兄弟们，我后背没有发凉。 但，我看到这个模型架构后高兴不已！ 大家还在疯狂堆参数、卷通用大模型的时候，Interfaze直接用一个全新混合架构。 把OCR、视觉、STT、结构化输出这些确定性任务的准确率干到了吊打Gemini-3-Flash、Claude-Sonnet-4.6、GPT-5.4-Mini和Grok-4.3的程度。 他们把任务专用的DNN/CNN编码器和全能transformer融合在一起，实现了真正的“专精+灵活”：CNN负责极致准确和元数据（边界框、置信度）。 transformer负责理解和推理，还能通过标签只激活部分模型，速度和成本直接起飞。 9个硬核基准上全面领先，而且在OCR、视觉、音频这些高频场景里，速度和性价比完胜通用大模型。 这才是最被低估的真相：未来很多真实生产力任务，根本不需要越来越大的通用模型，而是需要这种“专为确定性任务而生”的混合架构。 Interfaze把AI从“万能但贵且慢”拉回了“又准又快又便宜”的现实路径。 PS： 我觉得需要和我之前的ocr测试的用例看看一起再测试一下结果怎么样，是不是如榜单所描述！ 完整博客值得立刻读一遍 https://interfaze.ai/blog/interfaze-a-new-model-architecture-built-for-high-accuracy-at-scale…

Interfaze: A new model architecture built for high accuracy at scale

Source: https://interfaze.ai/blog/interfaze-a-new-model-architecture-built-for-high-accuracy-at-scale tl;dr: Interfaze is a new model architecture that outperforms models like Gemini-3-Flash, Claude-Sonnet-4.6, GPT-5.4-Mini, and Grok-4.3 across 9 head-to-head benchmarks in OCR, vision, STT, and structured output.

Humans are inefficient at computer-level tasks. We make mistakes, but we’re great at decision-making and understanding nuance.

Imagine telling a human to read a 50-page PDF, map every word to another document with its XY position, and translate the whole thing into Chinese. You’d get tons of mistakes, pay a lot to keep that human on payroll, and wait a long time for the result.

Transformer models are similar. They’re amazing at nuance and human-level tasks, and they make mistakes like a human, but that’s also what keeps them creative.

We’ve been using the wrong models for the wrong tasks.

CNNs/DNNs have existed since the early 90s, from LeNet-5 to ResNet, and more recently CRNN-CTC.

These are deep neural network architectures that are task-specific for things like OCR, translation, or GUI detection. The way they consume and see data is trained to be task specific, which makes them up to 100x more accurate at their specific task. They also produce useful metadata like bounding boxes and confidence scores, letting developers build predictable workflows they can rely on.

So why do so many of us still go for transformers/LLMs for deterministic tasks?

DNNs are not flexible. They’re only as good as their training data, and they aren’t great at human-level nuance.

They might be cheap to serve but expensive to maintain and retrain for new tasks. Take a passport: a CNN can extract the date of birth with bounding boxes and a confidence score, but it can’t calculate the person’s age.

Introducing Interfaze

A new model architecture that merges the specialization of DNN/CNN models with omni-transformers, giving you the best of both worlds.

That means high accuracy and low cost on deterministic tasks:

• Vision (image and document, object and GUI detection)
• Web extraction and search
• Audio (STT and speaker diarization)
• Translation
• Video (coming soon)

Model specs

FeatureValueContext window1M tokensMax output tokens32k tokensInput modalitiesText, Images, Audio, FileReasoningAvailable (default: disabled)

Benchmark

While Pro tier models like Claude Opus 4.7 and GPT 5.5 are the best generalist models in the market today for things like coding and complex reasoning tasks, they aren’t commonly used for high volume tasks like OCR or translation due to high cost and slow response times.

Interfaze is benchmarked against models in similar pricing tiers and feature sets that are optimized to squeeze the most performance out of the model at the fastest speed, while keeping cost low at scale.

Today, most people reach for two model categories for deterministic developer tasks:

• Flash/mini modelslike Gemini-3-Flash, GPT-5.4-Mini and Claude Sonnet 4.6. The best balance you can get between performance and price at scale.
• Specialized providerslike Reducto, Mistral OCR or Whisper.

Breakdown

BenchmarkInterfazeGemini-3-FlashClaude-Sonnet-4.6GPT-5.4-MiniGrok-4.3**OCRBench V270.7%**55.8%54.7%52.7%54.7%**olmOCR85.7%**75.3%73.9%80.1%81.9%**RefCOCO82.1%**75.2%75.5%67.0%25.0%VoxPopuli (WER)↓**2.4%**4.0%———**Spider 2.0-Lite52.9%**45.2%49.6%26.7%45.9%**GPQA Diamond89.9%**88.5%**89.9%**82.8%73.6%**MMMLU90.9%**88.7%84.9%75.3%89.7%**MMMU-Pro71.1%**67.6%46.3%40.4%68.7%**SOB Value Acc79.5%**77.3%77.9%75.1%78.4% ↓ = lower is better (word error rate). — = not scored (model has no native audio input). All other rows: higher is better.

Each model is compared head-to-head across nine benchmarks: OCRBench V2, olmOCR, RefCOCO, VoxPopuli-Cleaned-AA, SOB Value, Spider-2.0-Lite, GPQA Diamond, MMMLU, and MMMU-Pro.

View the full leaderboard →

Interfaze leads in almost every benchmark, against both specialized models in each category and the generalist flash/mini models.

Our goal isn’t to replace LLMs. It’s to specialize in deterministic tasks. The benchmarks focus on categories like OCR, object detection, and structured output, with a few general benchmarks like GPQA Diamond to show the level of problem-solving and understanding you’d expect from any transformer model.

Interfaze is priced in a similar range as Gemini-3-Flash, at**1\.50 per million input tokens**and**3.50 per million output tokens**.

OCR is our number one use case

Our number one use case from users has been OCR for images and complex, long PDFs.

Interfaze outperforms OCR providers like Chandra OCR and Reducto, and generalist models like Gemini-3-Flash and GPT-5.4-Mini.

It isn’t just the task-specific CNN encoder doing a good job. It’s the ability to lean on object detection for figures and graphics, or lean on the translation layers of the transformer all in a shared vector space.

View full olmOCR benchmarks →

Structured output is a big part of determinism

Most LLMs today are great at following a JSON schema, but pretty bad at filling it with accurate values. No public benchmark measures the accuracy of those values, so we releasedSOB (the Structured Output Benchmark)last week.

**TL;DR:**SOB gives the model the correct answer in its context, then asks it to generate a JSON output with data it already has. We measure who is the most accurate, with the fewest mistakes and hallucinations, across text, image, and audio modalities (all normalized to text).

Compared against the same flash/mini set used throughout this post. See thefull SOB leaderboardfor all 28 models, including frontier Pro-tier models like Gemini-3.1-Pro, GPT-5.5, and Claude-Opus-4.7.

There’s still huge room for improving structured output without raising cost or compute. Follow us onXorLinkedInto follow our research journey.

Multilingual performance beyond English

Interfaze has great multilingual performance across a wide range of languages.

View full MMMLU benchmarks →

Speech-to-text on par with specialized ASR providers

On VoxPopuli-Cleaned-AA, Interfaze comes in second on word error rate.

Speech-to-text inference speed

Interfaze transcribes 209 seconds of audio per second of compute, ~1.5× faster than Deepgram Nova-3, ~8× faster than Scribe v2, and over 11× faster than Gemini-3-Flash.

View full VoxPopuli benchmarks →

Here’s how you get started

Set up your SDK

Interfaze speaks the Chat Completions API standard, so any AI SDK that supports OpenAI works out of the box: just point it athttps://api\.interfaze\.ai/v1. Grab your API key from theInterfaze dashboardand drop it in.

OpenAI SDK

Vercel AI SDK

LangChain SDK

import OpenAI from "openai";

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

The sameinterfazeclient is reused in every example below.

Read the full setup guide →

Complex OCR + object detection

A magazine page with dense multi-column text and three illustrations. Interfaze runs OCR and object detection on the same image in one request, returning the full text plus pixel-coordinates for every figure, all under your schema.

OpenAI SDK

Vercel AI SDK

LangChain SDK

import { z } from "zod";
import { zodResponseFormat } from "openai/helpers/zod";

const OCRObjectDetectionSchema = z.object({
	text: z.string().describe("all text in the image"),
	graphic_objects: z
		.array(
			z.object({
				description: z.string(),
				top_left_x: z.number(),
				top_left_y: z.number(),
				bottom_right_x: z.number(),
				bottom_right_y: z.number(),
			})
		)
		.describe("graphics objects found in the image"),
});

const response = await interfaze.chat.completions.create({
	model: "interfaze-beta",
	messages: [
		{
			role: "user",
			content: [
				{ type: "text", text: "Extract the text and graphics from the image based on the schema." },
				{
					type: "image_url",
					image_url: {
						url: "https://r2public.jigsawstack.com/interfaze/examples/dense_text_ocr_figures.png",
					},
				},
			],
		},
	],
	response_format: zodResponseFormat(OCRObjectDetectionSchema, "ocr_object_detection_schema"),
});

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

//@ts-expect-error precontext is not typed
const precontext = response.precontext;
console.log("OCR bounding boxes + confidence:", precontext[0]?.result);

JSON output

objectcarries the schema response: full page text plus agraphic\_objectsarray with a description and pixel coordinates for each illustration.precontextcarries the raw OCR (per-line and per-word bounding boxes, confidence scores) on the same response.

{
  "object": {
    "text": "cane stopped on the corner and yelled... acter named Dick Manly. He was so observant... STOMPING GROUND ... \"The Adding Machine,\" from 1923, is about Mr. Zero, a repressed number cruncher who gets replaced by an adding machine... 12 THE NEW YORKER, APRIL 27, 2026",
    "graphic_objects": [
      {
        "description": "A drawing located at the top left under the \"STOMPING GROUND\" heading, featuring a cityscape with a moon and a whimsical character.",
        "top_left_x": 84,
        "top_left_y": 484,
        "bottom_right_x": 394,
        "bottom_right_y": 630
      },
      {
        "description": "A detailed line drawing of Daphne Rubin-Vega in front of a building facade, matching the main profile story.",
        "top_left_x": 77,
        "top_left_y": 1367,
        "bottom_right_x": 517,
        "bottom_right_y": 1878
      },
      {
        "description": "A drawing in the bottom right corner depicting a person interacting with a device, situated above the spray-on condom text.",
        "top_left_x": 985,
        "top_left_y": 1581,
        "bottom_right_x": 1264,
        "bottom_right_y": 1737
      }
    ]
  },
  "precontext": [
    {
      "name": "ocr",
      "result": {
        "extracted_text": "cane stopped on the corner and yelled he wrote science fiction-and observant. acter named Dick Manly. He was so\nout, \"What is that?\" \"I remember my mother coming home...",
        "sections": [
          {
            "lines": [
              {
                "text": "cane stopped on the corner and yelled he wrote science fiction-and observant. acter named Dick Manly. He was so",
                "bounds": {
                  "top_left": { "x": 83, "y": 80 },
                  "top_right": { "x": 1406, "y": 78 },
                  "bottom_right": { "x": 1406, "y": 111 },
                  "bottom_left": { "x": 83, "y": 110 },
                  "width": 1323,
                  "height": 30
                },
                "average_confidence": 0.99
              }
              // ... hundreds more lines with per-word boxes and confidences
            ]
          }
        ]
      }
    }
  ]
}

OCR docs →

OCR with partial model activation

With our hybrid architecture, you can activate parts of the model to run a specific task without using the full weights.

It’s faster and cheaper, with some tradeoffs, you get a fixed structured output that’s deterministic and consistent on every run, and you can only run one task per request.

Using the<task\>tag in the system prompt, you control which part of the model activates. Below, we run pure OCR on a handwritten poem.

OpenAI SDK

Vercel AI SDK

LangChain SDK

import { z } from "zod";
import { zodResponseFormat } from "openai/helpers/zod";

const response = await interfaze.chat.completions.create({
	model: "interfaze-beta",
	messages: [
		{ role: "system", content: "<task>ocr</task>" },
		{
			role: "user",
			content: [
				{ type: "text", text: "Extract all text from this image" },
				{
					type: "image_url",
					image_url: {
						url: "https://r2public.jigsawstack.com/interfaze/examples/handwriting.jpeg",
					},
				},
			],
		},
	],
	response_format: zodResponseFormat(z.any(), "empty_schema"),
});

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

JSON output

The response is the raw task result withnameandresult, ready to consume directly.

{
  "name": "ocr",
  "result": {
    "extracted_text": "The lovely Song night may song linen shined\nWelcome and faint wei my heart was beating\nthe reseach on the moon the violet beautifull\nThe artist's evening song our love new life\n...",
    "sections": [
      {
        "lines": [
          {
            "text": "The lovely Song night may song linen shined",
            "bounds": {
              "top_left": { "x": 27, "y": 22 },
              "top_right": { "x": 422, "y": 21 },
              "bottom_right": { "x": 423, "y": 47 },
              "bottom_left": { "x": 27, "y": 51 },
              "width": 395.5,
              "height": 27.5
            },
            "average_confidence": 0.78
          }
          // ... more lines with per-word boxes and confidences
        ]
      }
    ]
  }
}

Learn more about running tasks →

Accessing the internet

Interfaze comes built in with its own web index from scraping multiple SERP indexes and our own crawler.

OpenAI SDK

Vercel AI SDK

LangChain SDK

import { z } from "zod";
import { zodResponseFormat } from "openai/helpers/zod";

const GarryTanSchema = z.object({
	linkedin_url: z.string(),
	x_url: z.string(),
	first_name: z.string(),
	last_name: z.string(),
	location: z.string(),
	latest_education: z.string(),
	current_job: z.string(),
	followers: z.number(),
	experience: z.array(
		z.object({
			company: z.string(),
			title: z.string(),
			start_date: z.string(),
			end_date: z.string(),
		})
	),
});

const response = await interfaze.chat.completions.create({
	model: "interfaze-beta",
	messages: [{ role: "user", content: "Enrichment information of Garry Tan, Y Combinator" }],
	response_format: zodResponseFormat(GarryTanSchema, "garry_tan_enrichment_schema"),
});

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

//@ts-expect-error precontext is not typed
const precontext = response.precontext;
console.log("Web search results:", precontext[0]?.result);

JSON output

objectreturns the enriched profile typed exactly to the schema, whileprecontextincludes the raw web search results Interfaze pulled in to ground the answer.

{
  "object": {
    "linkedin_url": "https://linkedin.com/in/garrytan",
    "x_url": "https://x.com/garrytan",
    "first_name": "Garry",
    "last_name": "Tan",
    "location": "San Francisco, California, United States",
    "latest_education": "Stanford University (1999-2003), BS in Computer Systems Engineering",
    "current_job": "President & CEO at Y Combinator, Founder at Garry's List, Board Partner & Advisor at Initialized Capital",
    "followers": 319863,
    "experience": [
      {
        "company": "Garry's List",
        "title": "Founder",
        "start_date": "Jan 2026",
        "end_date": "Present"
      },
      {
        "company": "Y Combinator",
        "title": "President & CEO",
        "start_date": "Jan 2023",
        "end_date": "Present"
      },
      {
        "company": "Initialized Capital",
        "title": "Founder & Managing Partner",
        "start_date": "Jan 2012",
        "end_date": "Dec 2022"
      },
      {
        "company": "Posterous.com",
        "title": "Cofounder",
        "start_date": "Jan 2008",
        "end_date": "Jan 2011"
      },
      {
        "company": "Palantir Technologies",
        "title": "Lead Engineer, Designer",
        "start_date": "Sep 2005",
        "end_date": "Oct 2007"
      }
      // ... more roles
    ]
  },
  "precontext": [
    {
      "name": "search",
      "result": [
        {
          "title": "Garry Tan - President & CEO, Y Combinator - LinkedIn",
          "description": "President & CEO of Y Combinator. Y Combinator funds hundreds of companies per year...",
          "url": "https://www.linkedin.com/in/garrytan"
        }
        // ... more search results
      ]
    }
  ]
}

Long audio transcription

The clip below is1 hour 35 minutesof a podcast episode. Interfaze transcribes it in**~50 seconds**with per-chunk timestamps.

OpenAI SDK

Vercel AI SDK

LangChain SDK

import { z } from "zod";
import { zodResponseFormat } from "openai/helpers/zod";

const response = await interfaze.chat.completions.create({
	model: "interfaze-beta",
	messages: [
		{ role: "system", content: "<task>speech_to_text</task>" },
		{
			role: "user",
			content: [
				{ type: "text", text: "Transcribe the audio file https://r2public.jigsawstack.com/interfaze/examples/stt_long_audio_sample_3.mp3" },
			],
		},
	],
	response_format: zodResponseFormat(z.any(), "empty_schema"),
});

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

JSON output

The response is the raw task result as shown below.

{
  "name": "speech_to_text",
  "result": {
    "text": "We don't teach leaders how to have uncomfortable conversations. We don't teach students how to have uncomfortable conversations. You tell me which is going to be more valuable for the rest of your life. How to have a difficult conversation or trigonometry?...",
    "chunks": [
      { "timestamp": [0, 3.39], "text": "We don't teach leaders how to have uncomfortable conversations. We don't teach students how" },
      { "timestamp": [3.39, 6.79], "text": "to have uncomfortable conversations. You tell me which is going to be more valuable" },
      { "timestamp": [6.79, 10.18], "text": "for the rest of your life. How to have a difficult conversation or trigonometry?" }
      // ... thousands more timestamped chunks across the full 1h 35m
    ]
  }
}

Speech-to-text docs →

Conclusion

We’re excited to keep experimenting, growing and discovering new research that makes deterministic AI more efficient and accessible to all developers!

Get started for free and try it on your own documents, images and prompts. We’re excited to see what you build!

Get started

• Get an API key— sign in to the Interfaze dashboard.
• Read the docs— start with the quickstart and SDK setup.
• View pricing— usage-based, same range as the flash/mini models.

Dive deeper

• Full leaderboard— every benchmark, every model.
• Benchmark code- see how we ran the benchmarks.
• Structured Output Benchmark- the SOB leaderboard.

Stay in touch

• Follow us onXandLinkedInfor research updates.
• Join the conversation onDiscord.
• Questions or feedback? Email[email protected].