Ling-3.0-flash-VL brings 124B multimodal reasoning to local runtimes

🚀 RELEASES & LAUNCHES

• UPDATE: Ling-3.0-flash-VL brings 124B multimodal reasoning to local runtimes
  InclusionAI released Ling-3.0-flash-VL, a 124B vision-language model with 256K-token context.
  • For local AI developers building image-, video-, and agentic applications.
  • The MIT-licensed model supports image and video inputs, has 124B total parameters with 5.5B activated per token, and supports up to 256K tokens.
  • It scored 42 on the Artificial Analysis Intelligence Index v4.1.1, up from 38 for Ling-3.0-flash.
  • llama.cpp merged support, including image and video inference and a 248.9GB BF16 GGUF conversion.
  • The release enables multimodal reasoning and long-context inference in local runtimes such as llama.cpp and SGLang.
  Sources 📄 Source: huggingface.co · 📎 Coverage: github.com · 👁 via r/LocalLLaMA

📊 BENCHMARKS & EVALS

• Local-model SWE-bench results revised after evaluation workflow fix
  WonderRico re-ran 78 local-model SWE-bench evaluations under a frozen setup.
  • The results target developers evaluating local open-weight models for agentic coding.
  • The re-evaluation covered the first 100 Django tasks from SWE-bench Verified using mini-swe-agent.
  • Thirty-eight of 78 runs changed verdicts, including nine that shifted by at least 5 points.
  • Qwen3.8-Flash-Next scored 97, while the best heterogeneous-GPU llama.cpp run scored 92.
  • The correction shows that benchmark workflows must be pinned to stable scripts, containers, and dependency versions.
  Sources 📄 Source: github.com · 📎 Coverage: wonderrico.github.io · 👁 via r/LocalLLaMA