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After a phase in which AI labs competed on benchmark scores and “launch” announcements, the market is moving into a more pragmatic stage: whoever can optimize inference, API pricing, and integration into real workflows will have the edge. For Vietnamese marketers, this is not just a technology story but a signal about content costs, testing speed, and how to choose AI tools for the team.
- Key point: The AI race is moving away from “pretty scores” toward deployment efficiency and cost per task.
- Many signals show that benchmarks are only the first step; price, cache, context, and real-world usability are what determine the advantage.
- Businesses will need to evaluate models by “usable value” rather than reputation or scores alone.
- The Vietnamese market has an opportunity to take advantage of cheaper AI to test quickly, but it must design an internal validation process.
What is happening
Looking at recent developments, AI is no longer being framed as a race to find “the smartest model,” but as a race to find “the model that is usable, cheaper, and easier to bring into production.” DeepSeek V4 Flash has just moved out of preview with a major jump on DeepSWE, according to Bridgebench, while DeepSeek itself announced a public beta API and emphasized agent capabilities, the Responses API format, and Codex compatibility. Artificial Analysis also recorded DeepSeek V4 Flash 0731 at an Intelligence Index of 50, approaching larger rivals while still maintaining a significant advantage in Cost per Task thanks to a very deep cache hit discount policy on its first-party API.
At the same time, the broader market also shows that the focus has changed. Xiaoyin Qu says the new phase is “inference optimization,” where labs must compete on price and inference efficiency to serve users more cheaply. ZenMux uses DeepSeek V4-Flash itself as an example of the “try it immediately on real workloads” trend, emphasizing free access so users can test it on programming tasks. In another area, Alibaba Qwen continues pushing into deep-context problems such as ASR, domain-term recognition, and turning speech into structured transcripts. Together, these developments point to a common conclusion: AI is entering an era in which value lies in the operational layer, not just the demo layer.
What is notable is that even when benchmarks rise sharply, the question from professional users does not change: does this model help get real work done better, faster, and cheaper? DeepSeek being mentioned by Bloomberg as a public beta API, along with feedback from the developer community about real-world testing experiences, shows that “theoretical capability” now has to pass the “deployment test” before the market will recognize it.
Why the advantage is shifting to inference cost and integration
DeepSeek is the clearest example of how benchmarks are no longer the finish line. Bridgebench noted that from preview to release, DeepSeek V4 Flash’s DeepSWE score jumped dramatically; Artificial Analysis, meanwhile, places the model near the Pareto zone between intelligence and cost per task. Those two data points show that the same model can be seen as both “more powerful” and “more economical,” and the real advantage lies in whether it is good enough for users to accept it as a replacement for more expensive options.
The second point is integration. DeepSeek emphasizes support for Responses API and Codex workflows; ZenMux, meanwhile, cites its ability to serve programming tasks, from Terminal Bench to DSBench-FullStack. When a model does more than answer chat prompts and moves into agents, code, tool use, and workflows, enterprise buying criteria change: compatibility, latency, limits, pricing structure, and stability become as important as, or even more important than, a single benchmark score.
On the other side, community reactions to Grok’s pricing packages offer a reverse lesson: even a good model will struggle to achieve real adoption if its quota and pricing structure are not reasonable. Kun Chen’s comment that consumer pricing does not create a business advantage, along with the addition of SuperGrok Plus as a lower-priced middle tier beneath the premium plan, shows that the market is learning how to “package” AI into consumer products people can actually buy. In other words, the advantage no longer lies in who has a slightly stronger model, but in who makes that model easier to use within a real budget.
The real cost of the race is trust in benchmarks
When scores rise too quickly, professional buyers will automatically question the testing conditions. Bridgebench pointed to a footnote, emphasizing that the agent test ran on DeepSeek’s own harness; that reflects a broader industry issue: benchmarks can sometimes measure capability under conditions that are “optimized for measurement,” but not necessarily real-world effectiveness. This also aligns with feedback like “try it on real workloads” from ZenMux and the developer community.
The risk here is not that benchmarks are useless, but that they are easy to overinterpret when separated from deployment context. Artificial Analysis shows that a model can be highly competitive on cost/performance, but the final decision is still an ecosystem question: does the model integrate deeply, does it have a stable API, does it have a pricing policy that is easy to predict, and can it scale with demand? Even signals from other companies such as Qwen with domain-specific ASR or Google DeepMind’s Gemini Robotics 2 are saying the same thing: AI value is fragmenting into specific application layers, no longer contained in a single ranking table.
For businesses, the real cost of the AI race is not just API spend. It is also the cost of trial and error, the cost of training teams, and the cost of integrating into CRM, CMS, ad ops, or creative pipelines. The model that reduces that total cost is the winning model, even if it is not always at the top of every ranking.
A perspective for the Vietnamese market
For Vietnamese marketers, this trend opens up two major opportunities. First, models are becoming cheaper and offering more layers of trial access, so marketing teams can quickly test use cases such as ad copywriting, customer insight summarization, sales chatbot creation, or multilingual content production support. Second, as the race shifts toward operations, Vietnamese businesses do not necessarily need to chase the most expensive model; instead, they should choose the model that fits each task and measure it by real performance.
However, opportunity only becomes an advantage if businesses know how to segment their needs. A model used for content brainstorming may not be suitable for customer support or voice workflows. Qwen’s story about contextual ASR and DeepSeek’s story about API/agent capabilities show that each problem should have its own selection criteria: accuracy, speed, cost, integration capability, and data security. This is also the moment when Vietnamese marketing teams need to work more closely with IT, data, and operations, rather than buying AI tools based on instinct.
What to do now
- Build an internal AI evaluation framework based on 4 criteria: output quality, cost per task, latency, and integration capability.
- Test at least 2 models on the same real workflow, instead of only looking at demos or benchmark scores.
- Prioritize use cases with fast ROI such as content ops, research, lead classification, call summarization, and customer support assistance.
- Design testing processes using Vietnamese and the brand’s real data, because a globally strong model is not necessarily strong in a local context.
If we have to reduce it to one conclusion: the AI race is moving off the stage and into the operations room. Whoever makes AI cheaper, easier to integrate, and more measurable in real-world effectiveness will gain the long-term advantage.
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This article focuses on the AI race shifting to operational optimization with a perspective for the Vietnamese market.
References
- DeepSeek V4 Flash just came out of preview. The preview scored 7.3 on DeepSWE. The release scores 54.4.
- With DeepSeek v4 flash api launch, we are now entering the phase of inference optimization.
- Zenmux consistently makes it effortless to get hands-on experience with newly released models
- DeepSeek V4 Flash 0731 scores 50 on the Artificial Analysis Intelligence Index
- China’s DeepSeek rolls out a public beta API for its flagship AI model
- DeepSeek-V4-Flash Official API is now LIVE in public beta!
- Introducing Qwen-Audio-3.0-ASR-Flash: More context-aware. Stronger domain-term recognition.
- grok 4.5 is a great model … but i think its consumer adoption is heavily limited by its current terrible pricing strategy
