# Rio 3.5 Open 397B

URL: https://interfaze.ai/models/prefeitura-riorio-35-open-397b

Rio 3.5 Open 397B by prefeitura-rio, a image-text-to-text model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities.

## Comparison

| Feature | Rio 3.5 Open 397B | Interfaze |
| --- | --- | --- |
| Input Modalities | text, image, video | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | Yes | Yes |
| Language Support | 201 partial | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | 1M | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | Rio 3.5 Open 397B | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)

View model card on [Hugging Face](https://huggingface.co/prefeitura-rio/Rio-3.5-Open-397B)

![Rio 3.5 Open 397B benchmark results](https://huggingface.co/prefeitura-rio/Rio-3.5-Open-397B/resolve/main/rio-3.5-open-benchmarks.png)

**Rio 3.5 Open 397B** is a frontier-class general-purpose AI model developed by [IplanRIO](https://iplanrio.rio.rj.gov.br/), the municipal IT company of Rio de Janeiro's city government. Post-trained from Qwen 3.5 397B, Rio 3.5 Open 397B delivers state-of-the-art open-model performance across agentic coding, mathematics, STEM, multilingual, and multimodal benchmarks — surpassing its base model by significant margins and competing with the world's best open and proprietary models.

Rio 3.5 Open 397B features **SwiReasoning**, a training-free inference framework based on [Shi et al. (2025)](https://arxiv.org/abs/2510.05069) that dynamically switches between explicit chain-of-thought and latent-space reasoning, guided by entropy-based confidence signals. This enables both higher accuracy and dramatically improved token efficiency. This model was explicitly trained to maximize the efficiency gained via latent reasoning.

## Key Features

-   **397B total / 17B active parameters** (Mixture-of-Experts)
-   **1,010,000 token (1M) context window**
-   **SwiReasoning integration** — dynamic explicit/latent reasoning switching for Pareto-superior accuracy and efficiency
-   **General-purpose** — strong agentic coding, reasoning, instruction-following, and multimodal performance
-   **Post-trained from Qwen 3.5 397B**
-   **Multilingual** — strong performance in Portuguese, English, Chinese, and dozens of other languages
-   **MIT License** — fully open for commercial and research use

## Benchmark Results

### Agentic Coding & Software Engineering

| Benchmark | Rio 3.5 Open 397B | Qwen 3.5 397B (base) | Qwen 3.7 Plus | DeepSeek V4 Pro | Kimi-K2.6 | GPT 5.5 |
| --- | --- | --- | --- | --- | --- | --- |
| Terminal-Bench 2.1 | 70.8 | 52.5 | 70.3 | 67.9 | 66.7 | 78.2 |
| DeepSWE | 23.0 | 6.0 | – | 8.0 | 24.0 | 70.0 |
| SWE-Bench Pro | 58.1 | 50.9 | 57.6 | 59.0 | 59.5 | 58.6 |
| SWE-Bench Verified | 80.2 | 76.2 | 77.7 | 80.6 | 80.2 | 82.9 |
| SWE-Bench Multilingual | 77.0 | 69.3 | 75.8 | 76.2 | 76.7 | – |

### Knowledge & Reasoning

| Benchmark | Rio 3.5 Open 397B | Qwen 3.5 397B (base) | Qwen 3.7 Plus | DeepSeek V4 Pro | Kimi-K2.6 | GPT 5.5 |
| --- | --- | --- | --- | --- | --- | --- |
| GPQA Diamond | 90.9 | 88.4 | 90.3 | 90.1 | 90.5 | 93.6 |
| HLE | 36.5 | 28.7 | 34.7 | 37.7 | 36.4 | 41.4 |
| MMLU-Pro | 88.0 | 87.8 | 88.5 | 87.5 | 87.1 | – |
| MMLU-Redux | 94.6 | 94.9 | 94.5 | 94.8 | 95.3 | – |
| SuperGPQA | 72.3 | 70.4 | 71.4 | 69.9 | 71.3 | – |
| Apex | 29.2 | 9.4 | 22.7 | 38.3 | 24.0 | 80.2 |

### Mathematics

| Benchmark | Rio 3.5 Open 397B | Qwen 3.5 397B (base) | Qwen 3.7 Plus | DeepSeek V4 Pro | Kimi-K2.6 | GPT 5.5 |
| --- | --- | --- | --- | --- | --- | --- |
| HMMT 2026 Feb | 93.9 | 87.9 | 92.9 | 95.2 | 92.7 | 98.5 |
| IMOAnswerBench | 89.5 | 80.9 | 86.0 | 89.8 | 86.0 | – |

### Multilingual

| Benchmark | Rio 3.5 Open 397B | Qwen 3.5 397B (base) | Qwen 3.7 Plus | DeepSeek V4 Pro | Kimi-K2.6 | GPT 5.5 |
| --- | --- | --- | --- | --- | --- | --- |
| MMMLU | 89.8 | 88.5 | 89.0 | 87.9 | 87.5 | – |
| MMLU-ProX | 85.6 | 84.7 | 85.4 | 83.9 | 83.7 | – |

### Multimodal

| Benchmark | Rio 3.5 Open 397B | Qwen 3.5 397B (base) | Qwen 3.7 Plus | DeepSeek V4 Pro | Kimi-K2.6 | GPT 5.5 |
| --- | --- | --- | --- | --- | --- | --- |
| MMMU-Pro | 78.4 | 79.0 | 79.0 | – | 79.4 | 81.2 |
| MathVision | 89.1 | 88.6 | 90.3 | – | 87.4 | – |
| VideoMMMU | 81.6 | 84.7 | 85.4 | – | – | 86.4 |

### Agents & Instruction Following

| Benchmark | Rio 3.5 Open 397B | Qwen 3.5 397B (base) | Qwen 3.7 Plus | DeepSeek V4 Pro | Kimi-K2.6 | GPT 5.5 |
| --- | --- | --- | --- | --- | --- | --- |
| MCP-Atlas | 74.2 | 74.2 | 73.2 | 73.6 | 66.6 | 75.3 |
| IFBench | 78.4 | 76.5 | 79.1 | 77.0 | 76.0 | 76.0 |
| IFEval | 93.4 | 92.6 | 94.6 | 91.9 | 94.5 | – |

### Economic Value

| Benchmark | Rio 3.5 Open 397B | Qwen 3.5 397B (base) | Qwen 3.7 Plus | DeepSeek V4 Pro | Kimi-K2.6 | GPT 5.5 |
| --- | --- | --- | --- | --- | --- | --- |
| GDPval (estimated) | 1533 | 1200 | 1520 | 1554 | 1482 | 1769 |

### Gains Over Base Model (Qwen 3.5 397B)

| Benchmark | Base Model | Rio 3.5 Open 397B | Δ |
| --- | --- | --- | --- |
| Terminal-Bench 2.1 | 52.5 | 70.8 | +18.3 |
| DeepSWE | 6.0 | 23.0 | +17.0 |
| SWE-Bench Pro | 50.9 | 58.1 | +7.2 |
| SWE-Bench Verified | 76.2 | 80.2 | +4.0 |
| SWE-Bench Multilingual | 69.3 | 77.0 | +7.7 |
| GPQA Diamond | 88.4 | 90.9 | +2.5 |
| HLE | 28.7 | 36.5 | +7.8 |
| HMMT 2026 Feb | 87.9 | 93.9 | +6.0 |
| IMOAnswerBench | 80.9 | 89.5 | +8.6 |
| Apex | 9.4 | 29.2 | +19.8 |
| GDPval (estimated) | 1200 | 1533 | +333 |

## SwiReasoning: Latent/Explicit Reasoning

Rio 3.5 Open 397B integrates [SwiReasoning](https://arxiv.org/abs/2510.05069) (Shi et al., 2025), a training-free inference framework that dynamically alternates between two reasoning modes:

-   **Explicit reasoning** — standard chain-of-thought in natural language, where the model commits tokens to a single reasoning path
-   **Latent reasoning** — continuous reasoning in hidden space, where the model explores multiple implicit paths simultaneously without emitting tokens

The switching is governed by **block-wise confidence** estimated from entropy trends in the next-token distribution. When confidence is low (entropy trending upward), the model enters latent mode to explore alternatives. When confidence recovers, it switches back to explicit mode to commit to a solution.

This approach achieves a **Pareto-superior** trade-off: higher accuracy at unlimited budgets _and_ dramatically better token efficiency under constrained budgets. As with previous Rio generations, the model was post-trained to maximize the gains obtained from latent reasoning.

## How to Use

```
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prefeitura-rio/Rio-3.5-Open-397B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a poem about Rio de Janeiro."

messages = [
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=81920,
    temperature=0.6,
    top_p=0.95,
)

response = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
print(response)
```

### Using with vLLM

```
vllm serve prefeitura-rio/Rio-3.5-Open-397B \
    --tensor-parallel-size 8 \
    --max-model-len 1048576 \
    --trust-remote-code
```

### Using with SGLang

```
python -m sglang.launch_server \
    --model-path prefeitura-rio/Rio-3.5-Open-397B \
    --tp 8 \
    --context-length 1048576 \
    --trust-remote-code
```

## Model Details

|  |  |
| --- | --- |
| Developer | IplanRIO — Empresa Municipal de Informática e Planejamento S.A. |
| Base Model | Qwen 3.5 397B |
| Architecture | Mixture-of-Experts (MoE) Transformer |
| Total Parameters | ~397B |
| Active Parameters | ~17B |
| Context Length | 1,010,000 tokens (1M) |
| Training Method | Post-training |
| Inference Enhancement | SwiReasoning (latent/explicit switching) |
| License | MIT |
| Languages | Multilingual (en, pt, zh, ja, ko, fr, de, es, ar, and more) |

## Citation

If you use SwiReasoning, please also cite:

```
@misc{shi2025swireasoning,
    title={SwiReasoning: Switch-Thinking in Latent and Explicit for Pareto-Superior Reasoning LLMs},
    author={Dachuan Shi et al.},
    year={2025},
    eprint={2510.05069},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

## Acknowledgments

Rio 3.5 Open 397B is built upon the exceptional work of the [Qwen Team](https://github.com/QwenLM) and their Qwen 3.5 model family. We also acknowledge the authors of [SwiReasoning](https://github.com/sdc17/SwiReasoning) for their innovative inference framework.

Developed in Rio de Janeiro 🇧🇷 by [IplanRIO](https://iplanrio.rio.rj.gov.br/).

## Want more deterministic results?

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