Gemini 1.5 Flash vs GPT-4o mini
Head-to-head specifications
| Metric | Gemini 1.5 Flash | GPT-4o mini | Difference |
|---|---|---|---|
| MMLU (general capability) | 78.9% | 82.0% | -3.1% |
| Context window | 1M tokens | 128K tokens | — |
| Price (input / output per 1M) | $0.075 / $0.3 | $0.15 / $0.6 | — |
| Access | Proprietary API | Proprietary API | — |
- GPT-4o mini leads general capability (MMLU 82.0% vs 78.9%).
- Gemini 1.5 Flash offers the larger context window, useful for long documents and codebases.
Verdict: Gemini 1.5 Flash or GPT-4o mini?
Gemini 1.5 Flash advantages
- Context window (+87%)
- Input cost (+50%)
- Output cost (+50%)
GPT-4o mini advantages
- No decisive advantage on the tracked metrics.
Which should you choose?
- Choose the Gemini 1.5 Flash if you work with long documents or large codebases.
- Choose the Gemini 1.5 Flash if you process large volumes of input and want the lowest cost.
Value for money
Gemini 1.5 Flash offers more capability per dollar — a better value pick for high-volume use, delivering 1.92× the MMLU-per-cost of the alternative.
Gemini 1.5 Flash vs GPT-4o mini: which should you choose?
Gemini 1.5 Flash — Google large language model (2024) with a 1M-token context window and an MMLU score of 78.9%.
GPT-4o mini — OpenAI large language model (2024) with a 128K-token context window and an MMLU score of 82.0%.
Gemini 1.5 Flash vs GPT-4o mini: GPT-4o mini scores higher on the MMLU benchmark. GPT-4o mini leads general capability (MMLU 82.0% vs 78.9%). Gemini 1.5 Flash offers the larger context window, useful for long documents and codebases.
Capability and reasoning
On MMLU — a 57-subject benchmark of general knowledge and reasoning — the GPT-4o mini scores 82.0% versus 78.9%. MMLU is a useful proxy for raw knowledge but does not capture instruction-following, coding, tool use, latency or safety, so treat it as one signal among several.
Context window
The Gemini 1.5 Flash handles up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once — decisive for retrieval-augmented and long-document workflows.
Pricing and access
Gemini 1.5 Flash is proprietary api and GPT-4o mini is proprietary api. Proprietary models bill per token via API; open-weight models can be self-hosted, trading per-call cost for infrastructure you manage. For production, weigh throughput, rate limits and data-residency needs alongside headline price.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the Gemini 1.5 Flash better than the GPT-4o mini?
Gemini 1.5 Flash is the clearly stronger overall choice, winning most of the dimensions that matter. GPT-4o mini leads general capability (MMLU 82.0% vs 78.9%).
What is the main difference between the Gemini 1.5 Flash and the GPT-4o mini?
GPT-4o mini leads general capability (MMLU 82.0% vs 78.9%). Gemini 1.5 Flash offers the larger context window, useful for long documents and codebases.
Which is better value?
Gemini 1.5 Flash offers more capability per dollar — a better value pick for high-volume use, delivering 1.92× the MMLU-per-cost of the alternative.
Which should I choose?
Choose the Gemini 1.5 Flash if you work with long documents or large codebases. Choose the Gemini 1.5 Flash if you process large volumes of input and want the lowest cost.
Methodology
Large language models are compared on the MMLU benchmark (a widely-cited 57-subject test of general knowledge and reasoning, reported as a percentage), maximum context window, and published API pricing per million input and output tokens. Open-weight models can also be self-hosted. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit.