Source-tracked model comparison

gpt-5.6-luna vs DeepSeek V4 Flash non-thinking: which costs less for a practical API workload?

Compare verified pricing and model limits for API pricing and workload fit across providers. The cost example uses a long-context research workload and does not assume equal model quality.

Direct cost answer: DeepSeek V4 Flash non-thinking is estimated at $40.68 per month and gpt-5.6-luna at $333.00 for 150,000 input tokens, 5,000 output tokens, and 2,000 monthly requests. DeepSeek V4 Flash non-thinking is $292.32 lower under these assumptions. This does not identify a quality winner.

low priorityobserveSources checked 2026-07-12 / 2026-07-13

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Tracked facts

Pricing and model limits

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

Field	DeepSeek V4 Flash non-thinking	gpt-5.6-luna
Provider	DeepSeek	OpenAI
API model ID	`deepseek-v4-flash`	`gpt-5.6-luna`
Input / 1M	$0.14	$1.00
Cached input / 1M	$0.0028	$0.10
Output / 1M	$0.28	$6.00
Context window	1,000,000 tokens	1,050,000 tokens
Maximum output	384,000 tokens	128,000 tokens
Accepted input	text	text, image

Example workload

Long-context research cost scenario

150,000 input and 5,000 output tokens per request, 2,000 monthly requests, and 10% cached input.

DeepSeek

DeepSeek V4 Flash non-thinking

$40.68 / month

Input cost: $37.88
Output cost: $2.80
Per 1,000 calls: $20.34
Pricing profile: standard / cache miss

View model details

OpenAI

gpt-5.6-luna

$333.00 / month

Input cost: $273.00
Output cost: $60.00
Per 1,000 calls: $166.50
Pricing profile: standard / short context

View model details

Cost result: DeepSeek V4 Flash non-thinking is $292.32 lower per month for these assumptions. This is a price comparison, not a model-quality ranking.

StackLens assessment

Questions to answer before choosing

Which model meets the required quality threshold on representative inputs?
How do output length, retries, and caching change the measured cost?
Which provider and deployment path fit the team's operational requirements?

Workload caveat

What this estimate leaves out

Models whose tracked context window is below the scenario input are excluded from the compatible-model table.

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

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