Model reference · open weights
Ling-3.0-flash-Fin-fp4 is an open-weight language model from inclusionAI. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | inclusionAI |
|---|---|
| Type | Language models |
| Task | Text gen |
| Parameters (lead) | 65.6B |
| Context | 256k tokens |
| Based on | inclusionAI/Ling-3.0-flash |
| Released | 2026-09-10 |
| Popularity | 30 downloads / month |
| Licence | Open weights |
About
Ling-3.0-flash-Fin is the first finance-enhanced model in the Ant Ling family. Developed by Ant Group with leading financial institutions and domain experts, it extends Ling-3.0-flash through continued training on high-quality financial data.
With 124B total parameters, 5.1B activated parameters, and a 256K context window, the model combines financial expertise with efficient inference for long-horizon agent workflows.
Ling-3.0-flash-Fin was evaluated across FinFIRST, FinSearchComp Verified, FinCRAFT, Finance Agent, APEX-Agents, SpreadsheetBench, and τ³-Banking. These benchmarks cover source-grounded retrieval, investment research, long-horizon execution, valuation modeling, spreadsheet operations, and banking workflows. The model is competitive with both similarly sized models and substantially larger general-purpose models, with particular strength in source selection and tool-intensive financial tasks.
The current checkpoint is released in BF16. Because Ling-3.0-flash-Fin shares the same architecture as Ling-3.0-flash, it is compatible with the same SGLang and vLLM runtimes. For deployment instructions, see the Ling-3.0-flash deployment guide.
Important: Thinking mode is enabled by default. For optimal performance, we strongly recommend using
temperature=1.0,top_p=0.95, andtop_k=20for general inference.
We evaluate the quantized models using several datasets. The FP8 quantized model is applied via the blockwise quantization, and INT4 and FP4 models are applied via groupwise quantization with routed experts weights.
| dataset | BF16 | FP8 | INT4 | FP4 |
|---|---|---|---|---|
| GPQA-diamond | 86.30 | 85.26 | 85.39 | 84.38 |
| SciCode | 41.84 | 42.70 | 41.41 | 41.24 |
| FinCRAFT | 54.23 | 55.91 | 54.48 | 54.73 |
| FSC-verified | 78.28 | 77.48 | 76.86 | 76.77 |
As our first finance-enhanced release, Ling-3.0-flash-Fin still requires further validation in complex, long-horizon workflows. Key assumptions, valuation results, and investment conclusions require professional review and do not constitute investment advice.
Future releases will explore finance-enhanced models at larger scales to further improve complex reasoning and long-horizon task execution.
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys ling-3-0-flash-fin-fp4 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (ling-3-0-flash-fin-fp4 below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"ling-3-0-flash-fin-fp4","messages":[{"role":"user","content":"Hello"}]}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.