GPT · Mid-tier · 1,050K context window

GPT 5.4 pricing for resume parsing

$0.0183 per call at 2,500 input / 800 output tokens.

Per call

$0.0183

10K calls/mo

$182.5

Cache savings

90% off input

Batch savings

50% off all

Calculate your GPT 5.4 costs

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

Configure your usage

2,500
100100K
800
5050K
10,000
1001M

Estimated cost

Monthly cost
$183
10,000 calls/month
Cost per call$0.0183
Daily cost$6
Input rate$2.50/MTok
Output rate$15.00/MTok
Analyze your real spend

Quick start

Call GPT 5.4 with the OpenAI Python SDK:

from openai import OpenAI

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-5.4",
    max_tokens=800,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.0183 per call at standard rates
# Input:  $2.50/MTok
# Output: $15.00/MTok

How GPT 5.4 compares on cost

Per-call cost for all models scoring 50+ on resume parsing benchmarks. Hover for details.

Cheapest viable option: GPT 5.6 Luna at $0.001460/call (92% less, 89/100 quality).

What drives resume parsing cost

Resumes are longer than invoices and less uniformly structured, so input runs higher while output stays compact. Like invoice parsing this is a bulk, latency-tolerant workload where the batch API discount applies to the entire request. The parsing instructions are identical across every candidate, which puts them squarely in the cached prefix.

For GPT 5.4 specifically, that means output tokens cost 6.0x more than input, at $15.00 vs $2.50 per MTok. At this use case’s 2,500/800 token split, output accounts for 66% of the $0.0183 per-call cost.

Complete GPT 5.4 pricing breakdown

Standard token rates

Direction$/MTokTokens per call
Input$2.502,500
Output$15.00800

Prompt caching: 90% off input tokens

Caching drops input cost from $2.50 to $0.25/MTok. For repeated system prompts, that saves $0.005625 per call.

Batch API: 50% off all tokens

Submit requests asynchronously and receive 50% 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$0.0183$182.5$126.25$56.25
50,000 calls$0.0183$912.5$631.25$281.25
100,000 calls$0.0183$1,825$1,262.5$562.5
500,000 calls$0.0183$9,125$6,312.5$2,812.5
1,000,000 calls$0.0183$18,250$12,625$5,625

GPT 5.4 quality benchmarks for resume parsing

GPT 5.4 is a solid pick for resume parsing

Scores 89/100 (rank #1 of 20). Good balance of quality and cost for resume parsing.

Top-ranked: GPT 5.6 Terra (90/100) at $0.0146/call

Performance across all task types

GPT 5.4 scores by task type. Resume Parsing highlighted.

Summarization91Q&A91Extraction *89Classification88Code Generation84Code Review82Reasoning80Creative Writing78
Task typeConfidence scoreRank out of 20
Summarization91/100#1
Q&A91/100#1
Extraction(current page)89/100#1
Classification88/100#1
Code Generation84/100#4
Code Review82/100#5
Reasoning80/100#5
Creative Writing78/100#5

Confidence scores are a third-party benchmark snapshot from week 29 of 2026. This snapshot is 8 weeks old, so treat the scores as directional and validate against your own evals.

Alternative models for resume parsing

13 models score 80+ on resume parsing benchmarks, sorted by input rate. Cheapest: GPT 5.6 Luna (92% less, 89/100 quality).

ModelProviderInput $/MTokOutput $/MTokQualityPer callvs GPT
GPT 5.6 LunaGPT$0.20$1.2089/100$0.00146092% cheaper
Gemini 3.5 Flash-LiteGemini$0.30$2.5083/100$0.00275085% cheaper
Gemini 3 FlashGemini$0.50$3.0086/100$0.00365080% cheaper
Claude Haiku 4.5Claude$1.00$5.0088/100$0.00650064% cheaper
Gemini 3.5 FlashGemini$1.50$9.0090/100$0.011040% cheaper
Claude Sonnet 5Claude$2.00$10.0088/100$0.013029% cheaper
GPT 5.6 TerraGPT$2.00$12.0090/100$0.014620% cheaper
Gemini 3.1 ProGemini$2.00$12.0085/100$0.014620% cheaper
Claude Sonnet 4.6Claude$3.00$15.0086/100$0.01957% more
GPT 5.6 SolGPT$4.00$20.0088/100$0.026042% more
Claude Opus 4.8Claude$5.00$25.0082/100$0.032578% more
GPT 5.5GPT$5.00$30.0087/100$0.0365100% more
Claude Fable 5Claude$10.00$50.0085/100$0.0650256% more

About GPT 5.4

Provider

GPT

Tier

Mid-tier

Context

1,050K tokens

Released

Mar 2026

Other models from GPT

ModelTierInput $/MTokOutput $/MTokContext
GPT 5.5Flagship$5.00$30.001,050K
GPT 5.6 SolFlagship$4.00$20.001,050K
GPT 5.6 TerraMid-tier$2.00$12.001,050K
GPT 5.6 LunaFast / Budget$0.20$1.201,050K

You can compare GPT 5.4 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 GPT 5.4 cost for resume parsing per API call?

A typical resume parsing call using GPT 5.4 costs about $0.0183 per call. This estimate uses 2,500 input tokens and 800 output tokens at standard rates. Standard rates are $2.50 per million input tokens and $15.00 per million output tokens.

Is GPT 5.4 the best model for resume parsing?

GPT 5.4 scores 89 out of 100 on resume parsing benchmarks overall. It ranks #1 across all 20 models in our cross-provider registry. The highest-ranked model for this task is GPT 5.6 Terra from GPT. That model scores 90/100 on this task type. Confidence scores are a third-party benchmark snapshot from week 29 of 2026. This snapshot is 8 weeks old, so treat the scores as directional and validate against your own evals.

What is the cheapest model for resume parsing with good quality?

The most affordable model for resume parsing above 80/100 quality is GPT 5.6 Luna. It comes from GPT at $0.20 per million input tokens. Output costs $1.20 per million tokens with a quality score of 89/100. Switching from GPT 5.4 to GPT 5.6 Luna saves about 92% per API call.

How can I reduce my GPT 5.4 costs for resume parsing?

Three strategies lower GPT 5.4 costs for this task type effectively. Prompt caching gives a 90% discount on input tokens when reusing system prompts. The batch API gives 50% 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 $0.0183 to about $0.000913 per call.

How does GPT 5.4 pricing compare to other GPT models?

GPT has 4 other models alongside GPT 5.4 today: GPT 5.5 at $5.00/$30.00 per million tokens (flagship), GPT 5.6 Sol at $4.00/$20.00 per million tokens (flagship), GPT 5.6 Terra at $2.00/$12.00 per million tokens (mid-tier), GPT 5.6 Luna at $0.20/$1.20 per million tokens (fast / budget). The right choice depends on whether your resume parsing workload prioritizes quality, cost, or speed.

What context window does GPT 5.4 support?

GPT 5.4 supports a context window of 1,050K tokens per API call. This sets the maximum combined length of your input prompt and generated response. For resume parsing, typical calls use 2,500 input and 800 output tokens. That is well within this limit, leaving room for longer prompts or multi-turn conversations.

Track what you spend on AI

This calculator estimates costs based on published rates only. Connect your API usage to see where your money goes and which models drive your spend.