Output: $0.000240
Unit: $0.000000
Fees: $0.000000

Detailed Cost Analysis (from Plugin)

For 500,000 input tokens and 800 output tokens:

  • Input Cost: $0.040000
  • Output Cost: $0.000240
  • Total Cost: $0.040240
  • Cost per 1K tokens: $0.000080
  • Tokens per dollar: 12,445,328 tokens
  • Context Window: 10000000 tokens

Speed & Performance Analysis

With a processing speed of 600 tokens per second and 120ms time to first token:

  • Processing Time: 14 minutes, 53.27 seconds
  • Latency: 120 milliseconds to first token
  • Base Throughput: 600 tokens/second
  • Effective Throughput: 561 tokens/second (temperature-adjusted)

Best Use Cases

This comparison is for developers prototyping large-scale text generation where cost-efficiency and context window are key. Llama 4 Scout offers an exceptionally large context window at a low priceideal for tasks requiring extensive background information. Gemini 2.5 Flash provides similar affordability with the added benefit of multimodal capabilitiesmaking it versatile for projects that might evolve to include images or audio.

Want this applied to YOUR actual stack?

This calculator shows the math for Llama 4 Scout. 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 2.5 Flash Google 1000000

$0.038000 (rounded ~ $0.04)
Total Cost
📊 Batch API
👁️
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) $0.038000 (rounded ~ $0.04) Input: $0.037500 (rounded ~ $0.04)
Output: $0.000500
Optimized Cost $0.038000 (rounded ~ $0.04) Input: $0.037500 (rounded ~ $0.04)
Output: $0.000500
Unit: $0.000000
Fees: $0.000000

Advanced Cost Breakdown (from Plugin)

📊 Batch API
50.0% off
Asynchronous processing discount

Detailed Cost Analysis (from Plugin)

For 500,000 input tokens and 800 output tokens:

  • Input Cost: $0.037500 (rounded ~ $0.04)
  • Output Cost: $0.000500
  • Total Cost: $0.038000 (rounded ~ $0.04)
  • Cost per 1K tokens: $0.000076
  • Tokens per dollar: 13,178,947 tokens
  • Context Window: 1000000 tokens

Speed & Performance Analysis

With a processing speed of 600 tokens per second and 120ms time to first token:

  • Processing Time: 14 minutes, 53.27 seconds
  • Latency: 120 milliseconds to first token
  • Base Throughput: 600 tokens/second
  • Effective Throughput: 561 tokens/second (temperature-adjusted)

Best Use Cases

This comparison is for developers prototyping large-scale text generation where cost-efficiency and context window are key. Llama 4 Scout offers an exceptionally large context window at a low priceideal for tasks requiring extensive background information. Gemini 2.5 Flash provides similar affordability with the added benefit of multimodal capabilitiesmaking it versatile for projects that might evolve to include images or audio.

Want this applied to YOUR actual stack?

This calculator shows the math for Gemini 2.5 Flash. 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 Llama 4 Scout
📋 Active Input Parameters
Input Tokens: 500,000
Output Tokens: 800
Batch API: Enabled (50% discount)
Rank AI Model & Provider Total Cost vs Llama 4 Scout vs Gemini 2.5 Flash
🏆 Gemini 3.1 Flash Lite
Google
 $0.031550 (rounded ~ $0.03) Best Value ↓ 21.6% cheaper ↓ 17% cheaper
🥈 Nemotron 3 Super
Mistral AI
 $0.037664 (rounded ~ $0.04) ↓ 6.4% cheaper ↓ 0.9% cheaper
🥉 Gemini 2.5 Flash
Google
 $0.038000 (rounded ~ $0.04) ↓ 5.6% cheaper Same price
#4 Llama 4 Maverick (400B)
Meta AI
 $0.075480 (rounded ~ $0.08) ↑ 87.6% more ↑ 98.6% more
#5 Grok 4.3
xAI
 $0.156750 (rounded ~ $0.16) ↑ 289.5% more ↑ 312.5% more
#6 Gemini 3.5 Flash
Google
 $0.189300 ↑ 370.4% more ↑ 398.2% more
#7 Grok 4.20 Beta
xAI
 $0.251200 (rounded ~ $0.25) ↑ 524.3% more ↑ 561.1% more
#8 Gemini 3.1 Flash
Google
 $0.252400 (rounded ~ $0.25) ↑ 527.2% more ↑ 564.2% more
#9 Claude Sonnet 4.6
Anthropic
 $0.378000 (rounded ~ $0.38) ↑ 839.4% more ↑ 894.7% more
#10 Claude Opus 4.7
Anthropic
 $0.630000 ↑ 1465.6% more ↑ 1557.9% more
#11 Claude Opus 4.8
Anthropic
 $0.630000 ↑ 1465.6% more ↑ 1557.9% more
#12 Claude Opus 4.6
Anthropic
 $0.630000 ↑ 1465.6% more ↑ 1557.9% more
#13 Gemini 2.5 Pro
Google
 $0.631000 (rounded ~ $0.63) ↑ 1468.1% more ↑ 1560.5% more
#14 Gemini 3.1 Pro
Google
 $1.007200 (rounded ~ $1.01) ↑ 2403% more ↑ 2550.5% more
#15 GPT-5.4
OpenAI
 $1.259000 (rounded ~ $1.26) ↑ 3028.7% more ↑ 3213.2% more
#16 GPT-5.4 Thinking
OpenAI
 $1.259000 (rounded ~ $1.26) ↑ 3028.7% more ↑ 3213.2% more
#17 GPT-5.5
OpenAI
 $2.518000 (rounded ~ $2.52) ↑ 6157.5% more ↑ 6526.3% more
#18 GPT-5.5 Pro
OpenAI
 $3.786000 (rounded ~ $3.79) ↑ 9308.5% more ↑ 9863.2% more
#19 GPT-5.5 Pro
OpenAI
 $3.786000 (rounded ~ $3.79) ↑ 9308.5% more ↑ 9863.2% more
🏆

Gemini 3.1 Flash Lite
Google

$0.031550 (rounded ~ $0.03)
vs Llama 4 Scout: ↓ 21.6%
vs Gemini 2.5 Flash: ↓ 17%
🥈

Nemotron 3 Super
Mistral AI

$0.037664 (rounded ~ $0.04)
vs Llama 4 Scout: ↓ 6.4%
vs Gemini 2.5 Flash: ↓ 0.9%
🥉

Gemini 2.5 Flash
Google

$0.038000 (rounded ~ $0.04)
vs Llama 4 Scout: ↓ 5.6%
vs Gemini 2.5 Flash: Same
#4

Llama 4 Maverick (400B)
Meta AI

$0.075480 (rounded ~ $0.08)
vs Llama 4 Scout: ↑ 87.6%
vs Gemini 2.5 Flash: ↑ 98.6%
#5

Grok 4.3
xAI

$0.156750 (rounded ~ $0.16)
vs Llama 4 Scout: ↑ 289.5%
vs Gemini 2.5 Flash: ↑ 312.5%
#6

Gemini 3.5 Flash
Google

$0.189300
vs Llama 4 Scout: ↑ 370.4%
vs Gemini 2.5 Flash: ↑ 398.2%
#7

Grok 4.20 Beta
xAI

$0.251200 (rounded ~ $0.25)
vs Llama 4 Scout: ↑ 524.3%
vs Gemini 2.5 Flash: ↑ 561.1%
#8

Gemini 3.1 Flash
Google

$0.252400 (rounded ~ $0.25)
vs Llama 4 Scout: ↑ 527.2%
vs Gemini 2.5 Flash: ↑ 564.2%
#9

Claude Sonnet 4.6
Anthropic

$0.378000 (rounded ~ $0.38)
vs Llama 4 Scout: ↑ 839.4%
vs Gemini 2.5 Flash: ↑ 894.7%
#10

Claude Opus 4.7
Anthropic

$0.630000
vs Llama 4 Scout: ↑ 1465.6%
vs Gemini 2.5 Flash: ↑ 1557.9%
#11

Claude Opus 4.8
Anthropic

$0.630000
vs Llama 4 Scout: ↑ 1465.6%
vs Gemini 2.5 Flash: ↑ 1557.9%
#12

Claude Opus 4.6
Anthropic

$0.630000
vs Llama 4 Scout: ↑ 1465.6%
vs Gemini 2.5 Flash: ↑ 1557.9%
#13

Gemini 2.5 Pro
Google

$0.631000 (rounded ~ $0.63)
vs Llama 4 Scout: ↑ 1468.1%
vs Gemini 2.5 Flash: ↑ 1560.5%
#14

Gemini 3.1 Pro
Google

$1.007200 (rounded ~ $1.01)
vs Llama 4 Scout: ↑ 2403%
vs Gemini 2.5 Flash: ↑ 2550.5%
#15

GPT-5.4
OpenAI

$1.259000 (rounded ~ $1.26)
vs Llama 4 Scout: ↑ 3028.7%
vs Gemini 2.5 Flash: ↑ 3213.2%
#16

GPT-5.4 Thinking
OpenAI

$1.259000 (rounded ~ $1.26)
vs Llama 4 Scout: ↑ 3028.7%
vs Gemini 2.5 Flash: ↑ 3213.2%
#17

GPT-5.5
OpenAI

$2.518000 (rounded ~ $2.52)
vs Llama 4 Scout: ↑ 6157.5%
vs Gemini 2.5 Flash: ↑ 6526.3%
#18

GPT-5.5 Pro
OpenAI

$3.786000 (rounded ~ $3.79)
vs Llama 4 Scout: ↑ 9308.5%
vs Gemini 2.5 Flash: ↑ 9863.2%
#19

GPT-5.5 Pro
OpenAI

$3.786000 (rounded ~ $3.79)
vs Llama 4 Scout: ↑ 9308.5%
vs Gemini 2.5 Flash: ↑ 9863.2%
✨ 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.

Comparing Context and Capabilities for Prototyping

For developers prototyping AI features, understanding how different models handle significant token volumes is key. This comparison pits Llama 4 Scout, known for its massive context window, against Gemini 2.5 Flash, offering multimodal capabilities at an affordable price, both evaluated for generating around 500,000 tokens.

Llama 4 Scout from Meta AI excels with an industry-leading 10 million token context window, making it suitable for processing extremely long documents or maintaining extensive conversational memory. Its cost-effectiveness for such large contexts makes it a compelling option for applications requiring deep information retrieval or complex, long-running interactions.

Gemini 2.5 Flash, on the other hand, provides a 1 million token context window and, crucially, integrates multimodal understanding, including vision and audio processing, at a highly competitive rate. This makes it a versatile choice for applications that might expand beyond text to include richer media analysis.

When selecting between these two, developers should weigh the absolute necessity of a 10M context window versus the utility of multimodal features and integration within the Google ecosystem. Both offer excellent value for scaling text generation needs in early-stage development.

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 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.