GPT · Mid-tier · 1,050K context window
$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
Adjust token counts, volume, and discount toggles to model your resume parsing spend. Prices verified daily against provider pricing pages.
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/MTokPer-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).
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.
| Direction | $/MTok | Tokens per call |
|---|---|---|
| Input | $2.50 | 2,500 |
| Output | $15.00 | 800 |
Caching drops input cost from $2.50 to $0.25/MTok. For repeated system prompts, that saves $0.005625 per call.
Submit requests asynchronously and receive 50% off all token costs. This works best for offline pipelines where latency requirements are flexible.
| Monthly volume | Cost per call | Standard monthly | Cached monthly | You 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 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
GPT 5.4 scores by task type. Resume Parsing highlighted.
| Task type | Confidence score | Rank out of 20 |
|---|---|---|
| Summarization | 91/100 | #1 |
| Q&A | 91/100 | #1 |
| Extraction(current page) | 89/100 | #1 |
| Classification | 88/100 | #1 |
| Code Generation | 84/100 | #4 |
| Code Review | 82/100 | #5 |
| Reasoning | 80/100 | #5 |
| Creative Writing | 78/100 | #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.
13 models score 80+ on resume parsing benchmarks, sorted by input rate. Cheapest: GPT 5.6 Luna (92% less, 89/100 quality).
| Model | Provider | Input $/MTok | Output $/MTok | Quality | Per call | vs GPT |
|---|---|---|---|---|---|---|
| GPT 5.6 Luna | GPT | $0.20 | $1.20 | 89/100 | $0.001460 | 92% cheaper |
| Gemini 3.5 Flash-Lite | Gemini | $0.30 | $2.50 | 83/100 | $0.002750 | 85% cheaper |
| Gemini 3 Flash | Gemini | $0.50 | $3.00 | 86/100 | $0.003650 | 80% cheaper |
| Claude Haiku 4.5 | Claude | $1.00 | $5.00 | 88/100 | $0.006500 | 64% cheaper |
| Gemini 3.5 Flash | Gemini | $1.50 | $9.00 | 90/100 | $0.0110 | 40% cheaper |
| Claude Sonnet 5 | Claude | $2.00 | $10.00 | 88/100 | $0.0130 | 29% cheaper |
| GPT 5.6 Terra | GPT | $2.00 | $12.00 | 90/100 | $0.0146 | 20% cheaper |
| Gemini 3.1 Pro | Gemini | $2.00 | $12.00 | 85/100 | $0.0146 | 20% cheaper |
| Claude Sonnet 4.6 | Claude | $3.00 | $15.00 | 86/100 | $0.0195 | 7% more |
| GPT 5.6 Sol | GPT | $4.00 | $20.00 | 88/100 | $0.0260 | 42% more |
| Claude Opus 4.8 | Claude | $5.00 | $25.00 | 82/100 | $0.0325 | 78% more |
| GPT 5.5 | GPT | $5.00 | $30.00 | 87/100 | $0.0365 | 100% more |
| Claude Fable 5 | Claude | $10.00 | $50.00 | 85/100 | $0.0650 | 256% more |
Provider
GPT
Tier
Mid-tier
Context
1,050K tokens
Released
Mar 2026
| Model | Tier | Input $/MTok | Output $/MTok | Context |
|---|---|---|---|---|
| GPT 5.5 | Flagship | $5.00 | $30.00 | 1,050K |
| GPT 5.6 Sol | Flagship | $4.00 | $20.00 | 1,050K |
| GPT 5.6 Terra | Mid-tier | $2.00 | $12.00 | 1,050K |
| GPT 5.6 Luna | Fast / Budget | $0.20 | $1.20 | 1,050K |
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.
See how GPT 5.4 costs change across different workload types.
Code Generation
$0.0650/call
Unit Test Generation
$0.0825/call
SQL Generation
$0.0187/call
API Generation
$0.0975/call
Code Refactoring
$0.0700/call
Code Review
$0.0425/call
PR Review
$0.0650/call
Security Review
$0.0525/call
Bug Detection
$0.0425/call
Document Summarization
$0.0400/call
PDF Summarization
$0.0600/call
Meeting Notes
$0.0500/call
Email Summarization
$0.0150/call
Chatbot
$0.0200/call
RAG Pipeline
$0.0375/call
Customer Support Bot
$0.0225/call
FAQ Bot
$0.0158/call
Data Extraction
$0.0250/call
JSON Extraction
$0.0195/call
Invoice Parsing
$0.0140/call
Chain of Thought
$0.0825/call
Legal Analysis
$0.0850/call
Math Reasoning
$0.0488/call
Financial Analysis
$0.0800/call
Content Moderation
$0.002750/call
Sentiment Analysis
$0.002750/call
Categorization
$0.004250/call
Intent Detection
$0.002250/call
Copywriting
$0.0313/call
Blog Writing
$0.1225/call
Marketing Copy
$0.0470/call
Product Descriptions
$0.0238/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.
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.
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.
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.
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.
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.
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.