How to use Yi-Large in production
A practical guide to yi-large, using data from the AI & LLM Index. The goal is a shortlist and a decision, not a tour of every option.
What Yi-Large is
Yi-Large is a multimodal model from 01.AI, released in 2025-08-10. Yi-Large is a multimodal model from 01.AI. It offers a 32,768-token context window, costs $3.40 per million input tokens and $13.35 per million output tokens, and averages 73.5 across the index's benchmark suite.
It is an open-weight model under a open weights licence. That determines whether you can self-host it, fine-tune it and keep data inside your own infrastructure.
Pricing and context
Yi-Large costs $3.40 per million input tokens, $13.35 per million output tokens and $0.40 per million cached input tokens. The blended 3:1 figure is about $5.89 per million tokens.
The context window is 32,768 tokens and the maximum output is 32,768 tokens. If your prompts regularly exceed the context window, you need chunking or retrieval before price becomes the main issue.
Benchmark position
Yi-Large averages 73.5 across the index's scored benchmarks, ranking #287 of 600. Its strongest relative result is Arena Elo.
Use the table below to see the full benchmark profile. A single strong score is less useful than a consistent profile across the benchmarks that resemble your task.
Alternatives
The closest models by average score are GPT-5.2 Nano, Yi-Large Chat, Llama 3.3 Groq Base. Compare their prices before assuming the closest score is the best substitute; a small quality drop can cut unit cost substantially.
Yi-Large benchmark profile
| Benchmark | Score | Rank | Percentile |
|---|---|---|---|
| MMLU-Pro | 77.1 | #274 | 54th |
| GPQA Diamond | 70.7 | #242 | 60th |
| SWE-bench Verified | 63.7 | #241 | 60th |
| HumanEval | 79.4 | #254 | 58th |
| MATH-500 | 76.2 | #295 | 51th |
| AIME 2025 | 66.2 | #327 | 46th |
| LiveCodeBench | 62.7 | #338 | 44th |
| IFEval | 81.2 | #304 | 49th |
Related
FAQ
Is Yi-Large worth it?
Yi-Large averages 73.5 and costs $3.40 per million input tokens. Whether it is worth it depends on whether your task benefits from its benchmark profile; test it against a cheaper alternative.
What is the most important factor in yi-large?
Fit to the task beats raw benchmark rank. Define the failure modes, pick the benchmarks that resemble them, then test a small shortlist on your own examples.
How many models should I evaluate?
Three is usually enough: a quality candidate, a value candidate and a fallback. More candidates add work without changing the decision.
How do I control cost?
Compare blended 3:1 price, watch output tokens, use caching where available and route simple requests to a cheaper model.