@berryxia: Guys, my back isn’t chilling. But, I’m thrilled after seeing this model architecture! While everyone is still frantically stacking parameters and competing with general-purpose large models, Interfaze has introduced a brand-new hybrid architecture. It achieves OCR, vision, STT, and structured output accuracy for deterministic tasks that crushes Gemini-3-Flash…

Brothers, my back didn’t go cold. But seeing this model architecture made me thrilled! While everyone is still frantically piling on parameters and competing with general-purpose large models, Interfaze has directly introduced a brand-new hybrid architecture. It has pushed the accuracy of deterministic tasks like OCR, vision, STT (Speech-to-Text), and structured output to levels that completely crush Gemini-3-Flash, Claude-Sonnet-4.6, GPT-5.4-Mini, and Grok-4.3. They’ve merged task-specific DNN/CNN encoders with all-around transformers, achieving true “specialization + flexibility”: CNNs handle extreme accuracy and metadata (bounding boxes, confidence scores), while transformers handle understanding and reasoning. They can also activate only parts of the model via tags, causing speed and cost efficiency to skyrocket. Interfaze leads comprehensively across nine hardcore benchmarks, and in high-frequency scenarios like OCR, vision, and audio, its speed and cost-effectiveness completely outperform general-purpose large models. This is the most underrated truth: many real-world productivity tasks in the future won’t need increasingly large general models, but rather this kind of hybrid architecture “born for deterministic tasks.” Interfaze brings AI back from “versatile but expensive and slow” to the realistic path of “accurate, fast, and cheap.” PS: I think I need to test it against the use cases from my previous OCR tests to see how the results compare, and whether they match the leaderboard descriptions! The full blog post is worth reading immediately. 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. How Interfaze works: a hybrid architecture combining DNNs/CNNs with a transformer decoder, plus task-specific adapters and a built-in infra foundation for web index, scraping, and a code sandbox. (https://interfaze.ai/examples/howitworks.png) 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 → (https://interfaze.ai/leaderboards) 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, at1\.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 → (https://interfaze.ai/leaderboards/olmocr) ## 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) (https://interfaze.ai/blog/introducing-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 leaderboard (https://interfaze.ai/leaderboards/structured-output-benchmark)for 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 onX (https://x.com/interfaze_ai)orLinkedIn (https://www.linkedin.com/company/interfaze-ai)to follow our research journey. ## Multilingual performance beyond English Interfaze has great multilingual performance across a wide range of languages. View full MMMLU benchmarks → (https://interfaze.ai/leaderboards/mmmlu) ## 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 → (https://interfaze.ai/leaderboards/voxpopuli-cleaned-aa) ## 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 dashboard (https://interfaze.ai/dashboard)and 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: "", }); The sameinterfazeclient is reused in every example below. Read the full setup guide → (https://interfaze.ai/docs) ### 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. Dense magazine page with text and three figures detected, with red boxes around each illustration (https://r2public.jigsawstack.com/interfaze/examples/dense_text_ocr_figures_output.png) 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 → (https://interfaze.ai/docs/vision/ocr) ### 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. Handwritten poem used as the input image for the partial activation OCR example (https://r2public.jigsawstack.com/interfaze/examples/handwriting.jpeg) Using thetag 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: “ocr” }, { 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 with`name`and`result`, 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 → (https://interfaze.ai/docs/run-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** `object`returns the enriched profile typed exactly to the schema, while`precontext`includes 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”: “Fo