On October 6, 2026, Ant Group's InclusionAI team released Ling-3.1-flash, and for roughly the next two weeks you can try one of the largest new flagship models of this quarter without paying. The Vercel AI Gateway changelog states free access through October 13, and the trial window on the other official routes is described as two weeks. A time-boxed free window is the best excuse you will get this month to stress-test a heavyweight model on your real work, but only if you use those days deliberately. This SOP covers the three official entry routes, what to test while access is free, how to feed the model's long-context window properly, how to wire up the API, and what to do before the window closes.
Know the model before you test it
Ling-3.1-flash is a mixture-of-experts model with 560 billion total parameters and about 25 billion active per token. That ratio matters for your expectations: the model carries a large knowledge base while keeping per-token compute closer to a mid-size model, which is why Ant can offer a free trial at all.
The architecture is worth understanding because it explains the model's personality. Ant uses a hybrid attention stack: seven KDA linear-attention layers paired with one Gated MLA layer, and a router that picks 8 of 512 routed experts plus 1 shared expert on every token. Linear attention dominates the stack, which typically helps with very long inputs and streaming workloads, while the single Gated MLA layer keeps precise retrieval where it counts.
For scale reference, the previous generation, Ling-3.0-flash, was 124 billion total parameters with 5.1 billion active. The new model is roughly 4.5 times larger in total size and about 5 times larger in active parameters. In practice that should mean noticeably stronger reasoning and synthesis, not just a bigger number on a leaderboard.
One honesty warning before you plan anything: the model's context ceiling is 1 million tokens, but during the free trial the serving layer caps input at 256K tokens. Do not build a workflow that depends on 1M-token inputs this week, because it will break the moment you hit the trial limit. The full 1M window is slated to open on the paid tier, and Ant has said it plans to open-source the model as well, though no dates have been announced. Everything else in this SOP works within the 256K trial window.
Three official entry routes, and which one to pick
There are three official ways in, plus one additional hosted option. Pick based on what you are evaluating for.
Route 1: the web chat, zero setup. Point your browser at chat.ant-ling.com and sign up normally. This is the fastest way to get a feel for the model and requires no keys, no billing, and no code. Spend your first hour here: paste a real document, ask a real question, and see whether the answer style fits your work.
Route 2: the Ant Digital MaaS platform, for API keys. If you want to automate anything, you need an API key, and the official source is Ant Digital's MaaS platform at maas.antdigital.com. Register through the standard flow, create a key, and note the model identifier for Ling-3.1-flash, which is listed as modelservice-1790229240754001388. This route is the right one if your evaluation involves scripts, batch jobs, or integration into an existing application.
Route 3: Vercel AI Gateway, for overseas developers. If you already build on Vercel, the AI Gateway has listed Ling-3.1-flash, free through October 13 according to the changelog. This is the lowest-friction path if you are outside a convenient billing region for the MaaS platform or you want the model swapped in behind a gateway you already use.
There is also a hosted option on Novita AI if you prefer a third-party inference provider.
One rule worth stating plainly: use only these official signup routes. Do not chase gray-market resellers, do not borrow or share accounts, and do not look for ways around regional or identity restrictions. A free two-week window is not worth an account ban or a worse problem, and everything this model can do for you is reachable through the front door.
What to test during the free window
Two weeks goes fast. Test in priority order, starting with the tasks that would actually justify paying later.
First, long-document synthesis. The 256K window comfortably fits hundreds of pages, so this is the marquee capability to evaluate. Take your hardest real case: a stack of quarterly reports, a long contract with appendices, a research corpus. Ask for a structured synthesis with citations to specific sections, then verify the citations. Retrieval quality over very long inputs is exactly where hybrid linear-attention architectures either shine or disappoint, and only your own documents will tell you which.
Second, office document generation. Ant's official claims highlight strong results on GDPval-AA V2.1, an office-productivity benchmark, and HealthBench Professional, a medical benchmark. Be clear-eyed here: those are official claims, and no detailed scores have been published, so treat them as pointers rather than proof. What is concretely described is the ability to consolidate source material and generate deliverables such as Excel workbooks and PowerPoint decks. Bring a messy real folder of notes and data, ask for a slide outline and a spreadsheet, and judge the output yourself.
Third, long coding tasks. The official blog documents two cases worth knowing. In one, the model worked about 17 hours to write a Lua-to-x86-64 ELF compiler from scratch, passing 178 of 182 official test cases. In another, it spent roughly 20 hours porting a C image library to Rust, achieving an 8.015x speedup with all 30 correctness checks passing. Those are official self-reported numbers, but the test suites are public, which makes them unusually verifiable for vendor claims. You will not get 17 unattended hours out of a chat window, but you can hand the model a long, stateful coding task with a clear test harness and see how far it gets before it loses the thread.
How to feed a 256K window properly
The single most common mistake with long-context models is dumping everything in raw and hoping. With a 256K trial limit, structure your input instead. The pattern that works reliably is chunked summarization plus a full final section.
Step one: split your corpus into chunks of roughly 20K to 40K tokens each, keeping document boundaries intact wherever possible. Step two: for each chunk, ask the model for a dense, factual summary that preserves names, numbers, dates, and any quotes you may need. Step three: combine those summaries into an anchored digest and use it as the running context for your main task. Step four: identify the one or two documents that matter most for the final answer, and include their critical sections in full, verbatim, at the end of your prompt, closest to where the model generates.
Why this works: summaries carry breadth cheaply, while full text near the end of the window gives the model exact wording to quote and verify. If a cross-check between a summary and a full passage ever conflicts, trust the full passage and re-summarize that chunk. And watch your budget: 256K is the input ceiling for the whole request, so if your digest plus full sections plus instructions approach the limit, cut the digest before you cut the primary documents.
Calling the API: the OpenAI-compatible pattern
The MaaS platform exposes an OpenAI-compatible endpoint, which means you can reuse almost any existing client. The shape looks like this:
from openai import OpenAI
client = OpenAI(
api_key="YOUR_MAAS_API_KEY",
base_url="https://MAAS_ENDPOINT_PLACEHOLDER/v1",
)
resp = client.chat.completions.create(
model="modelservice-1790229240754001388",
messages=[
{"role": "system", "content": "You are a precise analyst."},
{"role": "user", "content": "Summarize the attached report."},
],
)
print(resp.choices[0].message.content)Treat the base URL above strictly as a placeholder. The exact endpoint address, path format, and any provider-specific parameters are defined in the MaaS platform documentation, and you should copy them from there rather than from any third-party tutorial, including this one. Compatibility layers occasionally differ in details like streaming field names or maximum parameter values, so run one trivial request first, confirm the response parses, and only then move your real workload over.
Two practical notes. First, log your token usage from day one, even during the free window, because that is the number you will need to estimate costs when the paid tier arrives. Second, if you go through Vercel AI Gateway instead, the same OpenAI-compatible pattern applies through the gateway's own base URL, so your application code barely changes.
Before the window closes
Three things deserve attention as October 13 approaches.
Watch for the 1M context announcement. The paid tier is expected to unlock the full million-token window, and workflows that were awkward under 256K, such as whole-codebase reasoning, may become straightforward. Wait for official pricing rather than assuming; no rates have been published yet.
Watch for the open-source announcement. Ant has said it plans to open-source the model in sync with the commercial release, but no date has been given. If that happens, self-hosting becomes an option for latency-sensitive or data-sensitive work, and the calculus of staying on the API changes.
Export your data now. Anything you produced through the web chat, including conversation histories and generated files, should be saved locally before the trial ends. If your evaluation went well, you want your best prompts, your chunked-summarization digests, and your test results ready to carry into whatever comes next. If it went badly, you want the evidence of why.
The decision at the end of two weeks is simple: if long-document synthesis and document generation saved you real hours during the trial, the paid tier with 1M context will likely save more. If the model only handled what your existing tools already handle, you lost nothing by checking.
FAQ
Q1: Is Ling-3.1-flash actually free right now?
A1: Yes, under an official two-week trial. Vercel's AI Gateway changelog states free access through October 13, and the web chat and MaaS trial routes are described as free for two weeks from launch. Paid terms and pricing have not been announced, so plan around the trial end date rather than around rumors.
Q2: Do I get the full 1 million token context during the trial?
A2: No. The context ceiling is 1M tokens, but the trial serving layer caps input at 256K. The full 1M window is planned for the paid tier. Design your prompts and pipelines for 256K, and use the chunked summarization pattern above to cover larger corpora.
Q3: Which entry route should I choose?
A3: Use chat.ant-ling.com if you just want to try the model with zero setup, the Ant Digital MaaS platform if you need an API key for scripts and integrations, and Vercel AI Gateway if you are an overseas developer already building on Vercel, where it is free through October 13. Novita AI is an additional hosted option. All signups should go through official channels only.
Q4: What is this model actually good at?
A4: Official claims highlight office productivity, citing strong results on GDPval-AA V2.1, and medical work, citing HealthBench Professional, with concrete capabilities around consolidating materials into Excel and PowerPoint outputs. No detailed benchmark scores have been published, so verify on your own tasks. Official long-coding cases include a 17-hour compiler build passing 178 of 182 tests and a 20-hour C-to-Rust port with an 8x speedup.
Q5: Will Ling-3.1-flash be open source?
A5: Ant has said it plans to open-source the model alongside the commercial release, but no date has been announced and no license terms are public yet. Follow the official InclusionAI channels for the announcement, and do not make infrastructure decisions based on unofficial speculation.
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Further Reading
If you are comparing options in the same weight class, our integration guides for other flagship models are directly applicable: the OpenAI-compatible request pattern in the DeepSeek V4.1 Flash integration SOP (/en/posts/deepseek-v4-1-flash-integration-sop) transfers almost unchanged, the Claude Sonnet 5.5 API SOP (/en/posts/claude-sonnet-5-5-api-sop) covers tool-calling conventions worth mirroring, and the GLM-5.3 Flash integration SOP (/en/posts/glm-5-3-flash-integration-sop) is a useful second reference for MoE models with large context windows.
This article was drafted with AI assistance and reviewed by a human editor.