--- title: "Duolingo Cut Its AI Video Call Cost From $0.30 to Under a Cent. What the Earnings Calls Actually Say" slug: "duolingo-video-call-ai-cost-open-weight-models" author: "ibl.ai" date: "2026-11-09 12:00:00" category: "Premium" topics: "Duolingo AI, Duolingo Video Call, open-weight models, AI inference cost, cost per call, frontier models, model-agnostic AI, LLM cost, AI unit economics, open-source LLM, enterprise AI cost" summary: "Duolingo says the AI inference behind one Video Call fell from about $0.30 to well under $0.01, mainly by moving to open-weight models while keeping frontier models for the few cases that need them. What its two earnings calls support, what the short version leaves out, and why the right to switch models is the real asset." banner: "" thumbnail: "" linkedin: | Duolingo says one AI Video Call used to cost about $0.30 in inference. It now costs well under a cent. CEO Luis von Ahn put it plainly on the November 3 earnings call: the drop came "mainly by moving to open-source models or open-weight models." For most learners, he said, open-weight models are about as good as frontier ones. Two details the viral version leaves out. The figure is per Video Call conversation, not per API request. And Duolingo did not abandon frontier models; it keeps them for the cases where they still win. The real lesson is not "use open models." It is that Duolingo could choose the model per task and change its mind when prices moved. That only works if your product is not wired to one vendor. #iblai #EnterpriseAI #OpenSource #LLM #AIEconomics --- ## The Short Answer **Duolingo says the AI inference behind one Video Call fell from about $0.30 to well under $0.01, mainly by moving to open-weight models. The saving came from choosing the model per task. On ibl.ai you own all the code and the data and run every agent model-agnostic, so that choice stays yours.** The figures come from Duolingo's own earnings calls: the [Q2 2026 call](https://www.fool.com/earnings/call-transcripts/2026/08/12/duolingo-duol-q2-2026-earnings-call-transcript/) on August 5, 2026, and the [Q3 2026 call](https://www.fool.com/earnings/call-transcripts/2026/11/04/duolingo-duol-q3-2026-earnings-call-transcript/) on November 3, 2026. ## What did Duolingo say about its Video Call AI cost? Duolingo's CEO, Luis von Ahn, said on the November 3 call that Video Call "was costing about $0.30 per call, just generally on AI inference." He added: "We've managed to reduce that cost to well under $0.01 per call." He named the cause in the same answer: "that's mainly by moving to open-source models or open-weight models." That is a drop of more than 30 times, a cut of more than 96%. The number is not new this week. Von Ahn first gave it on the August 5 call: "It is now under $0.01 per video call." The November call repeated it with more detail. ## What does the short version of the Duolingo story leave out? The short version leaves out two details, and neither changes the conclusion. First, "per call" here means one Video Call conversation with Duolingo's AI character, not a single request to a model. Second, Duolingo did not switch off frontier models. On November 3 von Ahn said "There are some small cases where a frontier model's still needed." In August he said the company wants "to use open-weight models as much as possible," because "sometimes the frontier models are more advanced." So the accurate version is a mix: open-weight models for the bulk of the work, frontier models where they still earn their price. ## Why did the cost of Duolingo's Video Call matter to its business? The cost decided who could get the feature. At $0.30 a call, von Ahn said in August, Duolingo "decided to put it behind our most expensive plan, which is Max." Once the cost fell, Video Call moved down a tier. On November 3 he said: "Video Call for the longest time was just available in Max. Now that it's in Super." The move also sits alongside Duolingo's plan, stated on the same call, to sunset Max. The CFO, Gillian Munson, said "The AI costs have not been as high as we thought, which is great," and named "less AI costs than we expected" as a driver of gross margin. She also expects margin to dip slightly as Video Call rolls out to all paid subscribers. ## Are open-weight models good enough for production AI features? For most of Duolingo's users, the company says yes. Von Ahn's words on November 3: "for the majority of our users, open-weight models are about as good as frontier models." Duolingo is not the only one reporting this. Revolut says self-hosting open GLM models cut its AI coding costs to roughly a tenth, about $0.30 per million tokens; we went through [what Revolut's numbers actually say](/blog/revolut-self-hosted-open-models-ai-coding-costs). The pattern across both: most production work, conversation practice, completion, extraction, routine drafting, runs well on models that cost a fraction of the frontier. For how open weights now set the price of closed models, see [the open-weight price floor](/blog/open-weight-models-price-floor-enterprise-ai). ## How can an organization switch AI models without rebuilding its product? An organization can switch only if the model is a setting, not a dependency. Duolingo could move because it was free to pick the model for each task. On ibl.ai, each agent's [LLM is chosen per agent](/docs/os/agent-settings/llm-selection). On the organization in our recording, the catalogue listed [45 model providers and 316 chat models on one grid](/updates/llm-catalog-45-providers-any-model); it syncs itself every six hours, and a switch keeps the agent's prompts, data and connectors. [Costs](/docs/os/analytics/costs) then shows what each agent spends, so the saving from a switch is measured rather than assumed. ## Who should own the AI stack that makes a model switch possible? The organization paying for the inference should own the AI stack. A product rented per seat from one model vendor cannot follow prices down; Duolingo's could. On ibl.ai you own all the code and the data, the platform runs model-agnostic, there is no per-seat pricing, and you can deploy anywhere, from your own cloud to a fully air-gapped network. That is the same freedom to choose that cut Duolingo's cost per call, without building the platform yourself. ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on. *Sources: Duolingo's [Q3 2026 earnings call](https://www.fool.com/earnings/call-transcripts/2026/11/04/duolingo-duol-q3-2026-earnings-call-transcript/) (November 3, 2026) and [Q2 2026 earnings call](https://www.fool.com/earnings/call-transcripts/2026/08/12/duolingo-duol-q2-2026-earnings-call-transcript/) (August 5, 2026), transcripts published by The Motley Fool. The brief called it "30x cheaper per call" and a switch to open-weight models; the calls support more than 30 times cheaper per Video Call conversation, first disclosed in August, with frontier models kept for some cases.* ## Why does owning the AI stack matter? **ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.** - **You own all the code and the data.** Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours. - **Model-agnostic.** Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform. - **No per-seat pricing.** Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ. - **Deploy anywhere.** Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University. ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.