whatmodel.sh / Ling-flash-2.0

Ling-flash-2.0

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Ling-flash-2.0 is an open-weights language model from inclusionAI (Ant Group), released September 2025. Its published size is 100B parameters (6.1B active per token). Licence: mit.

Catalogue identity notes

Comparison-only. Arena entry 'ling-flash-2.0' (text). Maker repo inclusionAI/Ling-flash-2.0 @96f9b7a: licence tag mit. Parameters: inclusionAI cards: total of 100B parameters with 6.1B activated (Ring-flash-2.0 card: 'Like Ling-flash-2.0, Ring-flash-2.0 has a total of 100B parameters, with only 6.1B activated'); HF safetensors metadata 102.9B incl. MTP. Released 2025-09: month the maker's Hugging Face repo was created (createdAt 2025-09-17).

59.0±2.1#236 of 949 LLMsfrom 2 benchmark results across 2 sources* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of Ling-flash-2.0, out of 1132 with a score, then more from inclusionAI (Ant Group).

#modelscorewhereRAMreleased
308CoALM-405B UIUC ConvAI & Oumi (CoALM)59±7local~854GB2025-02
309Gemma 2 9B IT DPO (Princeton) Princeton NLP59±1local~7.6GB2024-07
310Ling-flash-2.0 inclusionAI (Ant Group)59±2local~40GB2025-09
311Amazon Nova Experimental Chat (10-09) Amazon59±3comparison only
312Trinity-Large-Preview Arcee AI59±2local~155GB2026-01
more from inclusionAI (Ant Group)
87Ling 3.0 Flash (124B-A5B) inclusionAI (Ant Group)71±2api $0.090/M +local~75GB2026-08
111Ring-2.6-1T inclusionAI (Ant Group)69±2comparison only2026-05
376Ring-flash-2.0 inclusionAI (Ant Group)56±3local~41GB2025-09

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▄▆▅▆█▆▆▆▂ #174 of 466 * · 39% of its score · 1 test

Beats MiniMax-M2, MiniCPM5 2B, Granite 4.2 30B here, though they rank higher overall. Trails LFM2.5 1.2B Instruct, LFM2.5 1.2B Thinking, LFM2.5 8B-A1B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
LMArena (text)1343.8 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 185/379avg 1268▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 39%blunt
other tests1 not in a task · 59% of its score
testresultvs the others in its rundifficultycountswhy
Medmarks-V mean win rate (19 verifiable medical benchmarks, 71 configurations)0.5174 ✎▁▄▃▃▇▄█▃▅▁▁▁ beats 20/30avg 0.423▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 59%sharp

$ where to run it

llama.cpp · recommended

version

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run Ling-flash-2.0 · needs ~62.62 GB · from huggingface.co/bartowski
    chat in the terminal
    llama cli -hf bartowski/inclusionAI_Ling-flash-2.0-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf bartowski/inclusionAI_Ling-flash-2.0-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run Ling-flash-2.0 · needs ~62.62 GB · from huggingface.co/bartowski

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/bartowski/inclusionAI_Ling-flash-2.0-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Ling-flash-2.0 · needs ~36.25 GB · from huggingface.co/bartowski
    chat in the terminal
    ollama run hf.co/bartowski/inclusionAI_Ling-flash-2.0-GGUF:Q2_K

Official weights and documentation: huggingface.co/inclusionAI ↗

$ how the score was worked out · Medmarks: A Comprehensive Ope… 59%, LMArena leaderboard dataset 39%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks — authors' own runs of 61 models1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 59%CC BY 4.0
LMArena leaderboard dataset1▇▇▇▇▇▇▇▇▇▇▇▇ 39%CC BY 4.0
prior · pull toward the middle▇ 2%
weighted average= 59.0

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json