(This article is authored by ZhiXun Think Tank and published with authorization from Titanium Media.)
Article by Yang Faze, Analyst at Zhixun Think Tank
In the early days of smartphone development, launch events often emphasized benchmark scores, and users loved comparing specs. Later, when upgrading, more and more people began choosing a brand first: they were accustomed to the iPhone ecosystem, preferred a particular manufacturer’s camera system, or simply couldn’t part with their already integrated devices and apps. While performance remains important, the reasons for making a purchase have become far more diverse.
For large models, model performance may determine why users come, but it doesn’t necessarily decide why they stay. Model quality is a key weapon in the battle for users, yet this weapon can be repeatedly challenged by competitors. Technological leadership alone cannot automatically translate into lasting competitive advantages.
Since August 2026, large models have been updated at a rapid pace, with GPT‑6 Astra, Claude Opus 5.5, Gemini 4 Argon, and other variants continuously coming into users’ view; domestic models are also gaining a competitive edge in terms of both capabilities and cost, as DeepSeek keeps rolling out updates, while MiMo and Kimi remain among the top choices. [1][2] The more versions there are, the harder it becomes to choose: which upgrade is worth trying, and which product is worth paying for long-term?
As project data, personal preferences, and workflows gradually consolidate within a single AI product, users who switch tools must reconfigure settings, migrate their data, and adapt to new usage habits. Claude, WorkBuddy, and cloud-based Agents are all vying for the same core entry point into users’ daily work.
Today’s discussion around AI leaderboards bears a striking resemblance to the early smartphone market’s obsession with benchmark scores. Large models will follow the same trajectory: users will entrust their tasks to the product that understands their needs, minimizes rework, and allows them to pick up where they left off the next day. Whoever gets this right will be one step closer to becoming the “iPhone” that no one wants to replace.
This article answers three multiple-choice questions:
The AI you’re using isn’t at the top of the list—should you switch? — If your current model can reliably handle critical tasks, there’s no need to chase the latest or most expensive options; upgrades should deliver verifiable benefits.
Which aspect of the AI experience matters most? — Ease of use, meeting delivery expectations on the first attempt, and minimizing rework, verification, and corrections.
Which AI should you use, and how do you choose specifically? — Compare candidate products using the same set of tasks, then allocate primary and specialized roles based on delivery, human effort, and total cost.
Should we replace a model just because it’s no longer state-of-the-art?
A new model that ranks highly on the leaderboard can indeed serve as a compelling reason for users to replace their primary model. However, before that happens, it must first demonstrate that it can deliver results both effectively and efficiently.
The benchmark rankings of AnTuTu and other score‑based leaderboards from the mobile era bear a striking resemblance to today’s model‑performance lists. As of October 21, on the Artificial Analysis leaderboard, Claude Opus 5.5 in its max with fallback configuration scores 58 points, while Sonnet 5.5 at the same tier scores 56; GPT‑6 Astra (max) and Gemini 4 Argon (high) both score 53, DeepSeek V4.1 Flash 1017 (max) scores 45, and Kimi K3 (max) scores 44. [1] Rankings are far from useless—they can help us narrow down our options for testing.
However, the benchmark tests a set of standardized questions, while users are tasked with completing their own work.
Benchmark evaluations, by using fixed tasks, test environments, and scoring rules, enable the capabilities of different models to be compared. In real-world use cases, contracts, reports, or research materials—commonly handled by users—may not carry much weight on aggregate leaderboards. Moreover, actual tasks require models to retrieve project data, invoke tools, and maintain consistent requirements across multiple rounds of interaction. Ultimately, users evaluate whether the delivered results are usable and how much time they have spent on verification and revision.
For example, even when modifying code, testing is only one part of the delivery; it must also comply with project standards and integrate with existing systems. Similarly, for research briefs, in addition to ensuring accuracy, the sources must be traceable, and the conclusions must align with specific decision-making needs.
The ranking helps assess the upper limit of capabilities, while the results of common tasks indicate whether a switch is worthwhile. To truly understand the benefits of an upgrade, you should put candidate products through the same real-world workflows and compare their delivery quality, stability, and rework time.
Meanwhile, price also influences the choice: according to the October 21 ranking, MiMo-V2.6-Pro has an intelligence index of 46, with a per‑task cost of about $0.13; DeepSeek V4.1 Flash 1017 (max) costs approximately $0.26, while GPT‑6 Astra (max) is around $3.26. [2] Beyond capability, cost has secured another ticket of entry for domestic models.
Data note: The scatter plot uses the values shown on the leaderboard, with Claude set to max with fallback, Grok to xhigh, Gemini to high, and all other models with reasoning tiers set to max.
A single call may be inexpensive, but the final delivery isn’t necessarily cheap. Retries, validations, and modifications can all affect costs. When choosing a smartphone, beyond performance metrics, factors like camera quality, battery life, user interface, and price still play a role in the decision-making process. [3] When selecting a model, you should compare its capabilities with your actual usage costs.
You can recognize one as the strongest and still continue using another. When choosing everyday tools, users consider both model capabilities and whether delivery is stable and can be integrated into their existing workflows. If switching models requires too much time and effort, it may end up being more of a loss than a gain.
Who’s vying for the next iPhone spot? Taste is rooted in the operating environment.
What is the “taste” of large models? We use this highly anthropomorphic term to describe the comprehensive capabilities of large models.
Taste is reflected in the trade-offs your product makes on your behalf. “Taste” refers to first‑delivery usability, the number of corrective iterations, verification time, and the cost of project handover. When it’s easy to use and ready for production right out of the box, it delivers far greater long-term value than solutions that require repeated tweaks to achieve a satisfactory outcome.
Similarly, when “organizing materials into a one-page briefing,” some products lay out every detail, while others focus on the facts that matter for decision-making, cutting away the extraneous and first addressing any critical gaps with a clarifying question. When collaborating with AI, a common challenge is this: after several rounds of back-and-forth communication, the deliverable may appear complete yet still fall short of expectations; just as one issue is resolved, previously corrected errors resurface. Even worse, the direction may be off from the very beginning, yet the AI continues to execute all the way through, ultimately consuming a large number of tokens—while the resulting output remains unusable.
Taste lies in these trade-offs: understand what you want to achieve with the results, then decide what to keep, how to present it, and when to deliver. After a few deliveries, users will remember: this product gets me. This logic is like a photograph taken by a high-end camera—non‑professional users can’t articulate exactly what makes it good, but they sense that it’s beautiful and sophisticated. The same holds true for the aesthetic sensibility of large models.
The benchmark set by the iPhone is an integrated experience: multi-touch in 2007, followed by the App Store, which launched in 2008 and initially offered over 500 apps.[4][5] Today’s agent runtimes—platforms that ordinary users interact with via various AI applications—or execution frameworks connect models to tools, memory, permissions, and iterative workflows, aiming to become the gateway for users’ ongoing tasks. Different vendors adopt distinct strategic priorities.
If you have to reintroduce the project every time you start a task, no matter how powerful the model is, it’s hard to feel comfortable using it. Claude Code seamlessly integrates project rules, automatic memory, and execution workflows; WorkBuddy brings project rules, materials, and personal memories into subsequent tasks. [6–9] Trae’s SOLO combines Skills, Commands, Memories, and Rules, while Cursor also uses continuously active rules to guide Agents within projects. [10][11] Once you’ve taught something before, you don’t need to explain it from scratch next time.
Materials, formats, and project rules remain on the local workstation, along with the user’s habits. Re-subscribing to a product is quick, but re-teaching all that accumulated knowledge can be time-consuming.
Local workbench accumulation rules, while the cloud agent further maintains an environment for long-term task execution. Muse provides a dedicated cloud-based computer, Dots preserves files, software, and browser states, and Grok Bot enables bots under the same account to share cloud-based files and login sessions. [12-14]
When tasks span several days, require repeated logins to tools, or involve continuing work on files, whether the environment can be preserved directly impacts the user experience. The next time users return, they can pick up where they left off. This convenience of uninterrupted workflow turns a one-time trial into a regular entry point.
The open roadmap builds the ecosystem from the developer side. DeepSeek Harness is licensed under the MIT License (open source) and turns models, tools, sessions, sandboxes, and execution loops into composable plugins.[15][16] Developers can integrate with existing tools or extend new workflows.
The open‑source approach hinges on developer engagement. Only when the tools integrate smoothly and existing workflows can be reused will developers be motivated to keep building; the more extensible it is, the easier it becomes for newcomers to get started. Compatibility and maintenance quality will determine how far this ecosystem can go.
The ecosystem offers even greater benefits: entry points and distribution. The App Store helps users discover apps while also enabling developers to reach their audiences.[5] AI platforms are vying for the same position: wherever tools are discovered, invoked, and reused, developers have a compelling reason to build around that space. As a result, platforms can keep more tasks within the same environment, and new tools can more easily become part of users’ daily workflows.
When enterprises adopt AI, the user experience must be integrated into a seamless workflow, with factors such as SLA, stability, observability, and auditability all taken into account. Take procurement approval as an example: the agent needs to verify quotes, fill in any missing information, and escalate items exceeding the approved limit to the authorized approver. What matters to the enterprise is whether the next step can proceed smoothly and whether employees spend less time on redundant reviews.
Claude Agent SDK provides developers with tools, context management, permissions, and execution loops. [17] After switching models, tool calls and multi-turn executions need to be re-evaluated, and cache rebuilding may also increase costs and waiting times. [18][19] These are all the expenses enterprises incur when switching tools.
For the inexpensive new model, once it’s integrated into the old workflow, we’ll need to recalculate whether it actually saves money.
A user’s reliance on an AI product depends on the extent to which it integrates into their workflow. If the product is used solely for reasoning or content generation, users can more easily switch underlying models. However, when project rules, tool invocations, and execution workflows all hinge on a single product, replacing it entails migrating an entire way of working, thereby increasing long-term stickiness.
Which AI provider should you choose? Find the one you’re unwilling to switch from.
Who will become the iPhone of the large-model era? You don’t need to chase constantly updated AI rankings. Think back over the past week: did you most often ask AI to research information, draft documents, modify code, or work with spreadsheets? Start by choosing the task you do most frequently and that takes the most time—this approach is far more useful than following every new ranking update.
It can understand your requests, delivers results most of the time, and rarely requires you to explain yourself repeatedly—so there’s no need to replace it just because the rankings have been updated. Within budget and genuinely saves time every day, continuing to use it is a sensible choice.
Mobile users tend to stick with familiar brands, and AI users are gradually developing similar preferences. For users, this means less hassle; for products, it means that user experience is starting to translate into retention.
For example, writing always requires multiple revisions, researching always demands adding sources, and code repeatedly bugs out in the same spot. Take a recent task: give the new product the same materials and requirements—will it work straight out of the box? And how much time will it take to make adjustments?
First, keep the familiar mainstay and let the new product tackle that challenging task. If it delivers hassle-free results several times in a row, then gradually increase its usage and decide whether to pay for it. Each of the two has its own strengths—let them specialize, and there’s no need to stick with just one simply to pick the “strongest.”
What’s most worth keeping is the tool that saves you from making countless tweaks and worrying over and over. The more you use it, the better it becomes—this is the stickiness of the memory mechanism.
Start with a repetitive task, such as compiling weekly reports, verifying data, or resolving customer issues. Have the person who actually performs this work try it out to see whether the delivery is feasible, whether it saves colleagues time, and whether there’s someone to take over if problems arise. Permissions and data boundaries should be confirmed by the responsible colleague; if the pilot proves effective, then expand the scope.
Moreover, individual preferences can also influence an organization’s product selection. Once a user finds a tool easy to work with, they bring along prompts, templates, and success stories into the team. Colleagues, seeing a usable deliverable first-hand, are then more likely to start experimenting with the same product. In this way, personal habits spread through collaboration, often serving as the starting point for enterprise procurement.
From a personal perspective, it’s all about outcomes and ease of use; for enterprises, collaboration and maintenance are also key considerations. If your current solution is working well, proceed with upgrades at your own pace. A detailed table at the end of this article outlines a more granular approach to tracking trial usage.
Users choose their preferred option through repeated comparisons like these, and products strive to transition from “just trying it out” to becoming a “habitual choice.” Only those who can successfully navigate this journey will be closer to that iPhone they refuse to part with.
Capabilities and pricing determine who’s worth trying, while a complete work experience decides who can become the mainstay. Claude’s performance, along with DeepSeek and MiMo’s cost advantages, can all help products secure trial opportunities; Kimi has integrated document‑related delivery features into its Agent, further bridging the gap between capabilities and outcomes. [1][2][20] After securing a trial opportunity, you still need to strive for the next opening.
The workbench approach more directly aims to “work where you are every day.” Claude Code, WorkBuddy, Trae, and Cursor gradually organize rules, materials, and tools around projects; while Muse, Dots, and Grok Bot extend this continuity to the cloud. The former reduces redundant setup, and the latter preserves a continuous working environment—both making it feel more natural to open the product next time.
DeepSeek Harness, on the other hand, emphasizes openness and compatibility as its main attractions, encouraging developers to help refine the tool. Each of these approaches has its own strengths, but ultimately they all seek to answer the same question: after model updates, why would users still choose to stay?
Whoever gets closer to the iPhone is the one who can guide users from “I’ll give it a try this time” to “I’m used to doing this right here.” When daily materials, tasks, and collaboration flow seamlessly, the reasons to stick around will endure far longer than any leaderboard.
The winner in the large-model space may be the company that doesn’t always need to have the strongest model, yet can retain users through an overall superior experience.
Attachment: Selection Records and Evaluation Criteria
Choose three common tasks, such as writing a one-page report, verifying a set of sources, or processing a spreadsheet. Provide the candidate products with the same materials and requirements, then record the results in the table below. What makes Agent Eval worth emulating is that it focuses on whether the task has been completed, rather than relying on the system’s self‑reporting. [21]
Source: Based on the Agent Eval method [21], this paper proposes a design. Critical errors are listed separately, and costs are recorded separately from labor hours. For enterprise pilots, permissions, logs, and failure rollback mechanisms should also be reviewed. Test task costs and subscription prices should be evaluated independently. [2]
Reference material:
[1] ARTIFICIAL ANALYSIS. LLM leaderboard: intelligence[EB/OL]. [2026-10-21].
[2] ARTIFICIAL ANALYSIS. LLM leaderboard: intelligence and cost per task[EB/OL]. [2026-10-21].
[3] AnTuTu. AnTuTu Review: Product Download and Review Instructions [EB/OL]. [2026-10-21].
[4] APPLE. Apple reinvents the phone with iPhone[EB/OL]. (2007-01-09)[2026-10-19].
[5] APPLE. iPhone 3G on sale tomorrow[EB/OL]. (2008-07-10)[2026-10-19].
[6] ANTHROPIC. How Claude remembers your project[EB/OL]. (n.d.)[2026-10-22].
[7] ANTHROPIC. How Claude Code works[EB/OL]. (n.d.)[2026-10-22].
[8] Tencent. WorkBuddy: Project [EB/OL]. (n.d.) [2026-10-22].
[9] Tencent. WorkBuddy: Memory [EB/OL]. (n.d.) [2026-10-22].
[10] TRAE. Introducing the new SOLO[EB/OL]. [2026-10-21].
[11] CURSOR. Rules[EB/OL]. [2026-10-21].
[12] SHEASHA T. How we built safety into Muse[EB/OL]. (2026-09-08)[2026-10-22].
[13] OPENAI. Connect computers and apps to your dot[EB/OL]. (n.d.)[2026-10-22].
[14] XAI. Use the computer and apps[EB/OL]. (n.d.)[2026-10-22].
[15] DEEPSEEK. DeepSeek Harness: everything is a plugin[CP/OL]. [2026-10-21].
[16] DEEPSEEK. DeepSeek Harness developer preview: everything is a plugin[EB/OL]. [2026-10-21].
[17] ANTHROPIC. Agent SDK overview[EB/OL]. [2026-10-21].
[18] ANTHROPIC. Prompt caching[EB/OL]. [2026-10-21].
[19] VLLM. Automatic prefix caching[EB/OL]. [2026-10-21].
[20] Moonshot AI. What is Kimi Agent? Feature overview and access instructions [EB/OL]. [2026-10-21].
[21] ANTHROPIC. Demystifying evals for AI agents[EB/OL]. (2026-01-09)[2026-10-21].
For more exciting content, follow TMTPost on WeChat (ID: taimeiti), or download the TMTPost App.