
### Unlimited-OCR

Unlimited-OCR is a multimodal OCR and document-parsing model from Baidu, built for one-shot long-horizon document understanding. It reads images and pages and produces structured text output, extending the ideas behind DeepSeek-OCR toward parsing long documents in a single pass.

#### Overview

The model pairs a vision front-end with a DeepSeek-style mixture-of-experts language backbone, so it can look at a rendered page and transcribe or parse it end to end. It handles both single images and multi-page inputs, including PDFs converted to page images, and returns parsed document text rather than free-form chat. Output is multilingual, making it suitable for documents in a range of scripts.

#### Key features

- Document parsing and OCR from images and multi-page inputs
- One-shot long-horizon parsing for long documents
- Single-image modes (high-detail and full-page) plus a multi-page mode for PDFs
- Multilingual text recognition
- Mixture-of-experts language backbone with a vision encoder front-end

#### Use cases

- Converting scanned documents and PDFs into structured text
- Long-document and multi-page parsing in a single pass
- Extracting text from charts, tables, and mixed-layout pages
- Building document-understanding and search pipelines over image-based archives
- Multilingual OCR across diverse scripts

#### Model resources

- Model weights and card: [baidu/Unlimited-OCR on Hugging Face](https://huggingface.co/baidu/Unlimited-OCR)


Long-document OCR and document-parsing vision-language model

Unlimited-OCR


### Kimi K3

Kimi K3 is an open-weight, native multimodal agentic model from Moonshot AI and their most capable model to date. It is a 2.8T-parameter Mixture-of-Experts model built on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), with native vision capabilities and a 1-million-token context window. It is the first open 3T-class model, designed for frontier intelligence across long-horizon coding, knowledge work, and reasoning.

#### Key Features

- **New Architecture** — Built on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), scaling MoE sparsity with a Stable LatentMoE framework that activates 16 of 896 experts, yielding roughly a 2.5x improvement in overall scaling efficiency over Kimi K2.
- **Long-Horizon Coding** — Operating with minimal human oversight, Kimi K3 sustains long engineering sessions, navigates massive repositories, and orchestrates terminal tools, from GPU kernel optimization and compiler development to vision-in-the-loop game dev, CAD, and chip design.
- **Agentic Knowledge Work** — Advances end-to-end knowledge work, producing deep research with interactive visualizations, widgets and dashboards, and motion design and video editing, powered by its native multimodal architecture.
- **Native Multimodality and Long Context** — Understands text, images, and video within the same model, and supports a 1-million-token context window.
- **Always-On Thinking** — Thinking is always enabled and returned as `reasoning_content`; thinking effort is selectable between low, high, and max. The model was trained in preserved thinking history mode, so prior assistant reasoning is passed back across multi-turn conversations and tool calls.
- **Open Frontier Weights** — The full model weights are openly released, making frontier-class multimodal agentic capability available to run on your own infrastructure.

#### Architecture

Kimi K3 is a Mixture-of-Experts model with 2.8T total parameters and 104B activated per token, spread across 93 layers (1 dense). Its attention stack composes 69 KDA layers with 24 Gated MLA layers over 96 attention heads and a 7168-dimensional attention hidden size. The Stable LatentMoE framework uses a 3584-dimensional latent MoE space with 3072 hidden dimensions per expert, selecting 16 of 896 experts per token alongside 2 shared experts. The model uses SiTU-GLU activations, a 160K vocabulary, and a 1,048,576-token context length. Quantization-aware training is applied from the SFT stage onward, giving native MXFP4 weights with MXFP8 activations for broad hardware compatibility.

On Vast, Kimi K3 is available as a full-precision vLLM flagship served from the original weights at [moonshotai/Kimi-K3](https://huggingface.co/moonshotai/Kimi-K3), and as single-node Unsloth dynamic 1-bit and 2-bit GGUF quantizations from [unsloth/Kimi-K3-GGUF](https://huggingface.co/unsloth/Kimi-K3-GGUF).

#### Benchmarks

On reasoning and knowledge, Kimi K3 scores 93.5 on GPQA Diamond, 74.7 on AA-LCR, 43.5 on HLE-Full (56.0 with tools), and 23.4 on CritPt.

On coding, it reaches 88.3 on Terminal-Bench 2.1, 81.2 on FrontierSWE, 77.8 on ProgramBench, 72.9 on Kimi Code Bench 2.0, 67.5 on DeepSWE, 58.7 on SciCode, 48.3 on MLS-Bench-Lite, 42.0 on SWE-Marathon, and 36.6 on PostTrainBench.

On agentic tasks, it scores 95.0 on DeepSearchQA (F1), 94.6 on Harvey Lab-AA, 94.5 on MCPMark-Verified, 91.2 on BrowseComp, 84.8 on OSWorld-Verified, 84.2 on MCP-Atlas, 76.5 on Toolathlon-Verified, 76.2 on ResearchRubrics, 71.6 on CorpFin v2, 63.3 on OfficeQA Pro, 60.1 on SaaS-Bench, 58.3 on OSWorld 2.0, 54.4 on Finance Agent v2, 54.3 on JobBench, 44.2 on Legal Research Bench, 41.0 on APEX-Agents, 34.8 on SpreadsheetBench 2, 33.4 on tau-cubed Banking, 30.8 on AutomationBench, and 28.3 on Agents' Last Exam. It posts Elo ratings of 1686 on GDPval-AA v2 and 1548 on AA-Briefcase.

On vision, it scores 94.3 on MathVision (97.8 with python), 91.1 on OmniDocBench, 90.0 on Video-MME with subtitles, 85.7 on BabyVision with python, 84.8 on CharXiv reasoning questions (91.3 with python), 82.1 on MMVU, 81.6 on MMMU-Pro (83.4 with python), 58.5 on PerceptionBench, 51.0 on WorldVQA ForceAnswer, and 23.0 on ZeroBench pass@5 (41.0 with python).

#### Use Cases

- Long-horizon autonomous software engineering across large repositories
- Terminal and tool orchestration for build, test, and optimization loops
- Deep research producing interactive visualizations, dashboards, and widgets
- Document, chart, and screenshot understanding at scale
- Video understanding and motion design workflows
- Computer-use and browser agents operating over extended sessions
- Million-token context tasks spanning entire codebases or document sets


Moonshot AI's 2.8T-parameter multimodal MoE — full-precision vLLM flagship on 8xB300, plus single-node Unsloth 1-bit and 2-bit dynamic GGUF quants

UD-Q2_K_XL (Unsloth 2-bit)

UD-IQ1_M (Unsloth 1-bit)

UD-IQ1_S (Unsloth 1-bit)

Kimi K3


### Qwen3.6 35B A3B: Agentic Coding with Hybrid Gated DeltaNet

Qwen3.6 35B A3B is the first open-weight model in the Qwen3.6 series, built on direct community feedback and focused on stability and real-world utility. It combines a hybrid Gated DeltaNet and Gated Attention architecture with sparse Mixture-of-Experts routing and a vision encoder for unified multimodal reasoning.

#### Key Features

- **Agentic Coding** - Handles frontend workflows and repository-level reasoning with improved fluency and precision over earlier Qwen generations
- **Thinking Preservation** - New option to retain reasoning context from historical messages, streamlining iterative development and reducing redundant token generation
- **Hybrid Architecture** - Alternating Gated DeltaNet and Gated Attention blocks combined with sparse MoE, balancing long-context efficiency against attention precision
- **Sparse Mixture-of-Experts** - 256 total experts with 8 routed and 1 shared expert active per token, delivering 35B total capacity with only 3B active parameters
- **Multi-Token Prediction** - Trained with multi-step MTP, enabling speculative decoding for lower-latency inference
- **Native 262K Context** - Handles 262,144 tokens natively, extensible up to 1,010,000 tokens via YaRN RoPE scaling
- **Multimodal Inputs** - Unified vision-language model supporting text, image, and video inputs
- **Tool Calling** - Native tool-calling support with the `qwen3_coder` parser for agent workflows

#### Benchmark Performance

**Coding and Software Engineering:**
- SWE-bench Verified: 73.4
- SWE-bench Multilingual: 67.2
- SWE-bench Pro: 49.5
- Terminal-Bench 2.0: 51.5
- LiveCodeBench v6: 80.4
- NL2Repo: 29.4
- QwenClawBench: 52.6

**General Agent and Tool Use:**
- TAU3-Bench: 67.2
- DeepPlanning: 25.9
- MCPMark: 37.0
- MCP-Atlas: 62.8
- WideSearch: 60.1

**Knowledge:**
- MMLU-Pro: 85.2
- MMLU-Redux: 93.3
- SuperGPQA: 64.7
- C-Eval: 90.0

**STEM and Reasoning:**
- GPQA: 86.0
- HLE: 21.4
- HMMT Feb 25: 90.7
- HMMT Nov 25: 89.1
- HMMT Feb 26: 83.6
- IMOAnswerBench: 78.9
- AIME26: 92.6

#### Use Cases

- Agentic coding tasks across frontend, backend, and repository-level workflows
- Multi-turn agent scenarios where preserved reasoning context improves decision consistency
- Tool-calling and MCP-based automation
- Competition-level mathematics and STEM reasoning
- Long-context document analysis up to 262K tokens natively
- Visual question answering and image-grounded reasoning
- Video understanding with configurable frame sampling

#### Architecture

Qwen3.6 35B A3B uses a 40-layer hybrid architecture organized as ten cycles of three Gated DeltaNet blocks followed by one Gated Attention block, each paired with a sparse Mixture-of-Experts feed-forward layer.

Gated DeltaNet provides linear-attention efficiency with a fixed-size recurrent state, keeping long-context compute and memory cost tractable. The interleaved Gated Attention blocks use 16 query heads and 2 key-value heads with a 256-dimensional head and a 64-dimensional rotary position embedding, preserving precise token-level attention where it is most valuable.

The Mixture-of-Experts layer routes each token through 8 of 256 available experts plus 1 shared expert, with a 512-dimensional expert intermediate size. The model is trained with Multi-Token Prediction across multiple steps, enabling speculative decoding at inference time.

A 2048-dimensional language backbone pairs with a vision encoder to form a unified multimodal model, supporting a 248,320-token padded vocabulary and handling text, image, and video inputs through a shared representation.

Deploy Qwen3.6 35B A3B on Vast.ai with vLLM, SGLang, or llama.cpp for efficient agentic coding, long-context reasoning, and multimodal inference on flexible GPU infrastructure.


Agentic coding MoE with hybrid Gated DeltaNet and vision support

UD-Q8_K_XL

UD-Q5_K_XL

UD-Q4_K_XL

Qwen3.6 35B A3B


### Gemma 4 31B IT: Dense Vision-Language Model

Gemma 4 is Google DeepMind's next-generation family of open multimodal models. The 31B variant is the dense flagship of the family, built to deliver frontier-level reasoning, coding, and multimodal understanding on consumer GPUs and workstations. It natively handles text and image input, supports a 256K context window, and covers 140+ languages.

#### Key Features

-   **Dense 31B Architecture** - 30.7B-parameter dense transformer targeting the highest-quality end of the Gemma 4 family.
-   **Hybrid Attention** - Interleaves sliding window (local) and full global attention layers, with unified Keys and Values on global layers and Proportional RoPE (p-RoPE) for efficient long-context processing.
-   **Reasoning / Thinking Mode** - Built-in configurable thinking mode lets the model reason step-by-step before answering.
-   **Multimodal** - Native text and image understanding with variable aspect ratio and resolution support; video analysis via frame sequences.
-   **Function Calling** - Native structured tool use with a custom tool-call protocol for agentic workflows.
-   **Long Context** - 256K token context window for document analysis, long-form reasoning, and agent trajectories.
-   **Multilingual** - Out-of-the-box support for 35+ languages, pre-trained on 140+.
-   **Native System Prompts** - First-class support for the system role.

#### Use Cases

-   Document and PDF parsing, OCR (including multilingual and handwriting)
-   Chart, diagram, and screen/UI understanding
-   Long-context reasoning and summarization
-   Code generation, completion, and correction
-   Agentic workflows with structured function calling
-   Visual question answering and image analysis
-   Multilingual chat and translation

#### Architecture

Gemma 4 31B IT is a 60-layer dense transformer with a 1024-token sliding window on local attention layers and unified Keys/Values on global layers, paired with a ~550M parameter vision encoder. The final layer is always global, ensuring deep awareness for long-context tasks while local layers keep the memory footprint manageable.

#### Benchmarks

Instruction-tuned results reported by Google DeepMind (selected):

-   MMLU Pro: 85.2%
-   AIME 2026 (no tools): 89.2%
-   LiveCodeBench v6: 80.0%
-   Codeforces ELO: 2150
-   GPQA Diamond: 84.3%
-   Tau2 (average over 3): 76.9%
-   HLE (no tools): 19.5%
-   HLE (with search): 26.5%
-   BigBench Extra Hard: 74.4%
-   MMMLU: 88.4%
-   MMMU Pro (vision): 76.9%
-   MATH-Vision: 85.6%
-   MedXPertQA MM: 61.3%
-   MRCR v2 8-needle 128K: 66.4%

For full benchmark tables and model family comparisons, see the [model card on HuggingFace](https://huggingface.co/google/gemma-4-31B-it).


Gemma 4 31B dense vision-language model by Google with 256K context and thinking mode

Unlimited-OCR

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Unlimited-OCR

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Related Models

DeepSeek OCR