Source-tracked model comparison

GPT-5.4 Nano vs DeepSeek V4 Flash: how do classification API costs compare?

Compare verified pricing and model limits for low-cost non-reasoning classification routes. The cost example uses a classification and routing workload and does not assume equal model quality.

Direct cost answer: DeepSeek V4 Flash non-thinking is estimated at $60.20 per month and gpt-5.4-nano at $120.00 for 700 input tokens, 80 output tokens, and 500,000 monthly requests. DeepSeek V4 Flash non-thinking is $59.80 lower under these assumptions. This does not identify a quality winner.

Current provider alternativesClassification and routingSources 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-5.4-nano
Provider	DeepSeek	OpenAI
API model ID	`deepseek-v4-flash`	`gpt-5.4-nano`
Input / 1M	$0.14	$0.20
Cached input / 1M	$0.0028	$0.02
Output / 1M	$0.28	$1.25
Context window	1,000,000 tokens	400,000 tokens
Maximum output	384,000 tokens	128,000 tokens
Accepted input	text	text, image

Example workload

Classification and routing cost scenario

700 input and 80 output tokens per request, 500,000 monthly requests, and 0% cached input.

DeepSeek

DeepSeek V4 Flash non-thinking

$60.20 / month

Input cost: $49.00
Output cost: $11.20
Per 1,000 calls: $0.1204
Pricing profile: standard / cache miss

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OpenAI

gpt-5.4-nano

$120.00 / month

Input cost: $70.00
Output cost: $50.00
Per 1,000 calls: $0.24
Pricing profile: standard / short context

View model details

Cost result: DeepSeek V4 Flash non-thinking is $59.80 lower per month for these assumptions. This is a price comparison, not a model-quality ranking.

StackLens assessment

Questions to answer before choosing

Do both models support the required response schema?
How often does classification need a larger fallback model?
Are regional availability and data handling acceptable?

Workload caveat

What this estimate leaves out

Quality and latency are not assumed equal across models; validate labels on a representative evaluation set.

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

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