Is o3 cheaper than GPT-4o?

On input tokens, o3 is cheaper ($2.00 vs $5.00 per million). On output tokens, o3 is cheaper. Total cost on a typical workload depends on your input:output ratio.

Which has the larger context window, o3 or GPT-4o?

o3 has a 200,000-token context window. GPT-4o fits 128,000 tokens.

How do I switch from o3 to GPT-4o?

Update the model id in your API call: replace "o3" with "gpt-4o". Then verify cost impact on a sample of your real prompts using a token counter.

o3 vs GPT-4o

Side-by-side comparison of o3 (OpenAI) and GPT-4o (OpenAI). Exact API pricing per million tokens, context windows, output speed, and total cost on real-world prompts.

Specifications

Spec	o3	GPT-4o
Provider	OpenAI	OpenAI
Model id	o3	gpt-4o
Input price (per 1M tokens)	$2.00	$5.00
Output price (per 1M tokens)	$8.00	$15.00
Context window	200,000	128,000
Output speed (tokens/sec)	~40	~95
Tokenizer encoding	o200k_base	o200k_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	o3	GPT-4o	Cheaper
Short question + answer	50	150	$0.001300	$0.002500	o3
Code review on one file	500	1,500	$0.013000	$0.025000	o3
Long document summary	5,000	500	$0.014000	$0.032500	o3
Heavy reasoning task	2,000	8,000	$0.068000	$0.130000	o3
Full codebase analysis	50,000	10,000	$0.180000	$0.400000	o3

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 o3 for

•output-heavy workloads (long-form generation, code, summaries) — o3 is meaningfully cheaper per output token
•input-heavy workloads (long context, RAG, document QA) — o3 is cheaper per input token
•tasks needing a larger context window — o3 fits 2x more tokens than gpt-4o
•complex multi-step reasoning (math, debugging, planning) — o3 uses chain-of-thought internally

Pick GPT-4o for

•latency-sensitive UX — gpt-4o streams faster (~95 vs ~40 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 o3 to GPT-4o

# Before
model = "o3"

# After
model = "gpt-4o"

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 →