Yi-Large Base: pricing, context window and benchmarks
Yi-Large Base is a open-weight model from 01.AI. It offers a 16,384-token context window, costs $2.78 per million input tokens and $10.84 per million output tokens, and averages 70.3 across the index's benchmark suite.
Pricing
Yi-Large Base costs $2.78 per million input tokens and $10.84 per million output tokens. A blended 3:1 input-to-output mix works out to about $4.79 per million tokens. Cached input is listed at $0.23 per million tokens.
| Item | Price | Notes |
|---|---|---|
| Input | $2.78 | per 1M tokens |
| Output | $10.84 | per 1M tokens |
| Cached input | $0.23 | per 1M tokens |
| Blended 3:1 | $4.79 | estimate per 1M tokens |
| 1M in + 1M out | $13.62 | 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 | 75.4 | 322 | 46th | |
| GPQA Diamond | 68.0 | 312 | 48th | |
| SWE-bench Verified | 56.8 | 370 | 38th | |
| HumanEval | 73.3 | 369 | 39th | |
| MATH-500 | 71.2 | 378 | 37th | |
| AIME 2025 | 60.4 | 386 | 36th | |
| LiveCodeBench | 60.6 | 361 | 40th | |
| IFEval | 80.4 | 327 | 46th | |
| TruthfulQA | 75.9 | 311 | 48th | |
| HellaSwag | 83.0 | 322 | 46th | |
| MMMU | 68.6 | 333 | 45th | |
| Arena Elo | 1353.7 | 357 | 41th |
What Yi-Large Base is good for
Yi-Large Base 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 $2.78 per million input tokens (hosted list price) and a 16,384-token context window, it is a practical option for teams that want more control than a closed API allows.
Strengths
- Input price of $2.78 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
- Context window of 16,384 tokens is small for long-document work.
- Average benchmark score is below the index median; test it on your own task.
How it compares
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| GPT-4.1 Nano | OpenAI | 32768 | $7.42 | 70.5 |
| Firesearch Pro | Fireworks AI | 32768 | $7.67 | 70.4 |
| Llama 3 Novita Base | Novita AI | 8192 | $3.06 | 70.3 |
| Sonar Pro Mini | Perplexity | 8192 | $8.02 | 70.3 |
| Yi-Large Base | 01.AI | 16384 | $2.78 | 70.3 |
| Auto | OpenRouter | 32768 | $7.53 | 70.2 |
FAQ
How much does Yi-Large Base cost?
Yi-Large Base is listed at $2.78 per million input tokens and $10.84 per million output tokens. Cached input is $0.23 per million tokens. A blended 3:1 input-to-output mix is about $4.79 per million tokens.
What is the context window of Yi-Large Base?
Yi-Large Base has a context window of 16,384 tokens and can emit up to 16,384 output tokens per request in this index.
Is Yi-Large Base open weights?
Yes. Yi-Large Base is listed as an open-weight model under a open weights licence.
How does Yi-Large Base score on benchmarks?
Yi-Large Base averages 70.3 across the scored benchmarks in this index, ranking #361 of 600 models. Its strongest relative result is Arena Elo at 1353.7.
What is Yi-Large Base best for?
Yi-Large Base 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 $2.78 per million input tokens (hosted list price) and a 16,384-token context window, it is a practical option for teams that want more control than a closed API allows.