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Complete Guide to Prompt Strategy in the Age of Reasoning AI: How to Use GPT-5.6, Claude Fable 5, Kimi K3, and DeepSeek V4 Properly

The 기준 for using AI well is now changing completely.
There is growing talk that prompts like “assign a role, make it think step by step, and give many examples” can actually reduce the performance of the latest frontier models.

In this article, we summarize the latest reasoning AI models mentioned in the original text, including Claude Fable 5, GPT-5.6, Kimi K3, DeepSeek V4, and GLM 5.2.
It is structured so you can see at a glance which model is fast, which is expensive, which behaves too aggressively, and how to change prompts to reduce token costs while improving output quality.

In particular, we will also cover generative AI cost management from a corporate perspective, AI agent operating strategies, changes in token economics, data center investment burdens, and AI infrastructure issues connected to semiconductor stock prices.

1. Core News: The Latest AI Model Market Is Shifting from “Performance Competition” to “Cost-Effectiveness Competition”

The biggest change in the recent AI market is that the era in which a single model dominated every task has ended.
In the past, using ChatGPT alone was enough for most work.
But now, the trend is moving strongly toward using different models for different tasks.

• GPT-family models are strong at classification tasks.
• Claude-family models are highly regarded for creative work.
• For coding and long-horizon tasks, GLM 5.2, Claude, and some Chinese open models are drawing attention.
• For high-volume work and cost reduction, Chinese models such as DeepSeek V4 and Kimi K3 are emerging as options.

This trend is also important from the perspective of the global economic outlook.
If AI model prices fall, companies can put AI into more tasks.
Conversely, if the cost of using high-performance models remains high, AI adoption may concentrate in large enterprises.

In the end, the AI trend is shifting from simply “which model is smarter” to “who can operate AI more cheaply and stably.”

2. The U.S.-China Model Structure: The U.S. Pushes Performance, China Pushes Price

Looking at the structure summarized in the original text, the roles of U.S. models and Chinese models are clearly different.

Characteristics of U.S. Frontier Models

• Closed frontier models such as Claude Fable 5, GPT-5.6, and the Claude Opus line are central.
• Their performance is top-tier, but their cost is high.
• They are strong at complex reasoning, long-horizon tasks, high-quality creative work, and difficult analysis.
• For enterprise use, accuracy is excellent, but token costs can rise quickly.

Models like Claude Fable 5, in particular, tend to try to do too much when the user’s prompt is vague.
From the user’s perspective, it may feel convenient because the model “just handles it,” but for companies, that can turn into an API cost bomb.

Characteristics of Chinese Models

• DeepSeek V4, Kimi K3, GLM 5.2, and the Qwen family are representative.
• They often deliver performance somewhat below or similar to top U.S. models, while being much cheaper.
• They are suitable for high-volume calls, repetitive tasks, and cost-sensitive services.
• However, attention must be paid to prompt structure, mixed languages, overlong responses, and instruction placement.

Chinese models are setting a price floor in the AI market.
In other words, they keep lowering the benchmark for how cheaply AI can be used.

This is connected not only to the AI industry but also to data center investment, power demand, and semiconductor stock prices.
Running high-performance models endlessly requires GPUs and electricity, but if lightweight high-performance models spread, companies’ infrastructure strategies may change as well.

3. The Three Key Criteria for Choosing a Model: Performance, Speed, and Cost

The original text emphasizes three criteria when choosing an AI model.

• First, performance.
• Second, speed.
• Third, cost per task.

Using the smartest model is not always the right answer.
For example, using a top-tier model for a light task like “summarize today’s exchange rate trend” is inefficient relative to cost.
On the other hand, tasks such as investment report writing, code refactoring, and complex market analysis require a high-performance model because accuracy and context retention matter.

Recommended Direction by Task

• Simple search and summarization: lightweight or low-cost models are suitable.
• Cross-analysis of 10 or more documents: moderate to strong reasoning is needed.
• Coding automation and long-running execution: stability for long tasks, such as GLM 5.2 or Claude-family models, is important.
• High-volume API calls: low-cost models such as DeepSeek V4 and Kimi K3 are worth considering.
• Final decision reports: using top-tier models such as GPT-5.6 or Claude Fable 5 is advantageous.

In companies, rather than using only one model, an orchestration strategy that combines multiple models is becoming more important.
For example, use a cheap model for preprocessing and a high-performance model for the final decision.
This is a practical strategy for reducing generative AI costs while maintaining output quality.

4. Why the Old Prompt Formula No Longer Works

In the past, prompt writing commonly used the following approach.

• You are the world’s best expert.
• Think step by step.
• Analyze deeply.
• Refer to the examples below.
• Format the output like this.

But the latest reasoning models already plan internally, review multiple paths, and verify results.
So instructions like “think more deeply” no longer carry the same weight as before.
Instead, they can cause the model to spend unnecessary time thinking and use more tokens.

What matters now is not “making it think more.”
What matters is telling it “how far to think and when to stop.”

Core of the Latest Reasoning AI Prompt: Goal, Success Criteria, and Stop Condition

To use the latest AI agents well, you should clearly write three things at the top of the prompt.

• Goal: what must be achieved.
• Success criteria: what counts as success.
• Stop condition: when it is okay to stop.

For example, if you are writing a market research prompt, it should be revised like this.

Goal:Summarize the main growth drivers of the AI semiconductor market in 2026.Success criteria:Organize 5 key drivers and explain the impact of each on corporate investment and semiconductor stock prices.Stop condition:If more than 80% of the main evidence is secured, stop additional searching and write the answer.Mark uncertain content as an estimate.

The important expression here is “stop once more than 80% is secured.”
If you ask the model to find 100% perfect materials, it may keep searching for unnecessary sources.
In practice, 80% to 90% completion that is enough for decision-making is often more efficient than 100% completion.

6. How to Solve the “It Takes Too Long and Uses Too Many Tokens” Problem

The latest frontier models are smart, but overly proactive.
If you give them even a slightly vague instruction, they will plan, call tools, search, verify, and revise on their own.
This takes time and increases token costs.

Solution 1: Set Tool Usage Limits

If an AI agent can use tools such as web search, code execution, file analysis, and API calls, you need to set limits.

Tool usage conditions:Perform web search at most 3 times.Do not search further if the material is sufficient.If search results conflict, compare only the 2 most reliable sources.

Solution 2: Separate Tasks That Need Approval from Those That Do Not

If you ask the user at every step, the work slows down.
Conversely, if you let it handle everything automatically, it may go in an unwanted direction.
So you need to divide approval criteria.

Tasks that can proceed without approval:Document summarization, deduplication, table organization.Tasks that require approval:Data deletion, sending external links, payment-related actions, code deployment.

Solution 3: Adjust Reasoning Strength by Task

Modern models often include options like reasoning effort, thinking level, or reasoning strength.
You can often choose Low, Medium, High, or Max.

• Low: suitable for simple summaries, translations, and short answers.
• Medium: suitable for general analysis, report drafts, and document comparisons.
• High: suitable for complex strategy formulation, code debugging, and legal/financial analysis.
• Max: best used only for very complex long-term tasks or tasks with high failure costs.

Using High or Max unconditionally is not a good strategy.
In particular, using high reasoning strength for simple tasks can make the output longer and only increase costs.

7. Claude Fable 5 Prompt Strategy: The Core Point Is to Make It Do Less

Claude Fable 5-family models are strong at long-horizon work and deep context understanding.
But that also means they tend to explore too many possibilities.

Useful Sentences for Claude-Family Models

Do not broaden the scope by guessing my intent.If the success criteria below are satisfied, stop immediately.Do not look for additional material beyond what is necessary.If something is ambiguous, ask only one question before proceeding.

Claude-family models often produce better results when you explain “why this task is needed.”
The original text also explains that telling Claude the reason is important.

Reason this task is needed:It is to quickly prepare a draft investment report,so a decision-ready core summary is more important than a perfect academic investigation.

Explaining the reason like this increases the likelihood that the model will avoid excessive expansion and move in line with the purpose.

8. GPT-5.6 Prompt Strategy: The Core Point Is Accuracy

According to the original text, GPT-5.6 is described as a structure that is divided by model size and purpose, like Sol, Terra, and Luna.
Sol can be seen as the flagship, Terra as the balanced type, and Luna as the lightweight type.

GPT-family models are generally good at performing tasks based on purpose.
So it is important to give the criteria for the output first.

Useful Sentences for GPT-Family Models

Priority of the final output:1. Accuracy2. Evidence3. Actionable recommendations4. Concise sentencesAttach evidence to each claim,and if evidence is insufficient, mark it as an estimate.

For GPT-family models, requesting “progress reports” is also useful.
Especially for long tasks, asking what it is doing along the way can reduce drift.

If the task becomes long,report the intermediate progress in no more than 3 lines at the end of each stage.

9. DeepSeek V4 Prompt Strategy: Put Instructions at the End, Reduce Examples

DeepSeek V4-family models are described as highly cost-competitive.
They can be an attractive choice, especially for companies that need high-volume calls.

However, Chinese models like DeepSeek may use a different prompt arrangement than U.S. models.
The original text introduces a strategy of placing instructions after, rather than before, the material.

DeepSeek-Family Prompt Structure

<Material>Put the long document or analysis target data here.</Material><Instructions>Based on the material above, summarize the core content.If the evidence is not in the material, write "Not included in the provided material."Do not make assumptions.</Instructions>

For Chinese models, if a long document comes first, the instructions at the beginning may become diluted.
So it may be advantageous to clearly restate at the end what needs to be done now.

Also, if you include too many examples, the model may become confined by them and the answer may become narrow.
It is more stable to provide the output format and success criteria rather than many examples.

10. Kimi K3 Prompt Strategy: You Must Prevent Overactive Behavior

Kimi K3 is introduced as a model that talks a lot and uses a lot of tokens during reasoning.
Even if it is cheap, allowing overexecution can make actual costs bigger than expected.

Useful Sentences for Kimi K3

Do not explain in a long-winded way.Summarize only the core points in 5 items.If something is ambiguous, do not decide arbitrarily; ask only one question.Do not do additional work until requested.

The especially important expression is “ask a question if ambiguous.”
A highly proactive model like Kimi K3 may interpret the user’s intent on its own.
So ambiguity control is the core point.

If the task conditions are judged to be ambiguous,do not decide for me; first ask me a confirmation question.Ask only one question at a time.

11. GLM 5.2 Prompt Strategy: Make It State the Plan Before Execution

GLM 5.2 is introduced as a model that receives good evaluations for long coding tasks and agentic tasks.
Its strength is that it can keep working without collapsing over time.

However, models that are strong at long tasks also carry the risk of running in the wrong direction for a long time.
So you should make it state the plan before execution.

Useful Sentences for GLM 5.2

Before execution, write the plan first.Limit the plan to 5 steps or fewer.Do not make actual changes or execute anything until I approve.

This is especially important in coding tasks.
If the codebase is large, the model may modify multiple files at once and create unexpected errors.
Therefore, the structure of “plan first, then approval, then execution” is safer.

12. Notes When Using the Qwen Family: It Handles Korean Well, but Chinese and Sino-Korean Terms May Mix In

The Qwen family has strengths in Chinese and Asian languages, so it can also produce good results for Korean tasks.
However, Chinese-style Sino-Korean terms, Chinese expressions, or awkward Korean may sometimes appear.

How to Fix It

• Lower the temperature value to reduce output variability.
• Clearly limit the output language to Korean.
• Include a condition prohibiting Chinese and Sino-Korean terms.
• Request a language review step before the final answer.

Output conditions:Use Korean only.Do not use Chinese, Chinese-style Sino-Korean terms, or Japanese expressions.Before completing the answer, check for awkward translation-like phrasing on your own.

13. A More Important Question Than “Can You Review This?” in the Latest Reasoning Models

In the past, when AI gave an answer, people often asked, “Please review it again.”
But the latest reasoning models often answer after already going through an internal review process.

So rather than simply saying “review it,” it is better to ask “show me the evidence.”

Good Follow-up Questions

Show the basis for this conclusion item by item.Explain which sentence in which source the judgment came from.Mark uncertain parts separately.

This method helps reduce AI hallucinations.
It is especially essential for tasks where evidence matters, such as economic forecasts, investment analysis, and corporate strategy reports.

14. The Most Important Point Rarely Mentioned in Other YouTube Videos or News

The most important point is that “AI model costs may fluctuate like electricity bills in the future.”

The original text mentions DeepSeek’s peak-valley pricing concept.
That means charging higher prices when usage is high and offering lower prices when usage is low.

This is not just a pricing policy.
It is a signal that the AI industry is becoming like the power industry.

Why Is This Important?

• As AI usage increases, data center power demand surges.
• If pressure on the power grid grows, regulations on data center construction may increase.
• If model usage costs vary by time of day, companies’ AI operating strategies will also change.
• AI operations optimization that runs large jobs during cheaper time slots becomes important.
• In the end, AI cost management becomes a separate specialized field, like cloud cost management.

This is not just an AI trend; it is a key variable connected to the global economic outlook.
That is because AI data centers, the power grid, GPU supply, semiconductor stock prices, and cloud costs are all linked in one chain.

Going forward, companies will need to manage not only “which AI model is smart,” but also “when, which model, and at what level of reasoning strength to run.”
That is the real AI cost competitiveness.

15. Practical Prompt Template: A Common Version for the Latest Models

The template below is a structure that can be applied commonly to Claude, GPT, DeepSeek, Kimi, and GLM-family models.

Goal:[Write the result you want in one sentence]Context:[Write the context you are in, the purpose of use, and information about the reader or customer]Success criteria:1. [Condition that the result must include]2. [Quantitative criterion]3. [Evidence or source condition]Constraints:- Do not do unnecessary additional searching.- End the task when the success criteria are met.- If something is ambiguous, do not judge arbitrarily; ask only one question.Tool usage limits:- Search up to [number] times.- File analysis should be performed only within the provided materials.- If external information is needed, request confirmation first.Output format:- Title- Core summary- Detailed analysis- Risks- Action plan

16. One-Line Strategy Summary by Model

• Claude Fable 5: You must clearly define scope and stop conditions so it does not do too much.
• GPT-5.6: You must give the output priority and evidence criteria accurately.
• DeepSeek V4: It is better to place instructions clearly after long materials and reduce examples.
• Kimi K3: You must prevent overactive behavior and make it ask questions when ambiguous.
• GLM 5.2: It is better to have it state the plan first before execution and proceed only after approval.
• Qwen: Korean quality may be good, but Chinese and Sino-Korean term mixing must be controlled.

17. The Economic Meaning of AI Model Competition

This trend is not just a technology competition.
If AI model prices fall, corporate productivity rises and AI agents enter more industries.
Conversely, if high-performance model fees and data center costs rise, the AI adoption gap may widen.

U.S. models keep raising the performance ceiling.
Chinese models keep lowering the price floor.
As these two forces collide, the AI industry is entering a full-scale price war.

From an investor’s perspective, it is not enough to look only at model companies.
You also need to look at GPUs, data centers, power infrastructure, cloud companies, and the semiconductor supply chain together.
The AI trend is now expanding beyond a software issue into a massive infrastructure economy.

< Summary >

For the latest reasoning AI models, goal, success criteria, and stop condition are now more important than detailed prompts.
Claude Fable 5 needs to be prevented from doing too much, while GPT-5.6 needs clear output standards and evidence.
For DeepSeek V4, it is advantageous to place instructions after the data and reduce examples, while Kimi K3 needs overactive behavior controlled.
For GLM 5.2, planning and approval before execution are the core point.
AI model competition is expanding beyond performance competition into an important economic issue connected to price wars, token economics, data center investment, and the semiconductor market.

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*Source: [ 티타임즈TV ]

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