Gemini 2.0 Flash (Feb '25) vs GPT-4.1 Comparison

Gemini 2.0 Flash (Feb '25) (Google) and GPT-4.1 (OpenAI) are both large language models available via API. On list price, Gemini 2.0 Flash (Feb '25) is cheaper, while GPT-4.1 scores higher on benchmarks. When you factor in token efficiency — how many tokens each model needs for the same task — Gemini 2.0 Flash (Feb '25) delivers more intelligence per dollar. List prices can be misleading because different models consume different numbers of tokens for the same work. The effective costs below adjust for this using benchmark data, so you can compare what equivalent work actually costs.

Benchmark Scores

Intelligence Index

Gemini 2.0 Flash (Feb '25) 18.5

GPT-4.1 25.6

MMLU-Pro

Gemini 2.0 Flash (Feb '25) 0.8

GPT-4.1 0.8

GPQA

Gemini 2.0 Flash (Feb '25) 0.6

GPT-4.1 0.7

AIME

Gemini 2.0 Flash (Feb '25) 0.3

GPT-4.1 0.4

Performance

Metric	Gemini 2.0 Flash (Feb '25)	GPT-4.1
Output tokens/sec	N/A	85.9
Time to first token	N/A	0.49s
Context window	1,000,000	1,000,000

Pricing per 1M Tokens

Metric	Gemini 2.0 Flash (Feb '25)	GPT-4.1	Gap
Input price / 1M tokens	$0.15	$2.0	13.3x
Output price / 1M tokens	$0.6	$8.0	13.3x
Cache hit price / 1M tokens	$0.025	$0.01	2.5x

Intelligence vs Price

Higher is smarter, further left is cheaper. Top-left is best value.

Gemini 2.0 Flash (Feb '25) GPT-4.1 Other models

Value Analysis

Cheaper

Gemini 2.0 Flash (Feb '25)

Higher Benchmarks

GPT-4.1

Better Value ($/IQ point)

Gemini 2.0 Flash (Feb '25)

$0.04 / IQ point

GPT-4.1

$0.39 / IQ point

Frequently Asked Questions

Which is cheaper, Gemini 2.0 Flash (Feb '25) or GPT-4.1?

Gemini 2.0 Flash (Feb '25) is cheaper on list price. Gemini 2.0 Flash (Feb '25) costs $0.15/M input and $0.6/M output tokens. GPT-4.1 costs $2.0/M input and $8.0/M output tokens. On combined list price, Gemini 2.0 Flash (Feb '25) is 13.3x cheaper than GPT-4.1. However, list prices alone can be misleading because different models use different numbers of tokens for the same task. Check the effective cost comparison above, which adjusts for token efficiency using benchmark data.

Which scores higher on benchmarks, Gemini 2.0 Flash (Feb '25) or GPT-4.1?

GPT-4.1 has a higher Intelligence Index (25.6) compared to Gemini 2.0 Flash (Feb '25) (18.5). The Intelligence Index is a composite score from three industry-standard benchmarks: MMLU-Pro (general knowledge and reasoning), GPQA (graduate-level science), and AIME (mathematical problem solving). A higher score means the model produces more accurate and capable responses across a broad range of tasks. This composite approach is more reliable than any single benchmark because it measures different types of capability.

Which model is better value for money, Gemini 2.0 Flash (Feb '25) or GPT-4.1?

Gemini 2.0 Flash (Feb '25) offers better value at $0.04 per intelligence point compared to GPT-4.1 at $0.39 per intelligence point. Cost per intelligence point measures how much you pay for each unit of benchmark performance, calculated as the combined token cost divided by the Intelligence Index score. When token efficiency data is available, this calculation uses effective prices (adjusted for the fact that different models consume different numbers of tokens for the same task) rather than raw list prices. A lower cost per intelligence point means you get more capability per dollar.

Which has a larger context window, Gemini 2.0 Flash (Feb '25) or GPT-4.1?

Gemini 2.0 Flash (Feb '25) supports 1,000,000 tokens compared to GPT-4.1 with 1,000,000 tokens. The context window determines how much text (including your prompt, conversation history, and documents) the model can process in a single request. A larger context window is important for tasks like document summarization, long-form analysis, and multi-turn conversations with extensive history. If your use case involves processing large inputs, the context window may be a deciding factor.

Gemini 2.0 Flash (Feb '25) vs GPT-4.1