DeepSeek · Mid-tier · 128K context window

DeepSeek R1 pricing for json extraction

$0.003402 per call at 3,000 input / 800 output tokens.

Per call

$0.003402

10K calls/mo

$34.02

Cache savings

75% off input

Batch savings

0% off all

Calculate your DeepSeek R1 costs

Adjust token counts, volume, and discount toggles to model your json extraction spend. Prices verified daily against provider pricing pages.

Configure your usage

3,000
100100K
800
5050K
10,000
1001M

Estimated cost

Monthly cost
$34
10,000 calls/month
Cost per call$0.003402
Daily cost$1
Input rate$0.55/MTok
Output rate$2.19/MTok
Analyze your real spend

Quick start

Call DeepSeek R1 with the OpenAI Python SDK pointed at api.deepseek.com:

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.deepseek.com/v1",
    api_key=os.environ["DEEPSEEK_API_KEY"],
)
response = client.chat.completions.create(
    model="deepseek-r1",
    max_tokens=800,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.003402 per call at standard rates
# Input:  $0.55/MTok
# Output: $2.19/MTok

What drives json extraction cost

Structured JSON output is compact and predictable, so cost is set almost entirely by how much source text you send. The schema itself is identical on every call and belongs in the cached prefix. This is also a task where a smaller model often matches a larger one: the output space is constrained by the schema, which limits how wrong a cheaper model can go.

For DeepSeek R1 specifically, that means output tokens cost 4.0x more than input, at $2.19 vs $0.55 per MTok. At this use case’s 3,000/800 token split, output accounts for 51% of the $0.003402 per-call cost.

Complete DeepSeek R1 pricing breakdown

Standard token rates

Direction$/MTokTokens per call
Input$0.553,000
Output$2.19800

Prompt caching: 75% off input tokens

Caching drops input cost from $0.55 to $0.14/MTok. For repeated system prompts, that saves $0.001229 per call.

Batch API: 0% off all tokens

Submit requests asynchronously and receive 0% off all token costs. This works best for offline pipelines where latency requirements are flexible.

Monthly cost projections

Monthly volumeCost per callStandard monthlyCached monthlyYou save with caching
10,000 calls$0.003402$34.02$21.73$12.29
50,000 calls$0.003402$170.1$108.64$61.46
100,000 calls$0.003402$340.2$217.28$122.93
500,000 calls$0.003402$1,701$1,086.38$614.63
1,000,000 calls$0.003402$3,402$2,172.75$1,229.25

DeepSeek R1 quality benchmarks for json extraction

Benchmark data for json extraction is pending for DeepSeek R1. Test against your own evaluation suite before moving to production.

Benchmark data is not yet available for DeepSeek R1. We recommend testing against your own evaluation criteria.

Alternative models for json extraction

2 models score 80+ on json extraction benchmarks, sorted by input rate. Cheapest: Gemini 3 Flash (15% more, 86/100 quality).

ModelProviderInput $/MTokOutput $/MTokQualityPer callvs DeepSeek
Gemini 3 FlashGemini$0.50$3.0086/100$0.00390015% more
Claude Haiku 4.5Claude$1.00$5.0088/100$0.007000106% more

About DeepSeek R1

Provider

DeepSeek

Tier

Mid-tier

Context

128K tokens

Released

Jan 2025

Prices are off-peak rates from api-docs.deepseek.com (read 2026-10-11). Peak-hour prices are 2x the listed input and output rates — Tokeven does not model time-of-day tiers; off-peak is used as the base. Cache hit: $0.14/MTok input. No batch discount published. Reasoning model: output includes chain-of-thought tokens. Not yet benchmarked, so never recommended.

Other models from DeepSeek

ModelTierInput $/MTokOutput $/MTokContext
DeepSeek V3Mid-tier$0.27$1.10164K
DeepSeek FlashFast / Budget$0.15$0.601M
DeepSeek V4 ProFlagship$0.66$1.981M

You can compare DeepSeek R1 head-to-head against any other model on our model comparison pages, or track how pricing has changed over time on the pricing history tracker.

Frequently asked questions

How much does DeepSeek R1 cost for json extraction per API call?

A typical json extraction call using DeepSeek R1 costs about $0.003402 per call. This estimate uses 3,000 input tokens and 800 output tokens at standard rates. Standard rates are $0.55 per million input tokens and $2.19 per million output tokens.

Is DeepSeek R1 the best model for json extraction?

Our benchmark data does not yet include a json extraction ranking for DeepSeek R1. We recommend testing it against your own evaluation criteria before production usage.

What is the cheapest model for json extraction with good quality?

The most affordable model for json extraction above 80/100 quality is Gemini 3 Flash. It comes from Gemini at $0.50 per million input tokens. Output costs $3.00 per million tokens with a quality score of 86/100. Gemini 3 Flash costs more per call than DeepSeek R1 but scores higher on quality benchmarks for this task type.

How can I reduce my DeepSeek R1 costs for json extraction?

Three strategies lower DeepSeek R1 costs for this task type effectively. Prompt caching gives a 75% discount on input tokens when reusing system prompts. The batch API gives 0% off all tokens for asynchronous processing workloads. Prompt optimization reduces token counts through more concise instructions and context windows. Combining caching with batching brings your cost from $0.003402 to about $0.000868 per call.

How does DeepSeek R1 pricing compare to other DeepSeek models?

DeepSeek has 3 other models alongside DeepSeek R1 today: DeepSeek V3 at $0.27/$1.10 per million tokens (mid-tier), DeepSeek Flash at $0.15/$0.60 per million tokens (fast / budget), DeepSeek V4 Pro at $0.66/$1.98 per million tokens (flagship). The right choice depends on whether your json extraction workload prioritizes quality, cost, or speed.

What context window does DeepSeek R1 support?

DeepSeek R1 supports a context window of 128K tokens per API call. This sets the maximum combined length of your input prompt and generated response. For json extraction, typical calls use 3,000 input and 800 output tokens. That is well within this limit, leaving room for longer prompts or multi-turn conversations.

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