● One-Screen AI Takeover, PPT-Excel Lock-in, Agent Economy Surge
“One-click PPT/Excel” has truly become reality: Why Genspark’s new features are shifting workflows from “tool-hopping” to “single-screen automation”
Today’s post includes the following.
1) The decisive point where PPT moved beyond “making it well” into a stage where humans are simply slower and less efficient
2) What AI Sheets 2.0 means: generating even Excel (DCF/valuation/dashboard) at a “Wall Street template” level
3) The “work operating system (OS)” flow: Google/MS/Notion integrations plus automatic email sorting and reply drafting
4) The real core point most news/YouTube miss: not a “feature list,” but signals that “workflow lock-in + an agent economy” has begun
1) News briefing: Genspark evolves into an all-in-one AI workspace, bundling “docs-sheets-slides-agents” on a single screen
[Key takeaway in one line]
Genspark is not a “chat tool where AI only answers questions,” but is evolving into a form that changes the workflow itself by unifying documents/sheets/slides/design/development/collaboration/automation in one space.
[Why it’s getting attention right now]
– It’s part of the Silicon Valley-born AI workspace category, with a broad feature set and a strong configuration that puts “practical automation” front and center.
– The original narrative emphasizes that it quickly gains enterprise value despite small teams and short development cycles, which itself shows how fast the global AI market is reshaping “productivity software.”
[The bigger picture to view together from an SEO perspective]
This trend isn’t a simple fad; it’s growing in tandem with pressure on companies to cut costs while boosting productivity amid fears of a global economic slowdown.
In other words, AI workspaces are more accurately viewed not as “nice tools,” but as an investment trend aligned with corporate digital transformation 방향 and 방향.
2) Reorganizing feature updates by “work deliverables” (slides/sheets/images/web)
2-1. AI Slides: moving from “pretty drafts” to “near-final quality + partial edits”
[Change emphasized in the original]
– A strong sense that the fatal weaknesses of earlier AI slides—“text breakage and layout collapse”—have been greatly reduced
– It repeatedly emphasizes that small fonts and detailed text do not break.
[The truly important point in real work]
– Beyond simple generation, the core is editing functions such as changing only a selected area (partial edits), adjusting layouts, and refining content.
– This matters because corporate reports and proposal work spend far more time on “incorporating feedback and revisions” than on “creating from scratch.”
[What changes economically]
If presentation production shifts from “high value-added work by specialized personnel” to “standardized automated production,” the labor cost structure and work allocation itself change.
This directly ties to productivity gains and, over the long term, can affect companies’ operating efficiency and even their cost structures (margins).
2-2. AI Sheets 2.0: DCF valuation + scenarios + dashboards “all at once”
[Original summary]
– When asked to calculate a target price/expected price, it produces a sheet structure that can switch between bull/base-case scenarios
– It connects revenue estimates → discounted present value (DCF flow) → visualization dashboards
[The “real” core point here]
– Older AI Excel often stopped at a “table imitation” level, but this case is different in that it performs modeling that maintains a referenced cell structure (linked structure).
– If this works, it becomes not just a one-off calculation, but an “updatable model.”
[From an investment/finance practical perspective]
If this becomes widely used, people in internal FP&A (financial planning) or research will focus less on “drafting” and more on “verification and assumption-setting.”
In other words, rather than AI replacing Excel, it compresses Excel labor and changes the structure of work.
[Keywords connected at the macro level]
The spread of these productivity tools is also linked to Nasdaq volatility centered on tech stocks.
If the market continues to assign a premium to “companies whose profit margins will rise with AI,” tech stocks inevitably become more sensitive to short-term rate moves and Fed policy shifts.
2-3. AI images/design: from merchandise design to infographics, “text quality” is the key
[Original summary]
– Quickly generates product mockups like Tesla merchandise designs
– Emphasizes that text is sharp and suffers less breakage in infographic creation
[Practical usage points]
– Marketing and commerce teams can run “ad creative A/B tests” more frequently.
– Rather than “full replacement” of external agencies or in-house designers, it is more likely to shift repetitive work to AI while humans retain control of core concepts and branding.
2-4. HTML/web generation: a transition from documents directly to “web deliverables”
[Original summary]
– HTML-based output is clean, implementing even UI expressions like glassmorphism
– Includes details such as responsive animations (hover effects)
[Why this matters]
Companies usually convert multiple times—“document (PPT) → web page → landing/sales materials → customer delivery”—and if those conversion costs drop, launch speed increases.
That speed is ultimately competitiveness, and competitiveness is likely to translate into revenue growth.
3) The essence of Genspark: not a “tool,” but a “work operating system (OS) + agent automation”
3-1. External integrations (Google/Notion/MS) enable “execution right where the data lives”
[Original point]
– Integrates with Google services, Notion, and Microsoft services
[The biggest practical change users feel]
– What exhausts people isn’t the work itself, but “moving between tools.”
– When the repeated cycle—open email → move to docs → share → paste into sheets again—shrinks, perceived productivity jumps dramatically.
3-2. Super Agent: automating email classification → summarization → reply drafts
[Original scenario]
– Automatically classifies received proposals/invitations/partnership messages
– Highlights important emails
– Saves even polite rejection email drafts
[Why this is a hint of the “AI agent era”]
Instead of people pushing buttons to use AI, AI “handles tasks first” on defined schedules/rules, and people only approve/edit.
If this pattern spreads, a company’s work KPIs themselves will change.
3-3. Building agents + team chat: “AI becomes a member of collaboration”
[Original point]
– A 10 a.m. email briefing bot
– Automatic creation of polite reply drafts for unanswered emails
– Multiple people working with AI together in a team chat room
[The next stage of collaboration tools]
This is a picture of AI absorbing the “collaboration space” that Notion/Slack used to provide.
Ultimately, collaboration shifts from “documents at the center” to “agents at the center.”
4) Event/pricing info (based on the original): what free and unlimited really means
[Content mentioned in the original]
– Free, unlimited AI chat and AI image features throughout all of 2026
– Promotes “top models” available for unlimited use within those two features, including Nano Banana Pro, GPT Image, Flux, Seedream, Gemini 3 Pro, GPT-5.2, and Claude Opus 4.5
– Limited to January 1–7: 40% off annual plans (mentions savings for Plus/Pro)
[A realistic way to see this]
– Such aggressive promotions are likely aimed less at “user acquisition” and more at “capturing work data/workflow.”
– Once an organization starts placing its work routines onto a specific workspace, switching is no longer a simple tool change; it becomes a change in “how the work operates.”
5) The “most important core point” that other YouTube/news rarely talk about (a blog-oriented reinterpretation)
Core point 1) The competition is no longer “model performance,” but “workflow lock-in.”
People talk about whether GPT is smarter or Claude writes better.
What actually makes money is “where a company’s work gets attached.”
The moment the email-document-sheet-slide-share-approval flow sticks to one product, that product effectively becomes the work OS.
Core point 2) AI agents change the “approval chain” more than they “replace people.”
From a structure where people create and managers review,
to a structure where AI creates drafts and humans verify/make decisions.
This shift shakes organizational operations, hiring, and performance measurement together.
Core point 3) Why Excel automation is scary is not “democratization of analysis,” but “overproduction of analysis.”
If anyone can create a DCF template, the future problem is not “the ability to build,” but the quality of assumptions.
That is, the real competitive edge inside companies shifts from producing numbers to the capability to read markets/rates/competitive environments and design assumptions.
Core point 4) Macro variables (rates, inflation, the dollar) determine the speed of AI tool adoption.
In high-rate environments, companies obsess more over “labor cost reduction + productivity improvement.”
So the spread of AI workspaces must be viewed alongside macro 흐름 like the Fed’s rate path, inflation cooling/re-acceleration, and dollar strength.
This part will be felt much more strongly in real workplaces as it intersects with current investment trends.
6) Practical application checklist: the order to actually “put Genspark to work”
Step 1: Start with integrations
– Connect Gmail/Google Drive or an MS account first
– If you use Notion, connect Notion as well
Step 2: Lock in just one daily repetitive task as an agent
– Automate a fixed routine like “morning email briefing”
– Set reply-draft templates (rejection/scheduling/quote requests) first
Step 3: Connect the report/proposal flow end-to-end in one pass
– Finish sheets (assumptions/tables) → slides (story) → infographics (visualization) within a single workspace
Step 4: Spend human time on fact-checking and assumption validation for the final deliverable
– What AI creates has become faster, and now the risk is “incorrect content that looks plausible.”
– So embedding verification as a habit is even more important in real work.
< Summary >
Genspark is evolving to take PPT/Excel beyond “draft generation” to “near-final output + partial editing + automation,” binding work into a single-screen workflow rather than tool-hopping.
In particular, AI Sheets 2.0’s automatic generation of DCF/scenarios/dashboards dramatically reduces time in finance and planning work, shifting human roles from “writing” to “assumption design and validation.”
The real core point is not model comparisons, but a signal that “work OS lock-in” has begun through external integrations and agent automation.
[Related posts…]
Work automation transformed by AI agents: Who will win the productivity war of 2026?
Interest rates and tech stocks: A summary of how Fed policy changes affect Nasdaq volatility
*Source: [ 월텍남 – 월스트리트 테크남 ]
– 이제 완벽한 PPT/엑셀이 “딸깍”…진짜 업그레이드 된 AI 에이전트[Genspark 신기능]● Send the economic news headline
Why “pull-out-and-use” AI agents are now possible: A core point summary of Claude Skills + a practical implementation roadmap
This article includes the following.
1) A one-shot clarification of what “Claude Skills” exactly are, and how they differ from MCP/agents/GPTs.
2) A hands-on method to design skills like “folders + packages” to automate repetitive work.
3) How they are actually combined in real workflows—from emails to one-page news PDFs, quotes, brand guides, and PPTs.
4) A separate breakdown of the “most important points” that other videos and articles talk about less (reusability, token cost, operations/deployment).
1) News-style core briefing: Why people are excited about “Claude Skills” right now
① One-line definition
Claude Skills are “reusable work packages that bundle my own knowledge/instructions/scripts/resources and let me load them only when needed.”
Like in an RPG where you combine skills such as “chop wood” + “light fire” to make firewood, you can combine work skills like research/summarization/design/documentation to produce deliverables.
② Why it matters now
Because it is a mechanism that shifts AI from “ask once and done” to “automation” that runs repetitive work.
Especially in an environment like today—with high volatility in interest rates and exchange rates and increasing cost pressure—ROI for productivity tools becomes more sensitive.
In the end, from a company/team perspective, the core point is “turning proven prompts into reusable assets,” and Skills make that structurally possible.
2) Concept clarification: Skills vs MCP vs agents (Sub-agents) vs GPTs
① Skills: “Reusable packages I build”
You bundle templates/instructions/scripts/resources you created into one unit, store it, and when triggered (called), it loads only that context and performs the task.
The core keywords are “reusability” and “modularization.”
② MCP (Model Context Protocol): “A connectivity standard for external tools/data”
MCP is closer to a protocol that connects information or external apps/tools that the model did not originally learn.
In other words, if Skills are an “internal package,” MCP feels like an “external integration rail.”
③ Agents (Sub-agents): “Workflow executors that work on your behalf”
They are closer to “workers” that take a user request and execute it step by step, and you typically leverage pre-built forms.
Skills act as a “toolbox” that agents pull out and use when needed.
④ How it differs from GPTs (including the nuance mentioned in the video)
The core point was that there is a difference in the “document automation/visualization/work packaging experience,” and Skills were explained as leaning more toward structuring for operational use by including folders/resources/scripts.
In summary, if GPTs are closer to “customizing a chatbot,” Skills can be designed closer to “customizing work modules (processes).”
3) The structure of Skills: Why the “folder” concept matters
① A Skill is not “one file” but closer to a “package (folder)”
A Skill typically contains things like the following.
– Instruction (prompt) structure
– Scripts (if needed)
– Resources (company guides, templates, color codes, document rules, etc.)
② File formats/writing rules: MD-based + a specified template
One of the emphasized points in the video was that “Skills must follow a specific format, and a structure ending in .md is the default.”
At the top, you place name/description, and below that you include detailed instructions in markdown format.
③ The trigger (invocation phrase) determines quality
In practice, you need to explicitly mention the Skill, such as “Please use the email skill,” to reliably load that package in the flow.
In other words, before having great Skills, you need to build the habit of “invoking them well” for work automation to happen.
4) Practical examples (news-style summary): Where Skill combinations actually work
① Example 1: News URL → a one-page PDF report with only the key takeaway
The components are designed roughly like this.
– News analysis/information gathering method
– Fact-check rules
– Conditions that require additional research
– Report section structure (headline/summary/implications, etc.)
– Design rules (font 9–11pt, line spacing, visualization method)
– Quality verification checklist
If you set it up this way, it becomes a “report production line” that continuously produces deliverables of consistent quality, rather than repeatedly asking “summarize this.”
② Example 2: Quote generation (editable in Excel)
With only minimal inputs—client/project name/unit price/duration/VAT, etc.—it auto-generates in a quote format.
The important point here is: “The more templates and design assets you include, the more the completeness jumps.”
③ Example 3: A brand design guideline Skill
When creating PPTs or PDFs, if you include your preferred color codes/fonts/tone as resources, the document outputs become consistent.
When this scales to the team level, it effectively automates “brand consistency,” which is quite powerful.
④ Example 4: Auto-generating email replies (rejection/meeting/follow-up by type)
Put templates by type—rejection emails, meeting scheduling emails, follow-up emails—into a Skill, and it organizes the reply in the company tone using only the original message.
5) The most important points that “other YouTube videos or news” mention less
① The essence of Skills is not “being good at prompts” but “operational modularization”
Many pieces of content focus on “prompt tips,” but the real value of Skills is in maintenance.
If an error occurs, you do not have to tear apart the entire prompt—just fix that Skill and you are done.
The larger the team gets, the bigger this difference becomes.
② A “selective loading” structure that avoids token costs/context overload
As agents get smarter, context becomes important, but if you paste every rule/template/material into prompts every time, costs increase and accuracy often drops.
Skills have a structure of “load only when needed, and do not use at all otherwise,” so over the long term they can be advantageous in both cost and performance.
This is a point that determines ROI from a company productivity investment perspective.
③ It becomes a “deployable work package”
Most prompts that individuals use well are stuck in their heads and are not documented.
Because Skills aim for “the same result no matter who uses them” from the start, work becomes packaged, making sharing/onboarding/standardization easy.
This ultimately connects to a team’s execution ability, and more broadly, it also aligns with digital transformation.
④ It becomes a “reverse-learning” tool that improves prompt skill
You can create a draft with the Skill creator, then open up and inspect the resulting MD to reverse-learn: “Ah, this part is the tone/structure/verification logic.”
Even if code is difficult, prompts are readable, so the growth speed of practitioners increases.
6) Practical implementation roadmap: The order for turning Skills into a “work automation system”
Step 1. Break work into Skill units
Examples)
– Research Skill
– Summary/report Skill
– Visualization/infographic Skill
– PPT content structuring Skill
– PPT design Skill
– Email response Skill
Step 2. Generate a draft with the Skill creator
Enter “what task it will help with / in what situation it will be used / what the deliverable structure is / what the company guidelines are” to secure a first draft.
Step 3. Attach company standards (tone, templates, terminology) as resources
From here, the deliverable changes from “a plausible AI output” to “a real business deliverable.”
It is more likely to connect directly to real cost savings through team productivity.
Step 4. Standardize trigger phrases as a team rule
Examples)
– “Use the news-summary skill: [URL]”
– “Use the quote skill: [requirements]”
If you standardize invocation formats like this, the difficulty of using it drops significantly.
Step 5. Expand with an agent + Skills combination
If you move to a structure where the agent “executes work step by step” and Skills “load specialized packages only at the necessary moments,” scalability improves.
7) Interpretation from an economic/industry trend perspective: Why this becomes 2025-style “work competitiveness”
These days, companies differ not in AI itself, but in the ability to turn AI outputs into operational processes.
Especially as generative AI becomes commonplace, the gap comes from internal standardization/reuse/quality control rather than model performance.
This trend intersects with moves to reduce labor cost, time, and risk simultaneously amid productivity gains, digital transformation, and global supply chain restructuring.
And over the long term, this operational capability separates corporate competitiveness, investment decisions, and even the speed of responding to market volatility.
< Summary >
Claude Skills are “prompts turned into reusable work packages.”
MCP is external connectivity, agents perform steps, and Skills are modules that selectively load only the necessary context.
They are well-suited for standardizing and automating repetitive work such as emails, one-page news PDFs, quotes, and brand guides.
The real core point is not prompt tips but modularization, maintenance, deployment, and token-cost optimization.
[Related posts…]
- How AI automation affects corporate productivity: A practical implementation checklist
- Five things companies should prepare for amid expanding exchange-rate volatility
*Source: [ 티타임즈TV ]
– 필요할 때마다 ‘꺼내 먹으면’ 에이전트처럼 쓸 수 있는 클로드 스킬 (강수진 박사)
● Minimax M2-1 Shocks AI Automation Costs 10x Cheaper Than Claude ChatGPT
Minimax M2.1 vs Claude vs ChatGPT Hands-On Comparison: Will “One-Tenth the API Cost” Change the Game for Work Automation?
Today’s post includes all of the core points below.
1) Why Minimax M2.1 is disruptive in “token pricing,” and how much costs actually diverge based on real work
2) In three experiments—website creation, Excel analysis, and report writing—how the “real practical deliverables” differed by model
3) How agentic workflows like sub-agents and MCP create leverage for cost reduction plus productivity
4) And I’ll separately summarize the “most important core point (hidden key takeaway)” that other YouTube/news coverage often misses
1) News Briefing: The Conclusion of This Comparison Experiment Up Front
1-1. One-Line Conclusion
Minimax M2.1 delivers an insane cost-to-performance ratio for “coding/agent-style work + repetitive automation,”
Claude is strong in “document structuring/artifact UX,”
and ChatGPT is “generally solid overall, but showed risk of failure depending on conditions/environment (generation failures, text corruption, etc.).”
1-2. Why This Matters Now (Macro Perspective)
For companies to move from “Should we try AI?” to “Should we run it continuously?”, unit cost is ultimately essential,
and this trend is likely to reshape the cost structure of enterprise digital transformation and create a productivity shock (in a good way).
Especially the longer a high-rate environment lasts (even if rate cuts come, “cost sensitivity” remains), companies dislike fixed costs,
and AI will ultimately shift toward “pay-as-you-go optimization” rather than “monthly rent-style subscriptions.”
2) Core Comparison: The Game Is Decided by API Cost (Token Pricing)
2-1. Price Summary Based on the Original Source
Minimax M2.1: input $0.3 / output $1.2
Claude Sonnet 4.5: input $3 / output $15
2-2. Practical Example (Based on the Original Example)
Example: For document summarization/report drafting using 200,000 input tokens + 50,000 output tokens,
Claude is roughly about $1.35,
while M2.1 is roughly about $0.12, and the gap compounds over time.
2-3. Why “Cost Savings” Is Not Just About Saving Money
The real core point is this.
When unit price drops, you can run automation pipelines “more often and more at scale,”
and in practice, “a slightly better model once” loses to “a sufficiently good model 100 times.”
This can connect to enterprise productivity and, over the long term, even macro indicators like GDP growth (via productivity contribution).
3) Hands-On Test 1: Website Creation (Same Prompt, Same Conditions)
3-1. ChatGPT Results
One time, generation failed, and after retrying it produced a text-heavy page with solid structure (program/mentors/testimonials/FAQ/contact/social, etc.).
However, in terms of images/design, it felt relatively weaker as a “finished landing page.”
3-2. Claude Results
A length-limit issue occurred in Opus, so the test proceeded by switching to Sonnet 4.5.
The structure was stable, but it felt conservative overall, with issues like broken (or missing) images.
3-3. Skywork Results
The deliverable was similar to Claude’s (the original source also speculated “Is it using Claude?”),
and image breakage occurred.
3-4. Minimax M2.1 Results
Without a separate HTML download, it could be checked in a deploy-like form with a single click,
and it produced a result closer to a “finished page,” including image insertion, animations, and section-level visual elements (like a five-step flow).
In other words, by the “single prompt” standard, it was evaluated as having the highest completeness.
4) Hands-On Test 2: Excel (Instagram Insights) Data Analysis
4-1. ChatGPT
It did create charts, but there was a Korean text corruption issue,
and it then supplemented with text-based explanations.
4-2. Claude
The visualization and analysis were clean, but English-based output was mentioned as a downside.
4-3. Skywork
It produced a flow similar to Claude’s and was overall solid.
4-4. Minimax M2.1
It organized visualizations/insights in Korean, making them easy to read,
and summarized them in a form that could be used immediately for decision-making, including impression source share, engagement metrics, and a correlation heatmap.
5) Hands-On Test 3: Writing a Global Trend Report on “Physical AI”
5-1. ChatGPT
It wrote well in paragraph form around definitions/trends/company analysis/current status,
but it did not output a document-style deliverable (tables/page structure/downloadable artifact), so readability was weaker.
5-2. Claude
Its strength was an 11-page document-style structure via artifacts, with strong table of contents/organization.
However, too much blank space and weak sourcing were mentioned as drawbacks.
5-3. Skywork
It actively used external search/exploration via MCP and included sources,
and it also supported PDF/Docs/HTML downloads, so it was evaluated as looking best from a “report deliverable” perspective.
5-4. Minimax M2.1
It created the document using a sub-agent combination (report writer + researcher),
and it had the most volume at 18 Word-equivalent pages, with decent tables/structure as well.
However, the HTML download output was corrupted and difficult to read (room for workflow improvement).
6) Minimax M2.1 Practical Feature Core Points: Optimized for “Agent-Style Automation”
6-1. Three Web Modes (Lightning/Custom/Pro)
Lightning: do tasks quickly
Custom: customization such as sub-agents/MCP settings
Pro: feels like it handles things more “as a finished deliverable” on its own
6-2. Sub-Agents
You can combine purpose-built agents like slide maker, report writer, researcher, and PDF/Docs processor,
and role separation helps stabilize the “repetitive work that office workers/startups do.”
6-3. MCP
By connecting tools, access to external search/resources becomes easier,
allowing a shift from simple chat-style AI to “work-executing AI.”
6-4. Local Execution (Open-Source Ecosystem)
It can be used locally via Ollama/Hugging Face,
but the original source notes the model size is about 230GB, so hardware/storage planning is required.
7) The “Most Important Content” That Other YouTube/News Often Doesn’t Mention (Blog Core Points)
7-1. The Real Effect of a “Cost-Effective Model” Is “Optimizing AI Budget from CAPEX to OPEX”
Many pieces of content stop at “it’s cheap/it performs well,”
but in practice, the bigger change is that the budget execution structure shifts.
When token unit price is low, automation can run continuously at the team level,
and AI becomes “work infrastructure,” not a “tool used occasionally.”
7-2. Performance Comparison Is Now More About “Deliverable Pipelines” Than “Benchmark Scores”
The reason Minimax performed strongly in website creation was not just coding ability,
but because it pulled the “chain to the final deliverable” all at once—images/animations/section structure included.
Going forward, the battleground is not model performance but “workflow completeness” with sub-agents/MCP/tooling attached.
7-3. “Transparent Token Disclosure” Is a Killer Point for Enterprise Adoption in Cost Control (Governance)
As mentioned in the original source, showing token usage transparently
directly ties to internal controls for companies (cost allocation by department, ROI measurement, usage policies).
If this doesn’t exist, AI rollout eventually gets blocked by the CFO/finance team.
7-4. “Korean Quality + Visualization + Downloadable Deliverables” Is More Critical Than You Think in Domestic Work
Korean text corruption in Excel analysis may seem minor,
but it determines whether you can put it straight into a report/meeting deck or not.
For domestic teams (marketing/sales/planning), this directly impacts productivity.
8) Implications of This Trend for the Global Economy/AI Market in 2026
8-1. When “AI Cost Deflation” Fully Kicks In, Automation Demand Actually Explodes
AI usage has very high price elasticity.
When unit price drops, companies move toward “expansion” rather than “saving.”
8-2. Startups’ Model Selection Criteria Will Change
Now, rather than “one best-performing model,”
the default set may become “good-enough performance + low unit price + agent/tooling ecosystem.”
8-3. Big Tech Will Also Face Pressure on Pricing/Bundling Policies
As cost-efficient models spread, premium pricing strategies of incumbents will inevitably be shaken,
and they are highly likely to respond with subscription bundles (office/cloud) or enterprise lock-in.
9) Practical Application Guide: “Which Model Should I Use?”
9-1. Recommended: Minimax M2.1
Office workers/startups that run a lot of repetitive automation (summaries, report drafts, data analysis, agentic coding)
Teams where cost optimization is important under pay-as-you-go APIs
Cases where you want to build work pipelines with sub-agents/MCP
9-2. Recommended: Claude
Cases where you need to quickly produce document structure/artifact-based deliverables
Teams where the “format” of presentations/planning documents is important
9-3. Recommended: ChatGPT
Cases where you need general-purpose work + idea expansion + reliable conversational work
However, environment-dependent issues like files/visualization/Korean rendering should be checked in advance
< Summary >
Minimax M2.1 is priced at about 10% for input and 8% for output compared to Claude, creating a structure where cost gaps compound in repetitive automation.
In website creation, M2.1 was strong in finished deliverables including images/animations, and in Excel analysis M2.1 had an advantage in Korean readability.
For report writing, Skywork looked best with MCP + sources + downloadable deliverables, and M2.1’s strength was volume/structure with an 18-page document output.
The real core point is that enterprise productivity and AI adoption speed are determined more by “low unit cost + agent/tooling + cost governance” than by “model performance.”
[Related Posts…]
- How AI Cost Reduction Changes Startup Productivity
- Key Points Where Corporate IT/AI Investment Strategy Changes After Rate Cuts
*Source: [ AI 겸임교수 이종범 ]
– 미니맥스 M2.1 vs 클로드 vs 챗GPT 실전 비교 | API 비용 10분의 1로 업무 자동화하는 방법
