Verified 2026 API Rates • Prompt Caching & Batch ROI

OpenAI: GPT-4.1 vs Meta: Llama 4 Scout

Compare real-world API costs for OpenAI: GPT-4.1 ($2/$8) vs Meta: Llama 4 Scout ($0.1/$0.3). Calculate Prompt Caching ROI & Batch API savings.

Meta: Llama 4 Scout saves up to 95% on base input tokens

OpenAI

OpenAI: GPT-4.1

gpt-4.1

Base Input Rate: $2.00 / 1M
Output Rate: $8.00 / 1M
Prompt Cache Read: $0.50 / 1M (-75%)
Cache Write Surcharge: $0.00 (Free write)
Batch API Discount: -50% Off
Context Window: 1,047.576k tokens
Quality Index (MMLU): 78

Inspect Full Specs →Connect API

Meta: Llama 4 Scout

llama-4-scout

Base Input Rate: $0.10 / 1M
Output Rate: $0.30 / 1M
Prompt Cache Read: $0.10 / 1M (-0%)
Cache Write Surcharge: $0.00 (Free write)
Batch API Discount: Not Supported
Context Window: 1,310.72k tokens
Quality Index (MMLU): 78

Inspect Full Specs →Connect API

Differential Module 1

Prompt Caching ROI & Write Surcharge Break-Even Analysis

Prompt caching drastically reduces input token costs for static prefixes like system prompts, tool schemas, and document chunks. Compare caching mechanisms between OpenAI: GPT-4.1 and Meta: Llama 4 Scout:

OpenAI: GPT-4.1 Caching Profile-75% read discount

Cached Read Price: $0.500 per 1M tokens.
Write Surcharge: $0.00 (automatic caching with zero write penalty).
Minimum Cache Prefix: 1,024 tokens.
Amortization Rule: Immediate net savings from first cached read call.

Meta: Llama 4 Scout Caching Profile-0% read discount

Cached Read Price: $0.100 per 1M tokens.
Write Surcharge: $0.00 (automatic caching with zero write penalty).
Minimum Cache Prefix: 1,024 tokens.
Amortization Rule: Immediate net savings from first cached read call.

Differential Module 2

Batch API 50% Discount Offload vs Real-Time SLA

Asynchronous Batch APIs cut total token expenditures in half (50% off input and output) in exchange for non-realtime execution (up to 24-hour turnaround SLA).

Model / Architecture	Standard Input / Output	Batch 50% Off Input / Output	Turnaround SLA	Optimal Use Cases
OpenAI: GPT-4.1	$2.00 / $8.00	$1.00 / $4.00 (-50%)	24 Hours max (typically 30m - 2h)	Bulk evaluation, dataset labeling, background summarization
Meta: Llama 4 Scout	$0.10 / $0.30	Standard on-demand rate	Instant real-time	Offline document translation, embedding backfills, test suites

Differential Module 3

Context Window Escalation & Tiered Pricing Architecture

Massive context windows allow ingesting entire repositories or books, but pricing models differ drastically between flat-rate vendors and tiered step-up pricing.

OpenAI: GPT-4.1 Context Capacity

Provides a 1,047.576k token context window with a maximum output limit of 32.768k tokens.✓ Flat-rate billing across all prompt sizes up to max context ceiling.

Meta: Llama 4 Scout Context Capacity

Provides a 1,310.72k token context window with a maximum output limit of 16.384k tokens.✓ Flat-rate billing across all prompt sizes up to max context ceiling.

Quill Strategic Content

Real-World Production Scenarios: Monthly Spend Projections

Pre-computed monthly engineering bill projections across 3 representative enterprise application workloads:

Scenario 1

RAG Knowledge Base & Enterprise Search

100,000 queries/month, 3,500 input tokens (system + 5 retrieved chunks), 400 output tokens, 75% prompt cache hit rate.

OpenAI: GPT-4.1:$626.25/mo

Meta: Llama 4 Scout:$47.00/mo

Delta Savings:-$579.25 (92%)

Architecture Verdict:Meta: Llama 4 Scout delivers 92% lower monthly spend ($579.25/mo savings) for this workload profile.

Scenario 2

Multi-Turn Coding Agent & ReAct Loops

25,000 tasks/month, 8,000 input tokens (accumulating conversation context & tool schemas), 1,200 output tokens, 85% cache hit rate.

OpenAI: GPT-4.1:$356.13/mo

Meta: Llama 4 Scout:$29.00/mo

Delta Savings:-$327.13 (92%)

Architecture Verdict:Meta: Llama 4 Scout delivers 92% lower monthly spend ($327.13/mo savings) for this workload profile.

Scenario 3

High-Throughput Offline Data Extraction

500,000 records/month, 1,500 input tokens, 250 output tokens, 100% batch offload (24-hour turnaround SLA).

OpenAI: GPT-4.1:$1137.50/mo

Meta: Llama 4 Scout:$112.50/mo

Delta Savings:-$1025.00 (90%)

Architecture Verdict:Meta: Llama 4 Scout delivers 90% lower monthly spend ($1025.00/mo savings) for this workload profile.

Real-Time Interactive Engine

OpenAI: GPT-4.1 vs Meta: Llama 4 Scout

Pre-computed monthly engineering bill projections across 3 representative enterprise application workloads:

Live 50ms Engine

Monthly Requests100,000

Input Tokens / Req4,000

Output Tokens1,000

Prompt Cache Hit Rate70%

Batch API Offload0%

Meta: Llama 4 Scout Cheaper

saves approximately

OpenAI: GPT-4.1$1,180.00$11.800// 1k reqs

Meta: Llama 4 Scout$70.00$0.700// 1k reqs

Developer FAQs

Frequently Asked Questions: OpenAI: GPT-4.1 vs Meta: Llama 4 Scout

Authoritative answers on API rate limits, pricing math, break-even thresholds, and architectural migration.

1Which model is cheaper overall: OpenAI: GPT-4.1 or Meta: Llama 4 Scout?

At base pricing, OpenAI: GPT-4.1 costs $2/1M input and $8/1M output, while Meta: Llama 4 Scout costs $0.1/1M input and $0.3/1M output. The cheaper option depends on your input-to-output token ratio and whether prompt caching or batch discounts are utilized.

2How does Prompt Caching impact the cost of OpenAI: GPT-4.1 vs Meta: Llama 4 Scout?

OpenAI: GPT-4.1 offers a 75% read discount ($0.5/1M), whereas Meta: Llama 4 Scout provides a 0% read discount ($0.1/1M). High cache hit ratios (>70%) significantly compress the cost delta in agentic and RAG architectures.

3Can I use Batch API with OpenAI: GPT-4.1 or Meta: Llama 4 Scout to save 50%?

OpenAI: GPT-4.1 supports 50% batch offload discounts for asynchronous 24-hour turnaround tasks. Meta: Llama 4 Scout uses low base on-demand pricing.

4What are the context window limits and tiered pricing rules for OpenAI: GPT-4.1 vs Meta: Llama 4 Scout?

OpenAI: GPT-4.1 features a 1,047.576k context window with flat per-token pricing. In comparison, Meta: Llama 4 Scout supports 1,310.72k tokens with flat per-token pricing.