Yi-Large vs GPT-4o mini
Head-to-head specifications
| Metric | Yi-Large | GPT-4o mini | Difference |
|---|---|---|---|
| MMLU (general capability) | 83.0% | 82.0% | +1.0% |
| Context window | 32K tokens | 128K tokens | — |
| Price (input / output per 1M) | $3 / $3 | $0.15 / $0.6 | — |
| Access | Proprietary API | Proprietary API | — |
- Yi-Large leads general capability (MMLU 83.0% vs 82.0%).
- GPT-4o mini offers the larger context window, useful for long documents and codebases.
Verdict: Yi-Large or GPT-4o mini?
Yi-Large advantages
- No decisive advantage on the tracked metrics.
GPT-4o mini advantages
- Context window (+75%)
- Input cost (+95%)
- Output cost (+80%)
Which should you choose?
- Choose the GPT-4o mini if you work with long documents or large codebases.
Value for money
GPT-4o mini offers more capability per dollar — a better value pick for high-volume use, delivering 7.90× the MMLU-per-cost of the alternative.
Yi-Large vs GPT-4o mini: which should you choose?
Yi-Large — 01.AI large language model (2024) with a 32K-token context window and an MMLU score of 83.0%.
GPT-4o mini — OpenAI large language model (2024) with a 128K-token context window and an MMLU score of 82.0%.
Yi-Large vs GPT-4o mini: Yi-Large scores higher on the MMLU benchmark. Yi-Large leads general capability (MMLU 83.0% vs 82.0%). GPT-4o mini 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 Yi-Large scores 83.0% versus 82.0%. 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 GPT-4o mini handles up to 128K 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
Yi-Large 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 Yi-Large better than the GPT-4o mini?
GPT-4o mini is the clearly stronger overall choice, winning most of the dimensions that matter. Yi-Large leads general capability (MMLU 83.0% vs 82.0%).
What is the main difference between the Yi-Large and the GPT-4o mini?
Yi-Large leads general capability (MMLU 83.0% vs 82.0%). GPT-4o mini offers the larger context window, useful for long documents and codebases.
Which is better value?
GPT-4o mini offers more capability per dollar — a better value pick for high-volume use, delivering 7.90× the MMLU-per-cost of the alternative.
Which should I choose?
Choose the GPT-4o mini if you work with long documents or large codebases.
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.