Source-tracked model comparison

How do Claude Haiku 4.5 and DeepSeek V4 Flash costs compare for classification?

Compare verified pricing and model limits for economical classification and routing. The cost example uses a classification and routing workload and does not assume equal model quality.

Direct cost answer: Claude Haiku 4.5 is estimated at $550.00 per month and DeepSeek V4 Flash non-thinking at $60.20 for 700 input tokens, 80 output tokens, and 500,000 monthly requests. DeepSeek V4 Flash non-thinking is $489.80 lower under these assumptions. This does not identify a quality winner.

medium priorityactiveSources checked 2026-07-12 / 2026-07-12

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Tracked facts

Pricing and model limits

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

Field	Claude Haiku 4.5	DeepSeek V4 Flash non-thinking
Provider	Anthropic	DeepSeek
API model ID	`claude-haiku-4-5-20251001`	`deepseek-v4-flash`
Input / 1M	$1.00	$0.14
Cached input / 1M	$0.10	$0.0028
Output / 1M	$5.00	$0.28
Context window	200,000 tokens	1,000,000 tokens
Maximum output	64,000 tokens	384,000 tokens
Accepted input	text, image	text

Example workload

Classification and routing cost scenario

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

Anthropic

Claude Haiku 4.5

$550.00 / month

Input cost: $350.00
Output cost: $200.00
Per 1,000 calls: $1.10
Pricing profile: standard

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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

View model details

Cost result: DeepSeek V4 Flash non-thinking is $489.80 lower per month for these assumptions. This is a price comparison, not a model-quality ranking.

StackLens assessment

Questions to answer before choosing

Are the required structured outputs supported and reliable?
Which provider meets deployment and data-handling requirements?
What is the measured fallback rate on ambiguous cases?

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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