Is GPT-4.1 cheaper than GPT-3.5 Turbo?

On input tokens, GPT-3.5 Turbo is cheaper ($0.50 vs $2.00 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-4.1 or GPT-3.5 Turbo?

GPT-4.1 has a 1,047,576-token context window. GPT-3.5 Turbo fits 16,385 tokens.

How do I switch from GPT-4.1 to GPT-3.5 Turbo?

Update the model id in your API call: replace "gpt-4.1" with "gpt-3.5-turbo". Then verify cost impact on a sample of your real prompts using a token counter.

GPT-4.1 vs GPT-3.5 Turbo

Side-by-side comparison of GPT-4.1 (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-4.1	GPT-3.5 Turbo
Provider	OpenAI	OpenAI
Model id	gpt-4.1	gpt-3.5-turbo
Input price (per 1M tokens)	$2.00	$0.50
Output price (per 1M tokens)	$8.00	$1.50
Context window	1,047,576	16,385
Output speed (tokens/sec)	~120	~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-4.1	GPT-3.5 Turbo	Cheaper
Short question + answer	50	150	$0.001300	$0.00025	GPT-3.5 Turbo
Code review on one file	500	1,500	$0.013000	$0.002500	GPT-3.5 Turbo
Long document summary	5,000	500	$0.014000	$0.003250	GPT-3.5 Turbo
Heavy reasoning task	2,000	8,000	$0.068000	$0.013000	GPT-3.5 Turbo
Full codebase analysis	50,000	10,000	$0.180000	$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-4.1 for

•tasks needing a larger context window — gpt-4.1 fits 64x 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

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-4.1 to GPT-3.5 Turbo

# Before
model = "gpt-4.1"

# 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 →