DEV Community: Tom Tokita

Vibe Coding Works. Until It Doesn't. What the Vercel Breach Should Teach Every Developer.

Tom Tokita — Mon, 27 Apr 2026 07:04:40 +0000

The vibe coding risks most developers ignore became impossible to deny on April 19, 2026. That's when Vercel — the platform half the Philippine dev community deploys on — disclosed a security breach. A threat group called ShinyHunters claimed to be selling stolen data for $2 million on BreachForums.

The breach didn't come through a firewall exploit. It didn't come through a brute-force attack. It came through an AI tool.

A Vercel employee had connected Context.ai, a third-party AI productivity tool, to their Google Workspace. Context.ai got compromised. That compromise cascaded into Vercel's internal systems. Customer environment variables — API keys, tokens, database credentials — were exposed. The intrusion reportedly started in June 2024. It wasn't detected until April 2026. Twenty-two months.

That's the reality of building on platforms you don't understand.

Vibe Coding Is Real. I Use It. But the Risks Are Not Hypothetical.

I'm not here to tell you to stop using AI for coding. I use it every day. Claude, GPT, Gemini — I route between three to five LLMs daily in production. AI-assisted development is how I ship at the pace I do as a lean startup CEO running Aether Global Technology.

But there's a difference between using AI as a tool within a system you understand, and using AI as a replacement for understanding the system at all.

That difference is what separates a production application from a demo that dies the moment real traffic hits it.

The term "vibe coding" was coined to describe building software through AI prompts — describing what you want, letting the model generate the code, and shipping it without necessarily understanding every line. Platforms like Lovable, Bolt, Cursor, and v0 have made this accessible to anyone with a browser. That's genuinely powerful.

It's also genuinely dangerous when it becomes your entire engineering strategy.

The Numbers Behind Vibe Coding Risks

Vibe coding risks fall into three categories: the code itself has verified security flaw rates approaching 50%, the tools generating it are under active attack, and the platforms you deploy on have been breached for months without detection. Here's the evidence.

Layer	Risk	Evidence
Code output	Nearly half of AI-generated code has security flaws	CSET Georgetown, Veracode 2026
AI tools	8 CVEs in 3 months, 135K exposed instances	OpenClaw, SecurityScorecard
Infrastructure	22-month undetected breach via AI tool	Vercel / ShinyHunters 2026

And the research keeps piling up:

Nearly half of AI-generated code contains exploitable bugs — across five major LLMs tested (CSET Georgetown, 2024).
45% of AI-generated code has security flaws across more than 100 large language models (Veracode, 2026).
AI-generated code creates 1.7 times more issues than human-authored code in pull request analysis (CodeRabbit).
43% of AI-generated code changes require manual debugging in production — after passing QA and staging (Lightrun, 2026).
4x growth in duplicated code blocks since AI coding tools became mainstream, suggesting copy-paste from training data without architectural judgment (GitClear, 2025).

These aren't hypothetical risks from academic papers. These are measured failure rates from deployed systems.

The AI Tools Themselves Are Getting Hacked

It's not just the code that's the problem. The tools generating the code are under active attack.

OpenClaw, the open-source AI agent that went viral in early 2026, has accumulated eight CVEs in just three months:

CVE	What It Does
CVE-2026-25253 (CVSS 8.8)	One-click remote code execution — steals your auth token through WebSocket, works even on localhost
CVE-2026-24763	Command injection through Docker sandbox PATH manipulation
CVE-2026-25593	Unauthenticated command injection via WebSocket config write
CVE-2026-26317	Cross-site request forgery — no origin validation on localhost
CVE-2026-40037	Request body replay leaking sensitive data across redirects

SecurityScorecard found 135,000 internet-exposed OpenClaw instances. Infosecurity Magazine flagged 63% as vulnerable. Over 12,800 were directly exploitable via the patched RCE — meaning they hadn't even updated. Belgium's national cybersecurity center issued an emergency advisory: patch immediately.

And then there's the ClawHavoc campaign — malicious "skills" distributed through OpenClaw's community registry, deploying information-stealing malware to developers who thought they were installing productivity tools.

The Platform, the Agent, and the Code — All Compromised

Here's the pattern that should concern every developer in the Philippines:

Your deployment platform (Vercel) got breached through an AI tool an employee used. Twenty-two months of access before anyone noticed.

Your AI coding agent (OpenClaw) has eight CVEs, 135,000 exposed instances, and an active malware campaign targeting its plugin ecosystem.

The code your AI generates has a 45% security flaw rate and 1.7 times more issues than what a human writes.

The entire stack — from infrastructure to agent to output — is compromised if you don't understand what you're deploying.

Why Vibe Coding Risks Hit the Philippines Hardest

Vercel and Next.js are the default stack for a huge segment of Filipino developers. Bootcamp graduates, freelancers on Upwork, startup CTOs — this is the ecosystem. When Vercel gets breached, it's not a distant Silicon Valley story. It's the platform your client's app is running on.

The Philippines has one of the fastest-growing developer communities in Southeast Asia. AI adoption is accelerating. But the gap between "I can prompt an AI to build an app" and "I can deploy and maintain a secure production system" is enormous. The 2024 data on AI adoption in the Philippines tells the story: 92% of organizations experimented with AI, 65% got stuck in pilot, and only 3% achieved full adoption. That gap isn't a technology problem. It's a systems thinking problem.

Vibe coding in the Philippines carries an additional layer of risk: many freelancers and small dev shops are building client applications on these platforms without dedicated security teams, without infrastructure expertise, and without the budget for recovery when things go wrong.

Vibe coding without systems thinking is like drawing a blueprint on paper. It looks right. It communicates the idea. But the moment it gets wet — real traffic, real attackers, real edge cases — it's destroyed.

Beyond Vibe Coding: What Production Actually Requires

I'm not arguing against AI-assisted development. I'm arguing for combining it with fundamentals that vibe coding alone will never teach you:

Infrastructure. Understand where your code runs. Know the difference between a serverless function and a container. Know what environment variables are and why they need rotation policies. The Vercel breach exposed credentials that developers stored in plain env vars — because the platform made it easy and nobody questioned it.

Hardening. Every deployment needs security headers, input validation, authentication checks, and rate limiting. AI-generated code suggests vulnerable patterns more often than secure alternatives. If you can't read the code and spot what's missing, you can't ship it.

Edge cases and failure modes. AI generates code for happy paths. Production runs on unhappy paths — connections drop, requests time out, databases lock, users do things you never imagined. The 43% debugging-in-production rate exists because AI doesn't think about what happens when things go wrong.

Dependency auditing. AI tools pull in libraries without verifying them. The ClawHavoc campaign exploited exactly this — developers installing unvetted extensions because the tool made it frictionless. Every dependency is an attack surface. This is the same pattern that makes unsupervised AI agents dangerous in production — the absence of review loops.

Deployment pipelines. If your deployment process is "push to main and Vercel handles it," you've outsourced your entire release safety to a platform that just got breached for twenty-two months. CI/CD, staging environments, rollback procedures — these exist for a reason.

In the Philippines, where most dev teams are small and move fast, these fundamentals get skipped because the tooling makes it easy to skip them. That's exactly why they matter more here.

The Survival Engineer's Take

I built a production AI operations system out of necessity — not as a product, but as a survival tool for running a lean startup where I wear ten hats. That system uses AI constantly. It also has enforcement hooks, anti-fabrication rules, credential rotation, deployment gates, and rollback procedures.

The AI makes me faster. The systems thinking keeps me alive.

Vibe coding is a tool. A good one. But if you're building your career or your company on apps that were prompted into existence without understanding what holds them together, the Vercel breach is your preview of what's coming.

Learn the fundamentals. Not instead of AI. Alongside it.

Frequently Asked Questions

Is vibe coding safe for production applications?

Vibe coding can produce working prototypes quickly, but the research shows significant risks for production deployment. Veracode's 2026 report found that 45% of AI-generated code contains security flaws, and Lightrun's survey found that 43% of AI-generated code changes require manual debugging in production. Vibe coding is safe when combined with code review, security auditing, proper infrastructure knowledge, and deployment pipelines. Without those fundamentals, it's a liability.
What happened in the Vercel breach of April 2026?

Vercel disclosed a security incident on April 19, 2026. A third-party AI tool called Context.ai was compromised, which gave attackers access to a Vercel employee's Google Workspace account. That access cascaded into Vercel's internal systems, exposing customer environment variables including API keys, tokens, and database credentials. The intrusion reportedly began in June 2024 — a 22-month dwell time before detection. The threat group ShinyHunters claimed responsibility.
What are the biggest security risks of AI-generated code?

The three main risk layers are: (1) the generated code itself has verified flaw rates approaching 50% across multiple studies, including SQL injection, XSS, and hardcoded credentials; (2) the AI coding tools have their own vulnerabilities — OpenClaw accumulated eight CVEs in three months with 135,000 exposed instances; and (3) the deployment platforms developers rely on are themselves targets, as the Vercel breach demonstrated.
How can Filipino developers reduce vibe coding risks?

Focus on five fundamentals that vibe coding alone won't teach you: understand your infrastructure (don't treat deployment as a black box), harden every deployment (security headers, input validation, rate limiting), test edge cases and failure modes (AI codes for happy paths only), audit dependencies (every library is an attack surface), and build proper deployment pipelines (CI/CD, staging, rollback). Combine AI-assisted development with these practices — the speed of AI plus the safety of systems thinking.

Tom Tokita is an AI consultant and operations architect based in Manila, Philippines. He co-founded and runs Aether Global Technology Inc., a Salesforce consulting partner. He routes between 3-5 LLMs daily in production — not demos, not POCs.

Best LLM for Each Task: A Practitioner’s Reference Guide

Tom Tokita — Mon, 27 Apr 2026 06:59:43 +0000

Most AI vendors sell you one model at a flat fee. It works — until it doesn't.

Here's the pitch: "Unlimited AI, fixed price!" Under the hood, they've slapped a single budget model on everything — your customer support bot, your code reviews, your data analysis, your document generation. It handles the simple stuff fine. Then you ask it to reason through a complex business decision, and it confidently gives you an answer that's completely wrong.

You go back to the vendor. Their response? "You need to upgrade to the premium model." That's not an upgrade problem. That's a model selection problem — and you just paid to discover it the hard way.

Choosing the best LLM for each task is an architecture decision, not a shopping decision. LLMs are not interchangeable. Each model family is built with different strengths, different architectures, and different cost profiles. Using the wrong one doesn't just waste money — it produces hallucinations, missed context, and confidently wrong outputs that kill trust in AI across your team. (New to LLMs? Start with What Is AI, Really? for the fundamentals.)

Full disclosure: I use Claude as my primary daily driver. Where that might bias my recommendations, I've noted alternatives and linked directly to provider docs so you can verify independently.

This guide is your reference point. Bookmark it. Come back when a vendor tells you their tool "uses AI" and can't tell you which model — or why.

Why One LLM Doesn't Fit Every Task

If you've ever wondered how to decide which LLM to use, the answer starts with understanding what each model was actually built for.

Think of it like hiring. You wouldn't hire a junior analyst to architect your enterprise data platform. You also wouldn't hire a principal architect to sort spreadsheets — not because they can't, but because you're burning $300/hour on a $30 task.

LLMs work the same way:

Frontier models (Claude Opus, GPT-5.4, Gemini 3.1 Pro) are deep thinkers. They reason through multi-step problems, hold massive context windows, and produce nuanced output. They also cost 10-50x more per token than lightweight models.
Mid-tier models (Claude Sonnet, GPT-5.4 mini, Gemini 3 Flash) hit the sweet spot — fast enough for production, smart enough for most tasks, and priced for volume.
Lightweight models (Claude Haiku, GPT-5.4 nano, Gemini 2.5 Flash-Lite, DeepSeek V3.2) are built for speed and cost. They're excellent at structured extraction, classification, simple Q&A, and high-volume processing. Ask them to architect a system or reason through ambiguity? That's where hallucinations start.

The right approach is task routing — matching each task to the model that handles it best. Your total cost drops, your quality goes up, and you stop blaming "AI" for problems that are really model mismatch.

The Task-Model Matrix: Best LLM for Each Task

This is the reference table. Every recommendation comes from daily production use, cross-referenced with each provider's own documentation.

Task	Best Pick	Runner-Up	Why It Wins	Avoid
Complex reasoning & architecture	Claude Opus 4.6	GPT-5.4	Extended thinking, 1M token context, multi-step logic chains	Lite/Nano models — they hallucinate on multi-step reasoning
Production code generation	Claude Sonnet 4.6	GPT-5.4 mini	Fast + code-native, 64K output, strong instruction-following	Budget models — inconsistent on large codebases
Agent orchestration & tool use	Claude Opus 4.6	Grok 4.20 multi-agent	Reliable function calling, long-context planning, handles complex tool chains	Any "lite" model — they lose track of multi-turn tool sequences
Content writing & copywriting	Claude Sonnet 4.6	GPT-5.4	Natural voice, strong style control, follows nuanced instructions	DeepSeek, Grok fast — flat tone, poor style adaptation
Data extraction & structured output	Gemini 3 Flash	DeepSeek V3.2	Fast JSON mode, schema adherence, cheap at scale ($0.50/MTok in, $3/MTok out)	Frontier models — overkill, 10x+ cost for the same result
High-volume classification	Gemini 2.5 Flash-Lite	GPT-5.4 nano	$0.10/MTok input — pennies per thousand calls, fast enough for real-time	Any full-size model — you're paying for intelligence you don't need
Quick Q&A & chatbots	Gemini 2.5 Flash-Lite	Claude Haiku 4.5	Sub-second latency, low cost, good enough for conversational retrieval	Frontier reasoning models — latency kills UX, cost kills margin
Deep research & analysis	Claude Opus 4.6 (extended thinking)	Gemini 3.1 Pro	Can reason through 1M+ token contexts, extended thinking for deliberate analysis	Anything under 128K context — literally can't fit the data
Budget-conscious general use	DeepSeek V3.2	Gemini 2.5 Flash	$0.28/MTok input, $0.42/MTok output — 10x cheaper than most competitors at reasonable quality	Free tiers with rate limits — they throttle when you need them most

Every link above goes to the provider's official docs — no third-party benchmarks, no secondhand claims.

How to Choose the Right LLM: The Task-First Framework

Forget "which AI is best." The right question is: best for what?

Here's the framework I use across every production deployment:

1. Define the task type first. Is it reasoning, generation, extraction, or routing? Each has fundamentally different requirements.

2. Match to a model tier.

Needs to think? → Frontier (Opus, GPT-5.4, Gemini 3.1 Pro)
Needs to produce? → Mid-tier (Sonnet, GPT-5.4 mini, Gemini 3 Flash)
Needs to classify or extract? → Lightweight (Haiku, Nano, Flash-Lite)

3. Check the context window. If your task involves processing documents, code repositories, or conversation histories longer than 128K tokens, most lightweight models are physically incapable of handling it. This isn't a quality issue — the data literally doesn't fit.

4. Calculate the real cost. A $5/MTok model that gets it right on the first try is cheaper than a $0.10/MTok model that needs three retries and human review. Factor in error correction, not just token price.

5. Test with your actual workload. Benchmarks measure synthetic tasks. Your data, your prompts, your edge cases — those are what matter. Run a 100-call sample before committing.

Best LLM for Coding and Development

This is where model selection matters most, because bad code from an AI doesn't just waste tokens — it wastes developer hours debugging AI-generated bugs.

For code generation in production, Claude Sonnet 4.6 is the current leader. It handles multi-file edits, understands project context, and follows coding conventions consistently. At $3/MTok input and $15/MTok output, it's the workhorse — fast enough for iteration, smart enough for production-grade output.

For architectural decisions and complex debugging, Claude Opus 4.6 with extended thinking is the pick. The 1M token context window means it can hold an entire codebase in context. At $5/MTok input, it's expensive for bulk work — but for the tasks where getting it wrong costs days of rework, it's the cheapest option you have.

GPT-5.4 mini is a strong runner-up at $0.75/MTok input — particularly for code reviews, test generation, and structured refactoring where you need speed over depth.

What doesn't work: lightweight models for code. GPT-5.4 nano and Gemini Flash-Lite will generate syntactically valid code that has subtle logic errors — the kind that pass linting but fail in production. The cost savings evaporate when your team spends hours tracking down AI-introduced bugs.

Best LLM for Reasoning and Analysis

If you're asking "which LLM is best for research," the answer depends on what kind of research.

For deep analysis — parsing contracts, evaluating strategy documents, synthesizing research across hundreds of pages — you need extended thinking capabilities and large context windows. Claude Opus 4.6 with extended thinking leads here. It doesn't just retrieve information; it reasons through it, surfacing connections and contradictions that faster models miss.

GPT-5.4 at $2.50/MTok input is competitive for research tasks, especially when you need web grounding via OpenAI's built-in web search.

Gemini 3.1 Pro brings serious context capacity and Google's search integration, making it strong for research that needs real-time information.

For quick fact extraction from structured documents, you don't need any of these. Gemini 2.5 Flash at $0.30/MTok handles it fine. The key insight from context engineering applies here: it's not just about the model — it's about what context you feed it.

ChatGPT vs Claude vs Gemini: Which Is Actually Better?

This is the most common question, and it's the wrong one. "Which is better" assumes one winner across all tasks. There isn't one.

Here's the honest breakdown from production use:

Category	Claude	ChatGPT (GPT-5.4)	Gemini
Code generation	Strongest — Sonnet 4.6 is the daily driver	GPT-5.4 mini is a close second	Gemini 3 Flash is capable but less consistent
Instruction-following	Best in class — follows complex, multi-constraint prompts reliably	Good, occasionally overinterprets	Tends to be verbose, sometimes ignores constraints
Content writing	Natural, adaptable voice	Solid but can lean generic	Tends toward formal/corporate tone
Cost efficiency at scale	Mid-range ($1-5/MTok input)	Premium to mid ($0.20-2.50/MTok input)	Best value — Flash-Lite at $0.10/MTok
Context window	1M tokens (Opus/Sonnet)	Not publicly listed for 5.4	Up to 1M+ (Gemini 3.1 Pro)
Reasoning depth	Opus extended thinking is top-tier	GPT-5.4 is strong, less transparent	Gemini 3.1 Pro competes but less tested
Speed	Haiku is fastest in class	Nano is competitive	Flash-Lite wins on pure throughput
Tool use / agents	Opus leads — reliable multi-tool chains	Improving rapidly	Strong but newer ecosystem

The point isn't that Claude wins everything (it doesn't). It's that each model family has tasks where it's the clear best pick and tasks where it's a waste of money. The vendors who sell you one of these as "the AI solution" are leaving performance and budget on the table.

Best LLM for Orchestration and Multi-Agent Systems

This is where most AI tools being just LLM wrappers becomes a real problem. Agent orchestration — where an AI coordinates multiple tools, APIs, and sub-tasks — requires a model that can:

Maintain context across dozens of tool calls
Decide which tool to use and when
Handle failures and retry logic
Not hallucinate tool parameters

Lightweight models fail catastrophically here. They lose track of the conversation after 3-4 tool calls, start hallucinating function names, and make confident decisions based on context they've already forgotten.

Claude Opus 4.6 is built for this — Anthropic explicitly positions it as "the most intelligent model for building agents." The 1M token context means it can hold the full history of a complex multi-step workflow.

Grok 4.20 multi-agent from xAI is a contender at $2/MTok input with a 2M token context window — the largest available — and explicit multi-agent support.

The production pattern that works: use a frontier model as the orchestrator and lightweight models as workers. The orchestrator plans and routes. The workers execute structured subtasks. Your orchestration layer uses Opus at $5/MTok for 5% of your tokens. Your workers use Flash-Lite at $0.10/MTok for the other 95%. Total cost drops while quality goes up.

This is exactly what happens when autonomous agents hit production — the architecture matters more than any single model choice.

The Real Cost of Using the Wrong LLM

Here's the vendor trap in action:

The pitch: "Our AI platform, flat fee, unlimited usage!" Sounds great.
Under the hood: A single budget-tier model running everything — customer support, document analysis, code generation, reporting.
Month 1: Simple tasks work fine. Customer support bot answers FAQs. Document summaries look decent.
Month 2: You ask it to analyze a contract for risk clauses. It misses three critical terms. You ask it to generate an integration spec. It hallucinates an API endpoint that doesn't exist.
Month 3: Trust erodes. Your team starts double-checking every AI output manually — which defeats the purpose.
The call: "You need our premium tier." That's the upsell. The flat fee was the foot in the door.

The fix isn't a more expensive model. It's the right model for each task. A system that routes contract analysis to Opus ($5/MTok) and FAQ responses to Flash-Lite ($0.10/MTok) costs less total than running everything on a mid-tier model — and produces better results at both ends.

How to Audit Your AI Vendor

Five questions to ask before signing — or renewing:

Which LLM powers each feature? If they can't name the model, that's a red flag. If they say "proprietary AI," that's usually a wrapper around someone else's model.
Can I see the model ID in logs or API responses? Transparency matters. If you're paying for GPT-5.4-level intelligence and getting Nano-level output, you should be able to verify.
What happens when a task exceeds the model's capability? Do they route to a more capable model? Or does it just... hallucinate and hope you don't notice?
Is there task routing or is everything on one model? Single-model architectures are the "flat fee" trap. Multi-model architectures with intelligent routing are what production AI actually looks like.
What's the actual per-token cost vs. the flat fee? Do the math. If their flat fee works out to $50/MTok effective cost and the underlying model costs $3/MTok, you're paying a 16x markup for a wrapper.

The Manus Problem: When You Can't See the Model

Manus — now owned by Meta — is the poster child for the black-box approach. It's an agent platform that takes your task and runs it. You pay credits. Something happens. You get a result.

What you don't get: any visibility into which model ran your task. Was it a frontier model that reasoned through your request? Or a budget model that pattern-matched and hoped for the best? You have no way to know, no way to verify, and no way to optimize.

For demos and personal experiments, that's fine. For production — where you need to explain why the AI made a specific recommendation, debug when it gets something wrong, or control costs at scale — it's a liability.

This is the extreme version of the vendor trap: you're not just locked into one model. You don't even know which model you're locked into. If your AI vendor can't tell you which model powers each feature, ask yourself what else they can't tell you.

Provider Quick Reference

Anthropic (Claude)

Model	Input/MTok	Output/MTok	Context	Best For
Opus 4.6	$5.00	$25.00	1M	Complex reasoning, agents, architecture
Sonnet 4.6	$3.00	$15.00	1M	Code, content, production workhorse
Haiku 4.5	$1.00	$5.00	200K	Fast classification, simple Q&A, chatbots

Source: Anthropic Model Documentation

OpenAI (GPT)

Model	Input/MTok	Output/MTok	Best For
GPT-5.4	$2.50	$15.00	Professional work, deep reasoning
GPT-5.4 mini	$0.75	$4.50	Code, subagents, mid-tier tasks
GPT-5.4 nano	$0.20	$1.25	High-volume simple tasks

Source: OpenAI API Pricing

Google (Gemini)

Model	Input/MTok	Output/MTok	Best For
Gemini 3.1 Pro	$2.00	$12.00	Complex tasks, long-context research
Gemini 3 Flash	$0.50	$3.00	Data extraction, structured output
Gemini 2.5 Flash-Lite	$0.10	$0.40	Budget classification, high-volume Q&A

Source: Google AI Pricing

xAI (Grok)

Model	Input/MTok	Output/MTok	Context	Best For
Grok 4.20 reasoning	$2.00	$6.00	2M	Advanced reasoning, multi-agent
Grok 4-1-fast	$0.20	$0.50	2M	Quick responses, cost efficiency

Source: xAI Model Documentation

DeepSeek

Model	Input/MTok	Output/MTok	Context	Best For
DeepSeek V3.2 chat	$0.28	$0.42	128K	Budget general use, structured output
DeepSeek V3.2 reasoner	$0.28	$0.42	128K	Budget reasoning with extended thinking

Source: DeepSeek API Pricing

Frequently Asked Questions

How do I decide which LLM to use?

Start with the task, not the model. Define what you need — reasoning, code generation, data extraction, content writing, or orchestration — then match to the appropriate model tier. Use the Task-Model Matrix above as your starting point, and always test with your actual workload before committing. The "best" model is the one that handles your specific task reliably at a cost you can sustain.
Which AI is best for coding?

For production code generation, Claude Sonnet 4.6 leads — fast, code-native, and reliable on multi-file edits at $3/MTok input. For complex architectural decisions and debugging, Claude Opus 4.6 with extended thinking. GPT-5.4 mini at $0.75/MTok is the best value if you need speed over depth. Avoid lightweight models (Nano, Flash-Lite) for code — they produce syntactically valid code with subtle logic errors that cost more to debug than you saved on tokens.
Which LLM is best for research?

It depends on the depth. For deep analysis across hundreds of pages, Claude Opus 4.6 with extended thinking and its 1M token context window. For quick fact extraction from structured documents, Gemini 2.5 Flash at $0.30/MTok handles it fine. For research needing real-time web information, GPT-5.4 with web search or Gemini with Google Search integration.
Is ChatGPT better than Claude or Gemini?

None of them is universally "better." Claude leads on coding and instruction-following. GPT-5.4 is strong on general professional work and has the broadest tool ecosystem. Gemini wins on cost efficiency and context window size. The right answer is using each where it's strongest — which is why single-model AI solutions underperform multi-model architectures. See the full comparison table above.
What is LLM task routing?

Task routing is the practice of directing different AI tasks to different models based on what each model does best. Instead of running everything on one expensive model (or one cheap model that hallucinates on complex tasks), you route reasoning to a frontier model, data extraction to a lightweight model, and code generation to a mid-tier model. Your total cost drops, quality goes up, and you stop overpaying for simple tasks or underpaying for complex ones.

This guide reflects production experience as of March 2026. LLM pricing and capabilities change frequently — I'll update this reference as models evolve. All pricing and capability claims link to official provider documentation.

I'm Tom Tokita — Co-Founder & President of Aether Global Technology Inc., a consulting firm in Manila. I route between 3-5 LLMs daily across production deployments. Have a question about which model fits your use case? Let's talk.