Model Comparison
Side-by-side benchmarks, pricing, and value analysis. See which model costs less per intelligence point.
Gemini 2.5 Pro (Google) and GPT-4.1 mini (OpenAI) are both large language models available via API. On list price, GPT-4.1 mini is cheaper, while Gemini 2.5 Pro scores higher on benchmarks. When you factor in token efficiency — how many tokens each model needs for the same task — Gemini 2.5 Pro 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.
| Metric | Gemini 2.5 Pro | GPT-4.1 mini | Gap |
|---|---|---|---|
| Output tokens/sec | 159.0 | 74.1 | 2.1x |
| Time to first token | 35.38s | 0.46s | 76.6x |
| Context window | 32,000 | 200,000 | 6.2x |
| Metric | Gemini 2.5 Pro | GPT-4.1 mini | Gap |
|---|---|---|---|
| Input price / 1M tokens | $1.25 | $0.4 | 3.1x |
| Output price / 1M tokens | $10.0 | $1.6 | 6.2x |
| Cache hit price / 1M tokens | $0.3 | $0.075 | 4.0x |
List prices adjusted for token efficiency. Different models use different numbers of tokens for the same task — these prices reflect what equivalent work actually costs.
| Metric | Gemini 2.5 Pro | GPT-4.1 mini | Gap |
|---|---|---|---|
| Input (adjusted) / 1M | $1.4643 | $0.3415 | 4.3x |
| Output (adjusted) / 1M | $0.7425 | $21.5475 | 29.0x |
| Input token ratio | 1.17x | 0.85x | |
| Output token ratio | 0.07x | 13.47x |
Higher is smarter, further left is cheaper. Top-left is best value. Prices adjusted for token efficiency.
Cheaper
GPT-4.1 mini
Higher Benchmarks
Gemini 2.5 Pro
Better Value ($/IQ point)
Gemini 2.5 Pro
Gemini 2.5 Pro
$0.06 / IQ point
GPT-4.1 mini
$0.98 / IQ point
GPT-4.1 mini is cheaper on list price. Gemini 2.5 Pro costs $1.25/M input and $10.0/M output tokens. GPT-4.1 mini costs $0.4/M input and $1.6/M output tokens. On combined list price, GPT-4.1 mini is 5.6x cheaper than Gemini 2.5 Pro. 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.
Gemini 2.5 Pro has a higher Intelligence Index (34.5) compared to GPT-4.1 mini (22.4). 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.
Gemini 2.5 Pro offers better value at $0.06 per intelligence point compared to GPT-4.1 mini at $0.98 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.
GPT-4.1 mini supports 200,000 tokens compared to Gemini 2.5 Pro with 32,000 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.
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