Is GPT-4o cheaper than GPT-3.5 Turbo?

On input tokens, GPT-3.5 Turbo is cheaper ($0.50 vs $5.00 per million). On output tokens, GPT-3.5 Turbo is cheaper. Total cost on a typical workload depends on your input:output ratio.

Which has the larger context window, GPT-4o or GPT-3.5 Turbo?

GPT-4o has a 128,000-token context window. GPT-3.5 Turbo fits 16,385 tokens.

How do I switch from GPT-4o to GPT-3.5 Turbo?

Update the model id in your API call: replace "gpt-4o" with "gpt-3.5-turbo". Then verify cost impact on a sample of your real prompts using a token counter.

GPT-4o vs GPT-3.5 Turbo

Side-by-side comparison of GPT-4o (OpenAI) and GPT-3.5 Turbo (OpenAI). Exact API pricing per million tokens, context windows, output speed, and total cost on real-world prompts.

Specifications

Spec	GPT-4o	GPT-3.5 Turbo
Provider	OpenAI	OpenAI
Model id	gpt-4o	gpt-3.5-turbo
Input price (per 1M tokens)	$5.00	$0.50
Output price (per 1M tokens)	$15.00	$1.50
Context window	128,000	16,385
Output speed (tokens/sec)	~95	~150
Tokenizer encoding	o200k_base	cl100k_base

Cost on real prompts

Total cost = (input tokens × input price) + (output tokens × output price). Numbers below use the exact pricing tables published by each provider.

Scenario	Input	Output	GPT-4o	GPT-3.5 Turbo	Cheaper
Short question + answer	50	150	$0.002500	$0.00025	GPT-3.5 Turbo
Code review on one file	500	1,500	$0.025000	$0.002500	GPT-3.5 Turbo
Long document summary	5,000	500	$0.032500	$0.003250	GPT-3.5 Turbo
Heavy reasoning task	2,000	8,000	$0.130000	$0.013000	GPT-3.5 Turbo
Full codebase analysis	50,000	10,000	$0.400000	$0.040000	GPT-3.5 Turbo

Want the exact cost for your prompt instead of these examples? Open the cost calculator pre-loaded with both models →

When to pick which

Heuristics derived from the spec table above. Always validate on your own prompts before committing — these are starting points, not verdicts.

Pick GPT-4o for

•tasks needing a larger context window — gpt-4o fits 8x more tokens than gpt-3.5-turbo

Pick GPT-3.5 Turbo for

•output-heavy workloads — gpt-3.5-turbo is meaningfully cheaper per output token
•input-heavy workloads — gpt-3.5-turbo is cheaper per input token
•latency-sensitive UX — gpt-3.5-turbo streams faster (~150 vs ~95 tok/s)

Switching between them

For most use cases, switching providers means updating the model id and the request shape if the providers differ. Within the same provider, it's usually a single-line change.

From GPT-4o to GPT-3.5 Turbo

# Before
model = "gpt-4o"

# After
model = "gpt-3.5-turbo"

If the providers differ (OpenAI vs OpenAI), you'll also need to swap the SDK / endpoint URL. Cross-provider migrations usually take 30 minutes to a few hours depending on how many features (streaming, function calling, tool use) you depend on.

Calculate cost on your own prompt

The examples above use generic input/output ratios. For an exact comparison, paste your real prompt into the calculator — it counts tokens with the right tokenizer for each model and shows side-by-side cost.

Open the calculator with both models →