TokenCosts AI.iov1.0
Verified 2026 API Rates • Prompt Caching & Batch ROI

DeepSeek: DeepSeek V3.2 vs Meta: Llama 4 Scout

Compare real-world API costs for DeepSeek: DeepSeek V3.2 ($0.26/$0.38) vs Meta: Llama 4 Scout ($0.1/$0.3). Calculate Prompt Caching ROI & Batch API savings.

Meta: Llama 4 Scout saves up to 62% on base input tokens
DeepSeek

DeepSeek: DeepSeek V3.2

deepseek-v3.2
Base Input Rate
$0.26 / 1M
Output Rate
$0.38 / 1M
Prompt Cache Read
$0.13 / 1M (-50%)
Cache Write Surcharge
$0.00 (Free write)
Batch API Discount
Not Supported
Context Window
163.84k tokens
Quality Index (MMLU)
88
Inspect Full Specs →Connect API
Meta

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 DeepSeek: DeepSeek V3.2 and Meta: Llama 4 Scout:

DeepSeek: DeepSeek V3.2 Caching Profile-50% read discount

  • Cached Read Price: $0.130 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 / ArchitectureStandard Input / OutputBatch 50% Off Input / OutputTurnaround SLAOptimal Use Cases
DeepSeek: DeepSeek V3.2$0.26 / $0.38Standard on-demand rateInstant real-timeBulk evaluation, dataset labeling, background summarization
Meta: Llama 4 Scout$0.10 / $0.30Standard on-demand rateInstant real-timeOffline 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.

DeepSeek: DeepSeek V3.2 Context Capacity

Provides a 163.84k token context window with a maximum output limit of 147.456k 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.

DeepSeek: DeepSeek V3.2:$72.08/mo
Meta: Llama 4 Scout:$47.00/mo
Delta Savings:-$25.07 (35%)
Architecture Verdict:Meta: Llama 4 Scout delivers 35% lower monthly spend ($25.07/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.

DeepSeek: DeepSeek V3.2:$41.30/mo
Meta: Llama 4 Scout:$29.00/mo
Delta Savings:-$12.30 (30%)
Architecture Verdict:Meta: Llama 4 Scout delivers 30% lower monthly spend ($12.30/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).

DeepSeek: DeepSeek V3.2:$223.00/mo
Meta: Llama 4 Scout:$112.50/mo
Delta Savings:-$110.50 (50%)
Architecture Verdict:Meta: Llama 4 Scout delivers 50% lower monthly spend ($110.50/mo savings) for this workload profile.
毫秒級即時互動模擬引擎

DeepSeek: DeepSeek V3.2 對比 Meta: Llama 4 Scout

針對企業典型三大工程落地形態（RAG 知識庫、Coding Agent 多輪、離線批次清洗）進行精確成本演算：

Live 50ms Engine
月度請求量 (Requests)100,000
單次輸入代幣 (Input Tokens)4,000
單次輸出代幣 (Output Tokens)1,000
Prompt 快取命中率 (Cache Hit)70%
Batch API 批次分流比例0%
Meta: Llama 4 Scout 更便宜

預計每月節省

DeepSeek: DeepSeek V3.2$105.60$1.056// 千次請求
對比
Meta: Llama 4 Scout$70.00$0.700// 千次請求
Developer FAQs

Frequently Asked Questions: DeepSeek: DeepSeek V3.2 vs Meta: Llama 4 Scout

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

1Which model is cheaper overall: DeepSeek: DeepSeek V3.2 or Meta: Llama 4 Scout?

At base pricing, DeepSeek: DeepSeek V3.2 costs $0.26/1M input and $0.38/1M 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 DeepSeek: DeepSeek V3.2 vs Meta: Llama 4 Scout?

DeepSeek: DeepSeek V3.2 offers a 50% read discount ($0.13/1M), 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 DeepSeek: DeepSeek V3.2 or Meta: Llama 4 Scout to save 50%?

DeepSeek: DeepSeek V3.2 does not have a native 50% batch endpoint. Meta: Llama 4 Scout uses low base on-demand pricing.

4What are the context window limits and tiered pricing rules for DeepSeek: DeepSeek V3.2 vs Meta: Llama 4 Scout?

DeepSeek: DeepSeek V3.2 features a 163.84k context window with flat per-token pricing. In comparison, Meta: Llama 4 Scout supports 1,310.72k tokens with flat per-token pricing.