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OpenBMBOpenBMB·
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OpenBMB open-sources the MiniCPM-OCR suite: a 1.3B parser scoring 93.68 on Real5-OmniDocBench, plus a 2B judge model that flags OCR errors

On Oct 27 OpenBMB (ModelBest) open-sourced the MiniCPM-OCR suite with two models: MiniCPM-OCR 1.0, a 1.3B-parameter document parser that scores 93.68 on Real5-OmniDocBench and 54.7 on OCRBench v2 (vendor-reported), and MiniCPM-OCR Jev, a 2B OCR quality judge that scores any parser's output region by region from 0 to 1 and flags content that needs human review. Code and weights are Apache 2.0, with day-one support for vLLM, SGLang, llama.cpp, Ollama and multiple chip platforms.

OpenBMB open-sources the MiniCPM-OCR suite: a 1.3B parser scoring 93.68 on Real5-OmniDocBench, plus a 2B judge model that flags OCR errors

Key Takeaways

  • MiniCPM-OCR 1.0 has just 1.3B parameters and scores 93.68 on Real5-OmniDocBench and 54.7 on OCRBench v2 (vendor-reported, both claimed SOTA)
  • The 2B MiniCPM-OCR Jev judge reaches 93.6 AUROC on JevOCRBench, above JEV-35B-A3B at 90.1 and close to Qwen3.5-397B-A17B at 93.8 (vendor-reported)
  • Jev is parser-agnostic: AUROC stays between 91.9 and 93.4 on outputs from MiniCPM-OCR, GLM-OCR, PaddleOCR-VL-1.6 and Xiaomi-OCR-0
  • Throughput is about 1.49x PaddleOCR-VL-1.6 and 3.62x MinerU2.5 Pro, reaching up to about 128 pages per minute with SDK concurrency (vendor-reported)
  • Code and weights on GitHub are all Apache 2.0, with GGUF/AWQ/GPTQ quantized builds and a SKILL.md for coding agents
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Linked Models & Agents:
⚡ MiniCPM-OCR 1.0⚡ MiniCPM-OCR Jev⚡ PaddleOCR-VL-1.6⚡ MinerU2.5 Pro⚡ Qwen3.5-397B-A17B🌐 OpenBMB🌐 ModelBest🌐 Intel🌐 NVIDIA🌐 Arm
#MiniCPM-OCR#OCR#document parsing#open source#Apache 2.0#On device
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@GoogleDevs@GoogleDevs·
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Google AI Studio Opens Gemini 4 Argon in Build & Playground for Pro Users; Transformers v5.20.1 Adds Native Reflection AI Beam and Meta 816B Muse Spark Open

Google AI Studio launched Gemini 4 Argon access in AI Studio Build and Playground for Google AI Pro subscribers, while Hugging Face Transformers v5.20.0 and v5.20.1 added native support for Reflection AI's frontier coding model Beam, Meta's 816B-parameter (42B active, 1M context) multimodal agentic coding model Muse Spark Open, InclusionAI's Ling 3.0 Flash, and OpenBMB's MiniCPM-V 4.7.

Google AI Studio Opens Gemini 4 Argon in Build & Playground for Pro Users; Transformers v5.20.1 Adds Native Reflection AI Beam and Meta 816B Muse Spark Open

Key Takeaways

  • Google AI Studio opens Gemini 4 Argon in AI Studio Build and Playground (https://ai.studio/build) for Google AI Pro subscribers
  • Hugging Face Transformers v5.20.1 natively integrates Reflection AI's token-efficient coding model Beam and fixes DeepGEMM Mega MoE under expert parallelism
  • Adds native support for Meta's 816B-parameter (42B active, 1M context, BF16/NVFP4) omni-modal coding model Muse Spark Open and InclusionAI's KDA+MLA hybrid Ling 3.0 Flash
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Linked Models & Agents:
#Gemini-4-Argon#Google-AI-Studio#HuggingFace-Transformers#Reflection-AI-Beam#Muse-Spark-Open#Ling-3.0-Flash
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OpenBMB@OpenBMB·
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OpenBMB open-sources MiniCPM-V 4.7: a 35B-A3B MoE multimodal model that runs on-device at 13+ tokens/s on iPhone 17 Pro Max

On 2026-10-12 OpenBMB released MiniCPM-V 4.7, an Apache-2.0 open multimodal family with a 35B-A3B variant (the series' first MoE) and a 1B variant. Its Lookahead Expert Scheduling (LES) prefetches experts across layers and, combined with layer-wise mixed-precision quantization and DSpark speculative sampling, lifts SSD-offloaded decoding on an iPad Pro M4 (16GB) from 2.97 to 17.39 tokens/s (5.86x), per OpenBMB. Weights are on Hugging Face, ModelScope and Ollama, with vLLM, SGLang, llama.cpp and iOS/Android/HarmonyOS support.

OpenBMB open-sources MiniCPM-V 4.7: a 35B-A3B MoE multimodal model that runs on-device at 13+ tokens/s on iPhone 17 Pro Max

Key Takeaways

  • Two Apache-2.0 open-weight models: 35B-A3B (first MoE in the series, Qwen3.6 language backbone) and 1B, plus FP8 / AWQ / GPTQ / int4 / GGUF builds and DSpark draft models (Hugging Face)
  • LES on-device decode (OpenBMB, iPad Pro M4 16GB): baseline 2.97 → LES 9.81 → +layer-wise quant 14.92 → +DSpark 17.39 tokens/s, 5.86x overall (LES doc)
  • 35B-A3B reaches 13+ tokens/s on iPhone 17 Pro Max; mixed 4x/16x visual-token compression trades detail for cost per task
  • Vendor-reported (Instruct, 4x): MMBench 91.72, OCRBench 91.20, OmniDocBench v1.6 94.24, MMMU 80.22 vs Qwen3.6-35B-A3B at 86.76 / 86.00 / 80.17 / 77.44; still behind on SimpleVQA and ScreenSpot
  • Day-0 chip support for Rockchip, Houmo, Intel, AMD and Arm; runs on vLLM, SGLang, llama.cpp and Ollama; fine-tune via SWIFT / LLaMA-Factory
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Linked Models & Agents:
⚡ MiniCPM-V 4.7 35B-A3B⚡ MiniCPM-V 4.7 1B⚡ Qwen3.6-35B-A3B🌐 OpenBMB🌐 面壁智能
#OpenBMB#MiniCPM-V#MoE
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vLLM@vllm_project·
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vLLM: day-0 MiniCPM5-2B on stable — 131K context, Think/No-Think, tool calling

vLLM (@vllm_project) announced day-0 support for OpenBMB MiniCPM5-2B on stable vLLM: 2.6B dense with native 131K context, Think/No-Think from one checkpoint, and tool calling via vLLM’s minicpm5 parser. Built on stock LlamaForCausalLM with training data opened alongside weights.

Key Takeaways

  • Engine: day-0 MiniCPM5-2B on stable vLLM.
  • Capabilities: native 131K context; Think/No-Think from one checkpoint; tool calling via minicpm5 parser.
  • Ecosystem: stock LlamaForCausalLM; training data opened with weights.
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Linked Models & Agents:
⚡ Openbmb Minicpm5 2b🌐 vllm🌐 openbmb
#vLLM#MiniCPM5-2B#OpenBMB#Day 0#tool calling#131K context
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Artificial Analysis@ArtificialAnlys·
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MiniCPM5-2B scores 15 on AA Intelligence Index v4.2 — top open-weights model under 4B

Artificial Analysis (@ArtificialAnlys) scored OpenBMB MiniCPM5-2B (2.6B dense, 131k context, Apache 2.0) at 15 on Intelligence Index v4.2 — highest among open-weights models under 4B total parameters. Strong small-model agentic results (GDPval-AA / τ³-Banking); coding/long-context benches like Terminal-Bench lag larger peers.

Key Takeaways

  • Model: OpenBMB MiniCPM5-2B — 2.6B dense, 131k context, Apache 2.0.
  • Score: AA Intelligence Index v4.2 = 15, highest open-weights under 4B (vs Granite 4.2 3B at 11).
  • Tradeoff: strong small-model agentic benches; weaker on Terminal-Bench / HLE-style coding and knowledge.
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Linked Models & Agents:
⚡ Openbmb Minicpm5 2b🌐 openbmb
#MiniCPM5-2B#OpenBMB#Artificial Analysis#Intelligence Index#open weights#Benchmark
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