Yi-Large Chat: pricing, context window and benchmarks
Yi-Large Chat is a multimodal model from 01.AI. It offers a 65,536-token context window, costs $3.49 per million input tokens and $13.97 per million output tokens, and averages 73.5 across the index's benchmark suite.
Pricing
Yi-Large Chat costs $3.49 per million input tokens and $13.97 per million output tokens. A blended 3:1 input-to-output mix works out to about $6.11 per million tokens. Cached input is listed at $0.46 per million tokens.
| Item | Price | Notes |
|---|---|---|
| Input | $3.49 | per 1M tokens |
| Output | $13.97 | per 1M tokens |
| Cached input | $0.46 | per 1M tokens |
| Blended 3:1 | $6.11 | estimate per 1M tokens |
| 1M in + 1M out | $17.46 | estimate |
Benchmarks
Scores below are modelled estimates on a 0–100 scale. Use them to compare relative position, not as official vendor results.
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 75.7 | 314 | 48th | |
| GPQA Diamond | 69.0 | 297 | 51th | |
| SWE-bench Verified | 59.3 | 338 | 44th | |
| HumanEval | 79.5 | 249 | 59th | |
| MATH-500 | 76.1 | 297 | 51th | |
| AIME 2025 | 71.0 | 238 | 60th | |
| LiveCodeBench | 62.7 | 339 | 44th | |
| IFEval | 82.4 | 262 | 56th | |
| TruthfulQA | 78.4 | 242 | 60th | |
| HellaSwag | 82.9 | 324 | 46th | |
| MMMU | 71.2 | 269 | 55th | |
| Arena Elo | 1386.2 | 262 | 56th |
What Yi-Large Chat is good for
Yi-Large Chat is an open-weight model, so it can be self-hosted or run through a third-party inference provider. That matters for data residency, fine-tuning and long-run cost control. At $3.49 per million input tokens (hosted list price) and a 65,536-token context window, it is a practical option for teams that want more control than a closed API allows.
Strengths
- Input price of $3.49 per million tokens is at or below the index median.
- Open weights make self-hosting, fine-tuning and data-residency review possible.
- Accepts more than text, including image input where the provider supports it.
Trade-offs
- No standout weakness in the indexed specifications, but real-world behaviour still needs testing.
How it compares
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| Gemma 3 | Google DeepMind | 65536 | $3.68 | 73.6 |
| GPT-5.2 Nano | OpenAI | 128000 | $9.73 | 73.5 |
| Llama 3.3 Groq Base | Groq | 16384 | $3.24 | 73.5 |
| Yi-Large | 01.AI | 32768 | $3.40 | 73.5 |
| Yi-Large Chat | 01.AI | 65536 | $3.49 | 73.5 |
| Nova Lite | Amazon | 65536 | $9.20 | 73.4 |
FAQ
How much does Yi-Large Chat cost?
Yi-Large Chat is listed at $3.49 per million input tokens and $13.97 per million output tokens. Cached input is $0.46 per million tokens. A blended 3:1 input-to-output mix is about $6.11 per million tokens.
What is the context window of Yi-Large Chat?
Yi-Large Chat has a context window of 65,536 tokens and can emit up to 16,384 output tokens per request in this index.
Is Yi-Large Chat open weights?
Yes. Yi-Large Chat is listed as an open-weight model under a open weights licence.
How does Yi-Large Chat score on benchmarks?
Yi-Large Chat averages 73.5 across the scored benchmarks in this index, ranking #288 of 600 models. Its strongest relative result is Arena Elo at 1386.2.
What is Yi-Large Chat best for?
Yi-Large Chat is an open-weight model, so it can be self-hosted or run through a third-party inference provider. That matters for data residency, fine-tuning and long-run cost control. At $3.49 per million input tokens (hosted list price) and a 65,536-token context window, it is a practical option for teams that want more control than a closed API allows.