Yi-Large 1.5B: pricing, context window and benchmarks
Yi-Large 1.5B is a small open-weight model from 01.AI. It offers a 8,192-token context window, costs $0.42 per million input tokens and $2.13 per million output tokens, and averages 46.8 across the index's benchmark suite.
Pricing
Yi-Large 1.5B costs $0.42 per million input tokens and $2.13 per million output tokens. A blended 3:1 input-to-output mix works out to about $0.85 per million tokens. Cached input is listed at $0.05 per million tokens.
| Item | Price | Notes |
|---|---|---|
| Input | $0.42 | per 1M tokens |
| Output | $2.13 | per 1M tokens |
| Cached input | $0.05 | per 1M tokens |
| Blended 3:1 | $0.85 | estimate per 1M tokens |
| 1M in + 1M out | $2.55 | 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 | 52.3 | 585 | 3th | |
| GPQA Diamond | 43.4 | 576 | 4th | |
| SWE-bench Verified | 33.3 | 568 | 5th | |
| HumanEval | 49.1 | 580 | 3th | |
| MATH-500 | 45.0 | 577 | 4th | |
| AIME 2025 | 30.0 | 583 | 3th | |
| LiveCodeBench | 35.8 | 565 | 6th | |
| IFEval | 59.6 | 576 | 4th | |
| TruthfulQA | 55.6 | 572 | 5th | |
| HellaSwag | 66.7 | 570 | 5th | |
| MMMU | 43.8 | 583 | 3th | |
| Arena Elo | 1176.6 | 578 | 4th |
What Yi-Large 1.5B is good for
Yi-Large 1.5B is a small model. That makes it a good fit for classification, extraction, routing, summarization and other high-volume jobs where latency and cost dominate. It is not the right pick for open-ended frontier reasoning, but at $0.42 per million input tokens it can handle a large share of production traffic.
Strengths
- Input price of $0.42 per million tokens is at or below the index median.
- Open weights make self-hosting, fine-tuning and data-residency review possible.
Trade-offs
- Context window of 8,192 tokens is small for long-document work.
- Average benchmark score is below the index median; test it on your own task.
- Less reliable on open-ended reasoning and complex multi-step instructions.
How it compares
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| Mixtral Turbo 1.5B | Together AI | 4096 | $0.31 | 47.6 |
| Nemotron Nano 7B | NVIDIA | 8192 | $0.35 | 47.5 |
| DBRX 1.5B | Databricks | 16384 | $0.32 | 47.0 |
| StableLM 2 8B | Stability AI | 32768 | $0.38 | 47.0 |
| Yi-Large 1.5B | 01.AI | 8192 | $0.42 | 46.8 |
| Mixtral Groq 1.5B | Groq | 4096 | $0.34 | 46.1 |
FAQ
How much does Yi-Large 1.5B cost?
Yi-Large 1.5B is listed at $0.42 per million input tokens and $2.13 per million output tokens. Cached input is $0.053 per million tokens. A blended 3:1 input-to-output mix is about $0.85 per million tokens.
What is the context window of Yi-Large 1.5B?
Yi-Large 1.5B has a context window of 8,192 tokens and can emit up to 8,192 output tokens per request in this index.
Is Yi-Large 1.5B open weights?
Yes. Yi-Large 1.5B is listed as an open-weight model under a open weights licence.
How does Yi-Large 1.5B score on benchmarks?
Yi-Large 1.5B averages 46.8 across the scored benchmarks in this index, ranking #580 of 600 models. Its strongest relative result is Arena Elo at 1176.6.
What is Yi-Large 1.5B best for?
Yi-Large 1.5B is a small model. That makes it a good fit for classification, extraction, routing, summarization and other high-volume jobs where latency and cost dominate. It is not the right pick for open-ended frontier reasoning, but at $0.42 per million input tokens it can handle a large share of production traffic.