README.md

---
title: Interfaze
thumbnail: >-
  https://cdn-uploads.huggingface.co/production/uploads/62fb53b572a7ab50b4b06fca/II4KdeJkepE_NOg9HNnq_.jpeg
short_description: The AI model built for deterministic developer tasks
---

![Extraction](https://interfaze.ai/banner.png)

# The AI model built for deterministic developer tasks

Interfaze is an AI model built on a new architecture that merges specialized DNN/CNN models with LLMs for developer tasks that require deterministic output and high consistency like OCR, scraping, classification, web search and more.


[Try now](https://interfaze.ai/dashboard) or [Read paper](https://www.arxiv.org/abs/2602.04101)

- OCR, web scraping, web search, classification and more
- OpenAI chat completion API compatible
- High accuracy structured output consistency
- Built-in code execution and sandboxing
- Custom web engine for scraping and web research capabilities
- Auto reasoning when needed
- Controllable guardrails
- Fully managed and scalable
- Globally distributed fallback system with high uptime

### Model Comparison

| Benchmark | interfaze-beta | GPT-4.1 | Claude Sonnet 4 | Gemini 2.5 Flash | Claude Sonnet 4 (Thinking) | Claude Opus 4 (Thinking) | GPT-5-Minimal | Gemini-2.5-Pro |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| MMLU-Pro | 83.6 | 80.6 | 83.7 | 80.9 | 83.7 | 86 | 80.6 | 86.2 |
| MMLU | 91.38 | 90.2 | - | - | 88.8 | 89 | - | 89.2 |
| MMMU | 77.33 | 74.8 | - | 79.7 | 74.4 | 76.5 | - | 82 |
| AIME-2025 | 90 | 34.7 | 38 | 60.3 | 74.3 | 73.3 | 31.7 | 87.7 |
| GPQA-Diamond | 81.31 | 66.3 | 68.3 | 68.3 | 77.7 | 79.6 | 67.3 | 84.4 |
| LiveCodeBench | 57.77 | 45.7 | 44.9 | 49.5 | 65.5 | 63.6 | 55.8 | 75.9 |
| ChartQA | 90.88 | - | - | - | - | - | - | - |
| AI2D | 91.51 | 85.9 | - | - | - | - | - | 89.5 |
| Common-Voice-v16 | 90.8 | - | - | - | - | - | - | - |

\*Results for Non-Interfaze models are sourced from model providers, leaderboards, and evaluation providers such as Artificial Analysis.

### Works like any other LLM

OpenAI API compatible, works with every AI SDK out of the box

```
import OpenAI from "openai";

const interfaze = new OpenAI({
    baseURL: "https://api.interfaze.ai/v1",
    apiKey: "<your-api-key>"
});

const completion = await interfaze.chat.completions.create({
    model: "interfaze-beta",
    messages: [\
        {\
            role: "user",\
            content: "Get the company description of JigsawStack from their linkedin page",\
        },\
    ],
});

console.log(completion.choices[0].message.content);
```

### OCR & Document Extraction

[vision docs ->](https://interfaze.ai/docs/vision)

```
prompt = "Get the person information from the following ID."

schema = z.object({
  first_name: z.string(),
  last_name: z.string(),
  dob: z.string(),
  expiry: z.string(),
});
```

![Extraction](https://interfaze.ai/examples/extraction_example.png)

### Smart Web Scraping

[web docs ->](https://interfaze.ai/docs/web)

```
prompt = "Extract the information from Yoeven D Khemlani's linkedin page based on the schema."

schema = z.object({
  first_name: z.string(),
  last_name: z.string(),
  about: z.string(),
  current_company: z.string(),
  current_position: z.string(),
});
```

![scraping](https://interfaze.ai/examples/scraper_example.png)

### Translation

[translation docs ->](https://interfaze.ai/docs/translation)

```
prompt = "The UK drinks about 100–160 million cups of tea every day, and 98% of tea drinkers add milk to their tea."

schema = z.object({
  zh: z.string(),
  hi: z.string(),
  es: z.string(),
  fr: z.string(),
  de: z.string(),
  it: z.string(),
  ja: z.string(),
  ko: z.string(),
});
```

```
zh: 英国每天饮用约100–160百万杯茶，有98%的茶饮者在茶中加入牛奶。
hi: यूके हर दिन लगभग 100–160 मिलियन कप चाय पीता है, और 98% चाय पीने वाले अपनी चाय में दूध मिलाते हैं।
es: El Reino Unido bebe alrededor de 100–160 millones de tazas de té cada día, y el 98 % de los consumidores de té añade leche a su té.
fr: Le Royaume-Uni boit environ 100–160 millions de tasses de thé chaque jour, et 98 % des buveurs de thé ajoutent du lait à leur thé.
de: Das Vereinigte Königreich trinkt etwa 100–160 Millionen Tassen Tee pro Tag, und 98 % der Teetrinker fügen ihrem Tee Milch hinzu.
it: Il Regno Unito beve circa 100–160 milioni di tazze di tè ogni giorno e il 98% degli amanti del tè aggiunge latte al proprio tè.
ja: イギリスでは毎日約100～160百万杯の紅茶が飲まれており、紅茶を飲む人の98%が紅茶に牛乳を加えます。
ko: 영국에서는 매일 약 1억 ~ 1억 6천만 잔의 차를 마시며, 차를 마시는 사람의 98%가 차에 우유를 넣습니다.
```

### Speech-to-text (STT) and diarization

[stt docs ->](https://interfaze.ai/docs/speech-to-text)

```
prompt = "Transcribe https://jigsawstack.com/preview/stt-example.wav"

schema = z.object({
  text: z.string(),
  speakers: z.object({
    id: z.string(),
    start: z.number(),
    end: z.number()
  })
});
```

```
{
  "text": " The little tales they tell are false The door was barred, locked and bolted as well Ripe pears are fit for a queen's table A big wet stain was on the round carpet The kite dipped and swayed but stayed aloft The pleasant hours fly by much too soon The room was crowded with a mild wob The room was crowded with a wild mob This strong arm shall shield your honour She blushed when he gave her a white orchid The beetle droned in the hot June sun",
  "speakers": [\
    {\
      "start":0,\
      "end":4.78,\
      "id": "SPEAKER_00"\
    },\
    {\
      "start":4.78,\
      "end":9.48,\
      "id": "SPEAKER_00"\
    },\
    {\
      "start":9.48,\
      "end":13.06,\
      "id": "SPEAKER_00"\
    },\
    {\
      "start":13.06,\
      "end":17.24,\
      "id": "SPEAKER_00"\
    },\
    {\
      "start":17.24,\
      "end":21.78,\
      "id": "SPEAKER_00"\
    },\
    {\
      "start":21.78,\
      "end":26.3,\
      "id": "SPEAKER_00"\
    },\
    {\
      "start":26.3,\
      "end":30.76,\
      "id": "SPEAKER_00"\
    },\
    {\
      "start":30.76,\
      "end":35.08,\
      "id": "SPEAKER_00"\
    },\
    {\
      "start":35.08,\
      "end":39.24,\
      "id": "SPEAKER_00"\
    },\
    {\
      "start":39.24,\
      "end":43.94,\
      "id": "SPEAKER_00"\
    },\
    {\
      "start":43.94,\
      "end":48.5,\
      "id": "SPEAKER_00"\
    }\
  ]
}
```

### Configurable guardrails and NSFW checks

[guardrails docs ->](https://interfaze.ai/docs/guard-rails)

Fully configurable guardrails for text and images

```
S1: Violent Crimes
S2: Non-Violent Crimes
S3: Sex-Related Crimes
S4: Child Sexual Exploitation
S5: Defamation
S6: Specialized Advice
S7: Privacy
S8: Intellectual Property
S9: Indiscriminate Weapons
S10: Hate
S11: Suicide & Self-Harm
S12: Sexual Content
S12_IMAGE: Sexual Content (Image)
S13: Elections
S14: Code Interpreter Abuse
```

### Architecture

[read paper ->](https://www.arxiv.org/abs/2602.04101)

This architecture combines a suite of small specialized models supported with custom tools and infrastructure while automatically routing to the best model for the task that prioritizes accuracy and speed.

![How it works](https://interfaze.ai/examples/howitworks.png)

### Specs

- Context window: 1m tokens
- Max output tokens: 32k tokens
- Input modalities: Text, Images, Audio, File, Video
- Reasoning: Available

### Research references

- [Interfaze: The Future of AI is built on Task-Specific Small Models](https://www.arxiv.org/abs/2602.04101)
- [Agentic Context Engineering](https://www.arxiv.org/pdf/2510.04618)
- [Small Language Models are the Future of Agentic AI](https://arxiv.org/pdf/2506.02153)
- [The Sparsely-Gated Mixture-of-Experts Layer](https://arxiv.org/pdf/1701.06538)
- [DeepSeekMoE](https://arxiv.org/pdf/2401.06066)
- [Confronting LLMs with Traditional ML](https://arxiv.org/pdf/2310.14607)

### Who are we?

We are a small team of ML, Software and Infrastructure engineers engrossed in the fact that a small model can do a lot more when specialized. Allowing us to make AI available in every dev workflow.