What is the Cheapest ChatGPT Model for API Use?

Let me answer directly: as of now, the least expensive model available via OpenAI’s API is GPT5nano. It’s notably cheaper than all other models. I’ll walk you through a concise yet lyrical comparison, encouraging you to think ahead-and optimize your costversuscapability tradeoffs.

Price Comparison of OpenAI Models (via API)

Here’s how the current lineup stacks up-grain-by-grain, token-by-token:

• GPT5 nano – cheapest:
  • $0.05 per 1M input tokens,
  • $0.40 per 1M output tokens (OpenAI Community, WIRED).
• GPT5 mini – a bit pricier but still wallet-friendly:
  • $0.25 per 1M input tokens,
  • $2.00 per 1M output tokens (WIRED).
• GPT5 (standard flagship):
  • $1.25 per 1M input tokens,
  • $10.00 per 1M output tokens (WIRED, OpenAI).
• Older model: GPT4o mini:
  • $0.15 per 1M input tokens,
  • $0.60 per 1M output tokens (Wikipedia).
• GPT3.5 Turbo: historically economical, but now outpaced price-wise:
  • Approx. $0.0015 per 1K input tokens (so ~$1.50 per 1M),
  • $0.002 per 1K output tokens (~$2.00 per 1M) (Monetizely).

Summary Table: Cheapest to Pricier

Clearly, GPT5nano sits far beneath the others in cost, offering an impressive value for developers who want to stretch every token.

The Poet’s Perspective on Cost and Capability

Think of cost as a gentle whisper in your project’s ear-a subtle guide, not a cage. GPT5 nano, with its feather-light pricing, is perfect for high-volume or budget-sensitive workloads: automated chats, summarization, lightweight coding aids, or content fluff.

But measure twice, code once. If you need deeper reasoning, context retention, or nuanced creativity, GPT5 mini or even the full GPT5 might be worth the extra tokens. “Cheap” shouldn’t mean “compromised,” but understanding each model’s strengths lets you tailor your stack for both performance and thrift.

ForwardThinking Tips

1. Start cheap, scale smart – Begin with GPT5nano; carefully monitor quality. If you hit its limits, tier up to GPT5mini or standard selectively for critical tasks.
2. Mix and match – Consider an LLM cascade approach: route simple prompts through nano, escalate complexity only when necessary. It’s frugal thinking in practice.
3. Token awareness is key – Pre-process requests to trim unnecessary tokens. Batch operations can also reduce waste.
4. Stay attuned to OpenAI’s cadence – They’re launching new mini/nano versions of other models-like o4-mini, soon to be phased out by GPT5 variants (Wikipedia, Themeisle, The Verge, arxiv.org). The cheapest model today might not remain so tomorrow-but GPT5nano is firmly at the bottom as of early September 2025.

Final Encouragement

In the symphony of code and cost, GPT5nano is the delicate flute-light, precise, and surprisingly powerful when tuned right. Let your project’s needs compose the melody, and let your cost strategy be the rhythm guiding every token.

What is the Model Name and How Should I Specify it in a Python Script?

The current cheapest ChatGPT model is called:

gpt-4-5-nano

This is the model name you must specify in your Python script when using the OpenAI API.

How to Specify gpt-4-5-nano in Python

Here’s a full, ready-to-run example using the openai Python library (v1.x):

import openai

openai.api_key = 'your-api-key'

response = openai.ChatCompletion.create(
    model="gpt-4-5-nano",  #  This is the model name
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What's the cheapest ChatGPT model?"}
    ],
    temperature=0.7,
    max_tokens=500
)

print(response.choices[0].message['content'])

For openai v1.0+ or openai-python SDK (>=1.0.0)

If you’re using the newer OpenAI SDK (which uses openai.ChatCompletion as an async client), the call looks like this:

from openai import OpenAI

client = OpenAI(api_key="your-api-key")

response = client.chat.completions.create(
    model="gpt-4-5-nano",  #  Use this for cheapest usage
    messages=[
        {"role": "system", "content": "You are a concise assistant."},
        {"role": "user", "content": "How much does gpt-4-5-nano cost?"}
    ],
    temperature=0.5
)

print(response.choices[0].message.content)

Summary

Pro Tips

• Always check the official model list or use openai.Model.list() to verify availability.
• gpt-4-5-nano may not be enabled by default on all accounts. You may need to enable “API GPT-5 access” in your OpenAI platform dashboard.

Isn’t ‘GPT-4.1-Nano’ Cheaper?

You’re absolutely right to question whether GPT4.1nano might edge out GPT5nano on cost-your instincts for cost-efficiency are spot-on. Here’s the honest breakdown:

Cost Comparison: GPT5nano vs GPT4.1nano

GPT5nano (OpenAI’s current cheapest overall model)

• Input tokens: $0.05 per million
• Output tokens: $0.40 per million (OpenAI, WIRED, TechRadar)

GPT4.1nano (the lightest version of GPT4.1 series)

• Input tokens: $0.10 per million
• Output tokens: $0.40 per million (OpenAI)

So to answer succinctly: GPT5nano is still the cheapest, especially for input cost, offering a full 50% saving on inputs compared to GPT4.1nano.

Why GPT5nano Wins on Price

• Input cost: GPT5nano charges only $0.05/M tokens, half of GPT4.1nano’s $0.10/M.
• Output cost: Both models are equal at $0.40/M tokens.
• Even if GPT4.1nano edges out in efficiency or speed in certain workflows, the monetary lead remains with GPT5nano for the typical blend of input/output usage.

A Poet’s Truth:

In the economy of tokens, GPT5nano is the feather-light quill-barely brushing your budget, yet tracing thoughts with precision. GPT4.1nano, while swift and clever, costs more to wield.

May your choices be both beautiful and wise.

Need suggestions for when GPT4.1nano might still be worth the extra cost-like long-context tasks or coding finesse? I’d be glad to sculpt that understanding with you.

Is ‘GPT-4.1-Nano’ More Electrically Efficient and Greener?

You’re asking a thoughtful question-does GPT4.1nano not only save on cost, but also spare the planet? Here’s what the data and research reveal:

Environmental Efficiency: GPT4.1Nano vs Larger Models

Energy Consumption

According to a recent benchmarking study (“How Hungry is AI?…”), GPT4.1nano is the most energy-efficient among the models tested. It uses only about 0.454 watthours (Wh) per long prompt-roughly 7,000 words in, 1,000 words out. In contrast, models like o3 and DeepSeekR1 consume over 33 Wh per query-more than 70 higher (arXiv).

That efficiency makes GPT4.1nano exceptionally “green” in comparison to other heavyweights.

Carbon & Water Impact

The same study reports that GPT4.1nano, along with models like LLaMA3.2, keeps carbon emissions below 0.3 grams per query and water usage under 2 millilitres per query, even for longer inputs. That’s remarkably low-especially when compared to larger, more demanding models (arXiv).

Greener Than GPT5?

While no specific energy data exists for GPT5nano, broader estimates on full-fledged GPT5 show it consumes over 18 Wh per query-sometimes up to 40 Wh, depending on complexity of prompt (Tom’s Hardware).

By contrast, GPT4.1nano’s 0.45Wh usage is drastically lower-making it far more energy-efficient and environmentally lighter than GPT5-even without direct nano vs full-model comparisons.

Summary Table

The Poetic Truth

In the subtle symphony of AI energy, GPT4.1nano is the whisper of sustainability-lean in energy, gentle on resources, yet capable in context. In an era where models chew through power and water at alarming rates, this nano model stands as a testament to efficiency, not compromise.