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Interfaze - A new AI architecture built for highly deterministic developer tasks
Interfaze is an AI model built by merging specialized DNN/CNN models with LLMs for any developer tasks like scraping, OCR, classification, extraction, STT, coding, etc. Run complex tasks that require consistency, accuracy and reliability especially on backend tasks.
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***#####%###*****++++++++++++++++++++++**#%%%%%%%%@@@@@@ - OpenAI chat completion API compatible
- OCR, web scraping, STT, classification, internet access and more
- 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
Beta launch video
Model Comparison
See how Interfaze compares to other leading AI models across various benchmarks.
| 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 - just swap the base URL.
OpenAI SDK
Vercel AI SDK
Langchain SDK
OCR & Document Extraction
Smart Web Scraping
Translation
Translate text from one language while maintaining context, tone and consistency
Configurable guardrails and NSFW checks
Fully configurable guardrails for text and images
Speech-to-text (STT) and diarization
Architecture
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.
Specs
Context window
1m tokens
Max output tokens
32k tokens
Input modalities
Text, Images, Audio, File, Video
Reasoning
Available
Pricing
Input tokens
$3 / MTok
Output tokens
$10 / MTok
Caching
Included
Observability & Logging
Included
FAQ
Todo (Prioritized)
- Reduce transactional token count
- Pre-built prompts/schemas optimized for specific tasks
- Built-in observability and logging on the dashboard
- Complete metrics and analytics
- Reduce latency and improve throughput
- Custom SDKs for interfaze with AI SDK, Langchain, etc.
- Leaderboard for projects
If you have feature requests or recommendations, please reach out!
Research references
- Agentic Context Engineering
- Small Language Models are the Future of Agentic AI
- The Sparsely-Gated Mixture-of-Experts Layer
- DeepSeekMoE
- Confronting LLMs with Traditional ML
Who are we?
We are a small team of ML, Software and Infrastructure engineers focused on building small, specialized, efficient models at JigsawStack. Our only goal is to make AI accessible in every developers workflow.