OpenAI o1 vs Yi-Large
Head-to-head specifications
| Metric | OpenAI o1 | Yi-Large | Difference |
|---|---|---|---|
| MMLU (general capability) | 92.3% | 83.0% | +9.3% |
| Context window | 128K tokens | 32K tokens | — |
| Price (input / output per 1M) | $15 / $60 | $3 / $3 | — |
| Access | Proprietary API | Proprietary API | — |
- OpenAI o1 leads general capability (MMLU 92.3% vs 83.0%).
- OpenAI o1 offers the larger context window, useful for long documents and codebases.
Verdict: OpenAI o1 or Yi-Large?
OpenAI o1 advantages
- General capability (+10%)
- Context window (+75%)
Yi-Large advantages
- Input cost (+80%)
- Output cost (+95%)
Which should you choose?
- Choose the OpenAI o1 if you need the strongest reasoning and accuracy.
- Choose the Yi-Large if you process large volumes of input and want the lowest cost.
- Choose the OpenAI o1 if you work with long documents or large codebases.
Value for money
Yi-Large offers more capability per dollar — a better value pick for high-volume use, delivering 11.24× the MMLU-per-cost of the alternative.
OpenAI o1 vs Yi-Large: which should you choose?
OpenAI o1 — OpenAI large language model (2024) with a 128K-token context window and an MMLU score of 92.3%.
Yi-Large — 01.AI large language model (2024) with a 32K-token context window and an MMLU score of 83.0%.
OpenAI o1 vs Yi-Large: OpenAI o1 scores higher on the MMLU benchmark. OpenAI o1 leads general capability (MMLU 92.3% vs 83.0%). OpenAI o1 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 OpenAI o1 scores 92.3% versus 83.0%. 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 OpenAI o1 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
OpenAI o1 is proprietary api and Yi-Large 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 OpenAI o1 better than the Yi-Large?
These two are closely matched — the right pick comes down to which specific strengths you value and the price you actually pay. OpenAI o1 leads general capability (MMLU 92.3% vs 83.0%).
What is the main difference between the OpenAI o1 and the Yi-Large?
OpenAI o1 leads general capability (MMLU 92.3% vs 83.0%). OpenAI o1 offers the larger context window, useful for long documents and codebases.
Which is better value?
Yi-Large offers more capability per dollar — a better value pick for high-volume use, delivering 11.24× the MMLU-per-cost of the alternative.
Which should I choose?
Choose the OpenAI o1 if you need the strongest reasoning and accuracy. Choose the Yi-Large 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.