Source-tracked model comparison

How do GPT-5.6 Luna and DeepSeek V4 Pro costs compare for agent loops?

Compare verified pricing and model limits for cost-aware agent routing across providers. The cost example uses a ai agent loops workload and does not assume equal model quality.

Direct cost answer: gpt-5.6-luna is estimated at $597.60 per month and deepseek-v4-pro at $128.02 for 6,000 input tokens, 2,500 output tokens, and 30,000 monthly requests. deepseek-v4-pro is $469.58 lower under these assumptions. This does not identify a quality winner.
low priorityobserveSources checked 2026-07-13 / 2026-07-12
Tracked facts

Pricing and model limits

Prices are USD per 1M tokens under each model's verified default profile.

Fieldgpt-5.6-lunadeepseek-v4-pro
ProviderOpenAIDeepSeek
API model IDgpt-5.6-lunadeepseek-v4-pro
Input / 1M$1.00$0.435
Cached input / 1M$0.10$0.0036
Output / 1M$6.00$0.87
Context window1,050,000 tokens1,000,000 tokens
Maximum output128,000 tokens384,000 tokens
Accepted inputtext, imagetext
Example workload

AI agent loops cost scenario

6,000 input and 2,500 output tokens per request, 30,000 monthly requests, and 20% cached input.

OpenAI

gpt-5.6-luna

$597.60 / month
Input cost
$147.60
Output cost
$450.00
Per 1,000 calls
$19.92
Pricing profile
standard / short context
View model details
DeepSeek

deepseek-v4-pro

$128.02 / month
Input cost
$62.77
Output cost
$65.25
Per 1,000 calls
$4.2673
Pricing profile
standard / cache miss
View model details

Cost result: deepseek-v4-pro is $469.58 lower per month for these assumptions. This is a price comparison, not a model-quality ranking.

StackLens assessment

Questions to answer before choosing

  • Which agent steps require reasoning rather than routine execution?
  • How many retries occur before a task succeeds?
  • Does multi-provider routing justify its operational complexity?
Workload caveat

What this estimate leaves out

Treat monthly requests as model calls, not user tasks. Tool execution and external API charges are excluded.

Latency, reliability, output quality, retries, regional processing, and provider-specific tool charges can change the practical decision.

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