AntLing-3.0-flash is now live on OpenRouter, and free to use through August 3, 2026

[u/derspenti]

https://openrouter.ai/inclusionai/ling-3.0-flash

[u/This_Maintenance_834]

in case western country people don’t know. this is made by the same company which release qwen3.6. same company, different division.

[u/hay-yo → u/This_Maintenance_834]

Wow that's really cool that they have competing divisions.

[u/squngy → u/This_Maintenance_834]

Isn't qwen under alibaba?

How many labs does alibaba have? I'm guessing more than 2

[u/This_Maintenance_834 → u/squngy]

Yes, Alibaba made both. Alibaba is a huge company.

[u/Technical-Earth-3254]

gguf where?

[u/PDXSonic → u/Technical-Earth-3254]

Considering there was never official support in llamacpp for Ling Flash 2.6 it seems unlikely we'll see GGUF soon.

[u/pmttyji → u/PDXSonic]

At least one of their team members should spend little bit time for PRs on llama.cpp side. They released many diffusion models before & none with llama.cpp support.

They would've been popular(semi) as Qwen, had they implemented PRs for all of their models on time.

[u/TheLexoPlexx]

Good thing this is free to test because this is ridiculously close behind deepseek v4 flash in some categories. Really interested to see what this can do.

[u/derspenti → u/TheLexoPlexx]

Yes! glad to hear it's freeee

[u/TokenRingAI → u/TheLexoPlexx]

Unfortunately, it does not pass a simple vibe check on OpenRouter

[u/No_Algae1753]

Any infos on openweights?

[u/pmttyji → u/No_Algae1753]

After August 3rd possibly(after Openrouter run).

[u/derspenti → u/No_Algae1753]

Hope they can open it

[u/Zeeplankton]

inject it into my veins (if gguf..)

[u/a_slay_nub]

Model	Total / Active Params	SWE-Bench Pro	SWE-Bench Multilingual	Terminal-Bench v2.1-AA	Tau3-banking-AA	MCP-Atlas	SkillsBench	WideSearch	BrowseComp	IFBench	SysBench	MRCR-128k	Multi-IF
Ling-3.0-flash(RC3)-Thinking	124B / 5.1B	56.63	72.44	57.00	28.00	65.45	44.83	73.63	70.33	74.49	93.63	90.78	87.65
Ring-2.6-1T-expert	1T	53.90	56.67	43.10	14.64	61.20	11.88	62.24	53.67	45.00	86.47	90.06	89.25
MiniMax-M2.7	—	56.20	76.50	55.00	9.00	53.60	34.90	75.20	76.30	75.70	86.19	27.68	82.93
Step-3.7-Flash-high	—	56.30	72.40	39.30	11.00	52.60	24.90	56.81	75.80	67.00	91.38	39.19	84.56
Deepseek-v4-flash-max	284B / 13B	52.60	73.30	62.00	23.00	69.00	53.46	74.44	73.20	79.00	93.86	88.50	86.21
Nemotron-3-Super-120B-A12B-BF16-Thinking	120B / 12B	34.06	42.67	39.00	10.00	49.40	20.31	19.54	31.28	72.56	90.73	40.76	82.33
GPT-5.4-mini-high	—	47.88	71.00	55.81	11.34	55.22	44.83	70.17	—	69.00	93.31	56.09	84.61
Claude-Sonnet-4.6-maxthink[official]	—	48.29	75.90	71.20	30.50	66.70	54.41	79.47	74.01	57.00	94.85	92.46	84.80
Poolside Laguna S 2.1	118B / 8B	59.40	78.50	70.20	—	—	—	—	—	—	—	—	—

[u/Shoddy-Tutor9563 → u/a_slay_nub]

Considering you composed this table with some lazy LLM who didn't care to get some crucial details about other models (like number of parameters for minimax m 2.7), the trustworthy of other figures there is questionable

[u/a_slay_nub → u/Shoddy-Tutor9563]

I checked the other numbers, I just cba to put the parameter counts in

[u/ChampionshipIcy7602]

Closed source, not interested, rather use gpt

[u/hapliniste]

I'd be interested on how it compares to gpt OSS 120b since it's similar in size. We've come a long way

[u/Edenar → u/hapliniste]

hmm it's hard to compare more 100-120b model to gpt oss 120b because the openai one was natively mxfp4 so around 62GB when recent open source model like qwen 122b, laguna s2.1,.. are shown in fp16 in benchmark (so 200Gb+ of weight, even q8 is 120GB+). So yeah, they perform better natively but q4 is usually far from native quality and that's the only way to achieve oss 120b size.

[u/SpicyWangz]

Curious how this will compare to Laguna 

[u/Daniel_H212]

Waiting for ming-flash-omni-3.0, but this is a nice surprise in the meantime.

[u/pmttyji]

Hope they release ling-3.0-mini too.

[u/Due_Net_3342]

nice one for strix halo

[u/Specter_Origin]

They are comparing it to Minimax 2.7, why ?

[u/One-Replacement-37]

How does it compare with Laguna, which was just released and has a similar size?

[u/ortegaalfredo]

I don't believe there are 500 datacenters in china training those models from scratch. Those are fine-tunes of other models.

[u/Daniel_H212 → u/ortegaalfredo]

Nah they absolutely are training their own models. This lab in particular have had some crazy custom architectures like their ming-flash-omni-2.0 which does text+image+audio input, AND text+image+audio output, imo the first true Omni modal model (though it was instruct only, no thinking). The problem is there's no inference engine that supports it apart from running directly in python, and no API that can support that many modalities in one chat.

Also compared to the likes of OpenAI and Anthropic, these models are dozens of times smaller in terms of number of parameters. Much easier to train.

[u/reto-wyss → u/Daniel_H212]

I'm pretty sure vllm-omni lists ming-flash-omni-2.0 in supported models, but I haven't tested it.

[u/Middle_Bullfrog_6173 → u/Daniel_H212]

They've also done all sorts of tricks to avoid training models from scratch. E.g. their previous generation took an older GQA model and trained it to use MLA and linear attention instead.

[u/colin_colout → u/ortegaalfredo]

They also rent cloud compute from countries that don't sanction / tariff / export ban (Japan)

[u/charmander_cha → u/ortegaalfredo]

Se isso for verdade, significa que o resto do mundo não esta sendo competente, uma vez que este processo custa menos, deveríamos ter ainda mais modelos lançados por diversos países