TokenMix Research Lab · 2026-06-05

OpenAI API Cheapest Model 2026: GPT-5 Nano Cost Math Table

OpenAI API Cheapest Model 2026: GPT-5 Nano Cost Math Table

Last Updated: 2026-06-05 Author: TokenMix Research Lab Data verified: 2026-06-04 - OpenAI Platform pricing, OpenAI model docs, GPT-5.4 launch notes, prompt caching docs, Batch/Flex/Priority pricing guidance

The cheapest current OpenAI text model is gpt-5-nano, not GPT-5.4 nano. Use it for simple high-volume tasks, not frontier reasoning.

OpenAI's Platform pricing page lists gpt-5-nano at $0.05 input, $0.005 cached input, and $0.40 output per 1M tokens (OpenAI pricing). That makes it the cheapest current OpenAI text generation model in the pricing table. The cheapest GPT-5.4-class model is different: gpt-5.4-nano is listed at $0.20 input, $0.02 cached input, and $1.25 output per 1M tokens, while gpt-5.4-mini is $0.75 input and $4.50 output (OpenAI pricing, GPT-5.4 nano model page). The old "GPT-5.4 nano at $0.075/$0.30" read is no longer safe to publish. It conflicts with current OpenAI Platform pricing as checked on June 4, 2026.

Table of Contents

Quick Verdict

Claim Status Source
gpt-5-nano is the cheapest current OpenAI text generation model listed in Platform pricing Confirmed OpenAI pricing
gpt-5-nano costs $0.05 input, $0.005 cached input, and $0.40 output per 1M tokens Confirmed OpenAI pricing
gpt-5.4-nano is the cheapest GPT-5.4-class model Confirmed OpenAI pricing, model docs
gpt-5.4-nano is the cheapest OpenAI model overall False gpt-5-nano has lower input, cached input, and output price
gpt-4o-mini is still the cheapest OpenAI chat model False gpt-5-nano and gpt-4.1-nano are cheaper in current pricing
gpt-4.1-nano has the same standard output price as gpt-5-nano Confirmed OpenAI pricing
Batch/Flex can cut eligible workloads below standard pricing Confirmed OpenAI pricing, GPT-5.4 launch
Priority processing is the cheapest path for budget workloads False OpenAI describes Priority as a premium processing option
Embeddings are comparable substitutes for chat/reasoning models False Embedding models are not text-generation models

Cheapest OpenAI Text Models

Model Input / 1M Cached input / 1M Output / 1M Best budget use Status
gpt-5-nano $0.05 $0.005 $0.40 Classification, extraction, routing, simple agents Confirmed
gpt-4.1-nano $0.10 $0.025 $0.40 Older low-cost tasks, compatibility Confirmed
gpt-4o-mini $0.15 $0.075 $0.60 Legacy mini workloads, lightweight multimodal Confirmed
gpt-5.4-nano $0.20 $0.02 $1.25 Cheap GPT-5.4-class simple tasks Confirmed
gpt-5-mini $0.25 $0.025 $2.00 Better-defined GPT-5 tasks Confirmed
gpt-5.4-mini $0.75 $0.075 $4.50 Mid-cost GPT-5.4 workflows Confirmed
gpt-5 $1.25 $0.125 $10.00 General intelligent reasoning Confirmed
gpt-5.4 $2.50 $0.25 $15.00 Frontier GPT-5.4 tasks Confirmed
gpt-5.5 $5.00 $0.50 $30.00 Latest flagship tasks Confirmed

The ranking changes if your workload is output-heavy. gpt-5-nano and gpt-4.1-nano both list $0.40 output per 1M, but gpt-5-nano has lower input and cached input prices.

GPT-5 Nano vs GPT-5.4 Nano

Question gpt-5-nano gpt-5.4-nano Winner
Standard input price $0.05 / 1M $0.20 / 1M gpt-5-nano
Cached input price $0.005 / 1M $0.02 / 1M gpt-5-nano
Output price $0.40 / 1M $1.25 / 1M gpt-5-nano
Model family GPT-5 GPT-5.4 Depends on task
Best task Simple high-volume work GPT-5.4-class cheap lane Depends on quality need
Budget verdict Cheapest current text model Cheapest GPT-5.4-class model gpt-5-nano for cost

Cost calculation 1: a workload with 100M input tokens and 20M output tokens costs $5 + $8 = $13/month on gpt-5-nano. The same volume on gpt-5.4-nano costs $20 + $25 = $45/month. That is a $32/month difference before caching.

Cost Per Task

Task Assumed tokens gpt-5-nano gpt-4.1-nano gpt-5.4-nano Best cheap pick
Classify one support ticket 1K input, 100 output $0.00009 $0.00014 $0.000325 gpt-5-nano
Extract fields from invoice 4K input, 300 output $0.00032 $0.00052 $0.001175 gpt-5-nano
Rewrite short email 500 input, 300 output $0.000145 $0.00017 $0.000475 gpt-5-nano
Route agent step 2K input, 50 output $0.00012 $0.00022 $0.0004625 gpt-5-nano
Summarize 50K-token doc 50K input, 1K output $0.0029 $0.0054 $0.01125 gpt-5-nano unless quality fails

Cost calculation 2: at 1M support-ticket classifications with 1K input and 100 output each, gpt-5-nano costs about $90. gpt-5.4-nano costs about $325. The quality delta must save more than $235/month to justify the GPT-5.4-class nano lane for that workload.

$10 Token Buying Power

Model $10 buys input tokens $10 buys cached input tokens $10 buys output tokens
gpt-5-nano 200M 2,000M 25M
gpt-4.1-nano 100M 400M 25M
gpt-4o-mini 66.7M 133.3M 16.7M
gpt-5.4-nano 50M 500M 8M
gpt-5-mini 40M 400M 5M
gpt-5.4-mini 13.3M 133.3M 2.2M
gpt-5 8M 80M 1M
gpt-5.4 4M 40M 0.67M

Cost calculation 3: if your app is mostly cached prompt reuse, gpt-5-nano cached input at $0.005/1M means $10 buys 2B cached input tokens. If output is the bottleneck, the same $10 buys only 25M output tokens. Cheap input does not make long completions free.

Monthly Cost Projection

Monthly workload gpt-5-nano gpt-4.1-nano gpt-5.4-nano gpt-5-mini gpt-5.4
10M input + 2M output $1.30 $1.80 $4.50 $6.50 $55.00
100M input + 20M output $13.00 $18.00 $45.00 $65.00 $550.00
500M input + 50M output $45.00 $70.00 $162.50 $225.00 $2,000.00
1B input + 100M output $90.00 $140.00 $325.00 $450.00 $4,000.00
1B cached input + 100M output $45.00 $65.00 $145.00 $225.00 $1,250.00

For broad provider comparisons, use Cheapest AI API Providers 2026. This page answers one narrower question: which OpenAI model is cheapest inside OpenAI's own API menu.

Batch Flex Priority and Caching

Cost lever Effect Best use Caveat
Prompt caching Reduces repeated input cost Long system prompts, tool lists, repeated docs Output cost unchanged
Batch Lower cost for async work Offline evals, extraction, summarization Not user-facing latency
Flex Lower cost / flexible processing Latency-tolerant production jobs Availability and timing tradeoff
Priority Higher price for priority processing Latency-sensitive production Not a budget lever
Model downgrade Direct rate reduction Classification, routing, short extraction Quality risk
Output cap Reduces output spend Summaries, agents, rewriting Can harm answer quality

OpenAI's GPT-5.4 launch note says Batch and Flex pricing are available at half the standard API rate, while Priority processing is available at twice the standard API rate (OpenAI GPT-5.4). Use that as a routing rule: cheap model plus async lane beats expensive model plus priority lane for non-urgent tasks.

Use Case Matrix

Use case Cheapest safe OpenAI pick Why
Intent classification gpt-5-nano Short output, high volume
JSON extraction gpt-5-nano first, escalate on failure Cheap input and output
Simple email rewrite gpt-5-nano or gpt-4.1-nano Quality threshold is modest
Agent routing gpt-5-nano Router calls should be cheap
RAG chunk triage gpt-5-nano with caching Input-heavy and repetitive
Long legal summary gpt-5-mini or higher Quality and instruction following matter
Coding plan gpt-5-mini or gpt-5 Cheap nano may fail complex reasoning
Frontier reasoning gpt-5.4, gpt-5.5, or pro tier Cost is not the main constraint

If you route across providers, AI API Gateway and TokenMix vs OpenRouter vs Portkey vs LiteLLM cover the gateway tradeoff. Inside OpenAI only, start with nano and escalate.

Where Cheap Loses

Workload Why gpt-5-nano may lose Pick instead
Multi-step reasoning Cheap models can fail harder on planning gpt-5-mini, gpt-5, or higher
Complex coding Code reasoning needs stronger model class gpt-5, Codex model, or GPT-5.4
Long-horizon agents Small failures compound over steps Stronger model with eval gates
Customer-facing final answer Quality variance is visible Test against gpt-5-mini and gpt-5
Safety-sensitive decisions Cheap output is not worth wrong answer Use stronger model and guardrails
High-output generation Output tokens dominate anyway Shorten output or use better model

The budget rule is not "always use nano." It is "default to nano where failure is cheap, measurable, and recoverable." For model-level routing in the Claude cluster, compare Claude Opus 4.8 Review and Frontier Pro Tier 2026.

If the "cheapest OpenAI model" question is really about embeddings, not text generation, use Text Embedding Ada 002 Dimension 2026. If a cheap model is unavailable because of organization access, check OpenAI API Verification 2026 before assuming a pricing problem.

Final Recommendation

For the cheapest OpenAI API model in June 2026, use gpt-5-nano. Use gpt-5.4-nano only when you specifically need a GPT-5.4-class cheap lane. For production, route nano first, cache aggressively, cap output, and escalate only when evals show quality loss.

FAQ

What is the cheapest OpenAI API model in 2026?

The cheapest current OpenAI text generation model is gpt-5-nano. OpenAI Platform pricing lists it at $0.05 input, $0.005 cached input, and $0.40 output per 1M tokens.

Is GPT-5.4 nano the cheapest OpenAI model?

No. gpt-5.4-nano is the cheapest GPT-5.4-class model, but gpt-5-nano is cheaper overall in the current OpenAI Platform pricing table.

Is GPT-4o mini still the cheapest OpenAI model?

No. gpt-4o-mini is cheaper than many older large models, but current OpenAI pricing lists gpt-5-nano and gpt-4.1-nano below it for text generation.

How much does GPT-5 nano cost?

OpenAI lists gpt-5-nano at $0.05 per 1M input tokens, $0.005 per 1M cached input tokens, and $0.40 per 1M output tokens.

What is the cheapest OpenAI model for classification?

Use gpt-5-nano first. Classification is usually short-output and high-volume, which fits the cheapest current OpenAI text model.

What is the cheapest OpenAI model for long summaries?

Start with gpt-5-nano only if quality is acceptable. For long summaries where instruction following matters, test gpt-5-mini or gpt-5 against your eval set.

Do Batch and Flex make OpenAI cheaper?

Yes for eligible latency-tolerant workloads. OpenAI says Batch and Flex pricing can be half the standard API rate, while Priority is a premium lane.

Are embedding models cheaper than GPT-5 nano?

Embedding models can be cheaper per token, but they are not chat/text-generation models. Do not compare them as substitutes for completion or reasoning workloads.

Sources

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