Ling-3.1-flash vs DeepSeek V4.1 Flash: Two Points of Index, Three Times the Bill

Ling-3.1-flash and DeepSeek V4.1 Flash (Max) are two points apart on the Artificial Analysis Intelligence Index — 41 against 39 — and nowhere near each other on price. Run the ten evaluations that make up that index against each model and Ling-3.1-flash costs roughly $0.99 per task against Deep​Seek's $0.27. Both are ~550-billion-parameter mixture-of-experts models with a claimed 1M-token context. One is a closed, text-only model from Ant Group's InclusionAI lab, released 2026-10-01 and still without published weights or a licence. The other has been open under MIT since 2026-09-10, takes images as well as text, runs on 23 providers, and is the model most teams would already have in a config file. The comparison is not close on most axes. The interesting question is why the two points exist at all.

Where Ling-3.1-flash is genuinely ahead

It is ahead on the evaluations that measure driving a terminal and writing code that has to run, and the margin is worth stating precisely because the aggregate hides it.

• Terminal-Bench 4.0 — Ling-3.1-flash 0.333 vs DeepSeek V4.1 Flash (Max) 0.268. Both are modest numbers in absolute terms; the gap is a quarter of DeepSeek's score.

• SciCode — 0.541 vs 0.519.

• CritPt physics reasoning — 0.180 vs 0.143.

• GDPval-AA agentic real-world work — 1,621.75 Elo vs 1,600 Elo.

Two of those four are near-ties, one is a narrow win, and Terminal-Bench 4.0 is the only row where the difference would survive a change of seed. Set that against where DeepSeek wins: AA-Briefcase v1.1 agentic knowledge work at 1,420.47 Elo against 1,400.35, AutomationBench-AA at 0.689 against 0.617, AA-LCR long-context reasoning at 0.84 against 0.83, and — the row that matters most for production — AA-Omniscience at −5.30 against Ling-3.1-flash's +2.22 on a measure where a hallucination is worse than a refusal. DeepSeek's accuracy there is 46.4% with a 96.5% hallucination rate among the questions it answers; Ling-3.1-flash answers 29.1% correctly with a 37.9% hallucination rate. Neither is a model you point at a knowledge base without retrieval in front of it.

The price gap is larger than the index gap, and it is not just the rate card

The headline rates look almost identical. Artificial Analysis records both at $0.30 per million input tokens. They diverge sharply on the other two numbers:

• Output — Ling-3.1-flash $0.90 per million vs DeepSeek V4.1 Flash (Max) $1.20 per million. Here Ling-3.1-flash is the cheaper model outright, by 25%.

• Cache read — Ling-3.1-flash $0.06 per million vs DeepSeek V4.1 Flash (Max) $0.006 per million, a 98% discount against an 80% one. This is where the two models stop being comparable.

The blended rate at a 7:2:1 cache-hit/input/output mix comes out at $0.192 for Ling-3.1-flash and $0.184 for DeepSeek V4.1 Flash (Max) — nearly a wash. But the blend is an assumption about how you use a model, and the assumption is doing a lot of work. Any workload with heavy prompt reuse — a long system prompt, a retrieval preamble, an agent loop that re-sends accumulated context on every turn — pushes the effective mix toward cache reads, and there the 10× difference in cache pricing takes over. Token volume compounds the same way: Ling-3.1-flash is a talkative reasoner, generating 220 million output tokens across the index evaluations against DeepSeek's 250 million, and averaging roughly 357 seconds per evaluation task against DeepSeek's 285. It does more thinking per answer and takes longer doing it.

A worked example: the same agent loop on both

Take a tool-driving agent that runs 1M input tokens per task with 90% of them served from cache, and returns 200,000 output tokens. On Ling-3.1-flash at the figures above: 900,000 cached input tokens at $0.06 plus 100,000 fresh input at $0.30 plus 200,000 output at $0.90 comes to about $0.26 per task. The identical loop on DeepSeek V4.1 Flash (Max) comes to about $0.14 — roughly half.

Now apply DeepSeek's schedule, because this is the part most comparisons skip. DeepSeek's off-peak rate is the one above, and off-peak covers weekends and most of the day; but during two weekday windows, 01:00–04:00 and 06:00–10:00 UTC, input and cache pricing doubles. Move the same loop into a peak window and DeepSeek's cost lands near $0.28 — at which point Ling-3.1-flash's flat, higher rate card is the cheaper option for that hour and the 10× cache discount has been neutralised by the clock.

That is the whole decision in one number pair. If your agent traffic is cache-heavy and you can schedule it, DeepSeek V4.1 Flash (Max) is roughly half the cost of Ling-3.1-flash for a two-point index difference. If your traffic is cache-heavy and lands in DeepSeek's peak windows, the two models cost about the same and Ling-3.1-flash's marginally better terminal-agent row becomes the tiebreaker.

The axes where there is no comparison to make

Three dimensions are not close, and they are the ones that tend to decide an architecture before price is discussed.

• Weights and licence — Ling-3.1-flash has not published weights, has no licence text, and Artificial Analysis files it as proprietary. DeepSeek V4.1 Flash (Max) is open weights under MIT, downloadable from Hugging Face, with commercial use permitted. One of these can be self-hosted, fine-tuned and air-gapped; the other cannot.

• Modality — Ling-3.1-flash is text in, text out. DeepSeek V4.1 Flash (Max) accepts images alongside text and is scored on multimodal benchmarks. If any part of your pipeline hands the model a screenshot, a chart or a scan, the comparison ends there.

• Serving surface — DeepSeek V4.1 Flash (Max) is available through 23 providers, including OrcaRouter; Ling-3.1-flash runs through the vendor's own API and the third-party platforms carrying its release. For a production path, provider count is a resilience property, not a popularity contest.

One more asymmetry that does not show up in any of those lists: DeepSeek V4.1 Flash (Max) exposes 384,000 output tokens in a single response. Ant has not published a maximum output length for Ling-3.1-flash, so a long-generation workload cannot be sized against it yet. Absence of a published cap is not the same as a small one, but it is not a number you can design against either.

Running both, and what that actually looks like

Ling-3.1-flash is not on the OrcaRouter catalogue today — a lookup against our public model API returns not-found for the model under InclusionAI, and this piece will not pretend otherwise. DeepSeek V4.1 Flash is routable right now at the provider's list price with zero markup, so a vendor price change is live on our side the same day it lands, and it sits behind the same single API key as the rest of the catalogue with automatic failover between upstream providers.

That asymmetry has a practical shape. The sane way to hold a comparison where one model is closed, priced roughly four times its predecessor, and reachable through one vendor, is to keep the open, widely-served model as the default path and treat the challenger as something you test rather than something you commit to. DeepSeek V4.1 Flash (Max) can carry the production loop today — open weights, image input, 384K output, a 98% cache discount — while Ling-3.1-flash gets the agent-specific work where its Terminal-Bench and SciCode margins actually apply. Because both speak the OpenAI-compatible chat-completions format, that split is a routing decision rather than an integration project: one endpoint, one key, and a rule that sends the tasks each model is measurably better at.

The verdict

On the evidence available, DeepSeek V4.1 Flash (Max) wins this matchup, and it wins mostly on things that have nothing to do with two Index points: open weights under MIT, image input, 384,000 output tokens, a 98% cache discount, and 23 providers to fail over between. Ling-3.1-flash's case is narrower and real — it is the better terminal-and-tool agent of the two, and it is the only one of the pair that has not published a rate schedule with a peak-hours multiplier baked into it.

Two things would change this assessment. If Ant publishes weights under a permissive licence, the openness gap closes and the comparison becomes a straight capability-and-cost conversation. And if Ant's served long-context window is validated at the 1M tokens both models declare, the context parity claim stops being a claim. Until then, the honest summary is that a model can win an agentic benchmark row and still lose the evaluation — and that the two-point index gap between these models is worth roughly 3.6× the per-task cost, which is a trade only a specific workload should take.