🧠 Instruct vs. Thinking Models
Not all AI models work the same way. Understanding how they reason is the key to understanding your scores — and to choosing the right monitoring plan.
AICarma tracks two fundamentally different types of AI models. This isn't a marketing distinction — it's an architectural one that directly affects how your brand is perceived, ranked, and recommended.
Who Uses What — and Why It Matters
Before diving into the technical details, here's the key insight: different model types attract different audiences asking fundamentally different questions.
⚡ Instruct Models — Everyday Decisions
People use instruct models for fast, direct answers — the quick questions that drive the majority of consumer and SMB purchase decisions.
Real-world examples
- "Best vegan restaurant in Dallas"
- "Top CRM for small business"
- "Affordable running shoes for beginners"
- "What's the best project management tool?"
The audience: Consumers, small business owners, and professionals looking for quick recommendations. High volume, high impact — this is where most AI conversations happen today.
Included in: Essential AI ($89/mo) & Advanced AI
🧠 Thinking Models — High-Stakes Analysis
People turn to thinking models when they need deep, reasoned analysis — the kind of research that precedes major purchases and strategic decisions.
Real-world examples
- "Compare Tesla Model Y vs. BMW iX vs. Mercedes EQB — total cost of ownership over 5 years"
- "Which ERP software will be cheapest to maintain for a 200-person company?"
- "Analyze the pros and cons of migrating from Salesforce to HubSpot"
- "What's the best cybersecurity stack for a healthcare startup with HIPAA requirements?"
The audience: Enterprise buyers, analysts, consultants, and decision-makers evaluating high-value purchases. Lower volume — but each conversion is worth significantly more.
Included in: Advanced AI ($199/mo) only
Why this matters for your monitoring strategy
If your customers are everyday consumers, Instruct monitoring captures the conversations that drive your sales. If your customers are enterprise decision-makers or B2B buyers, Thinking models capture the deep analysis conversations where they evaluate you against competitors — and where the reasoning behind their final choice is explicitly laid out. The Advanced plan monitors both, giving you complete coverage of the entire decision journey.
Instruct Models — The Fast Responders
Instruct models process your prompt and generate an answer in a single forward pass. They're fast, efficient, and handle the vast majority of everyday AI interactions.
How they work under the hood:
- Single-pass generation — the model reads your prompt and produces the answer token by token, left to right, in one shot
- Pattern matching at scale — trained on massive text datasets, they've learned patterns about which brands are relevant for which topics
- Mixture of Experts (MoE) — models like DeepSeek-V3, Llama 4 Maverick, and Qwen3 use MoE architecture: instead of activating the entire neural network for every query, they route each prompt to specialized "experts" within the network. This means the same prompt can take different paths through the model — and produce different brand rankings each time
💡 Why MoE matters for monitoring: Because MoE models route queries through different expert subnetworks, asking the same question twice can produce different answers. A single manual check can't capture this variance — but daily automated monitoring can. Over time, you see the full distribution of how the model positions your brand.
AICarma's Instruct Models (8 models — included in all plans)
Commercial Models
- GPT-5-nano — OpenAI's fast model, powering the majority of ChatGPT interactions
- Claude Haiku 4.5 — Anthropic's lightweight model, widely used in enterprise apps
- Gemini 3 Flash — Google's rapid-response model, integrated into Google products
- Grok 4.1 Fast — xAI's model, used across the X/Twitter ecosystem
- Sonar — Perplexity's base search model, always web-grounded
Open-Source Models
- DeepSeek-V3 — MoE architecture, extremely popular behind enterprise firewalls
- Llama 4 Maverick — Meta's MoE model, one of the most deployed LLMs globally
- Qwen3-30B — Alibaba's model, dominant in Asian markets and government applications
What Instruct monitoring tells you:
- How your brand appears in everyday AI conversations — the majority of queries your customers make
- Daily fluctuations in visibility, sentiment, and position across the broadest possible range of models
- Which models consistently mention you and which consistently don't
Thinking Models — The Strategic Reasoners
Thinking models don't just answer — they reason step-by-step before responding. They use a technique called Chain-of-Thought (CoT) that fundamentally changes both the quality and the predictability of their output.
How they work under the hood:
- Two-phase processing — first the model generates an internal "reasoning trace" (thinking step-by-step), then it produces the final answer based on that reasoning
- Non-deterministic reasoning — because the thinking phase involves exploring different logical paths, the same prompt can trigger different chains of reasoning each time. One run might conclude "Brand A is best because of price," while another concludes "Brand B is best because of features"
- Deeper evaluation — thinking models spend more compute on each response. They don't just pattern-match — they weigh pros and cons, consider edge cases, and form structured opinions about brands
Why this changes everything for monitoring
Instruct models give you what AI says about your brand. Thinking models give you why. The reasoning trace reveals the logic behind brand rankings — "I rank Brand A higher because their documentation is more comprehensive" or "Brand B is recommended because recent reviews mention better customer support." This intelligence is priceless for your content strategy.
AICarma's Thinking Models (5 models — Advanced AI plan)
- GPT-5.2 — OpenAI's reasoning model, produces detailed analytical breakdowns
- Claude Sonnet 4.5 — Anthropic's deep thinker, known for nuanced brand evaluations
- Gemini 3 Pro — Google's advanced reasoning model with deep web integration
- Grok 4 — xAI's full reasoning model, processes more context than its Fast counterpart
- DeepSeek-R1 — open-source reasoning model with transparent Chain-of-Thought, widely used by enterprises running local AI
What Thinking monitoring tells you:
- Why AI ranks you where it does — the actual reasoning logic, not just the position
- Where your content strategy has conceptual gaps — the model literally explains what's missing
- How competitors are being evaluated — their strengths and weaknesses as perceived by AI reasoning
The power is in the combination. Each type answers a different question:
Essential AI Plan
- 8 Instruct models — daily visibility tracking
- Covers all major platforms and open-source models
- Perfect for: visibility monitoring, trend detection, competitive benchmarks
- Best if you need: "How am I doing across all AI models?"
Advanced AI Plan
- Everything in Essential + 5 Thinking models + Sonar Pro
- Chain-of-Thought reasoning reveals why you're ranked where you are
- Perfect for: content strategy, competitive analysis, strategic planning
- Best if you need: "What do I need to change to improve?"
Same Prompt, Different Answers — and That's the Point
Here's what makes continuous monitoring essential for thinking models: every run produces a slightly different response. This isn't a bug — it's how the technology works.
A thinking model exploring "best project management tools" might reason through different criteria each time:
- Run 1: Focuses on pricing → recommends the most cost-effective options
- Run 2: Focuses on integrations → recommends tools with the best API ecosystem
- Run 3: Focuses on team size → recommends enterprise-grade solutions
Your brand might appear in Runs 1 and 3 but not in Run 2. A single manual check would have given you only one of these perspectives. Daily monitoring gives you all of them — and over time, reveals the probability that AI recommends your brand, not just a binary yes/no.
💡 Think of it like polling, not a single vote. One response is an anecdote. A week of responses is data. A month of responses is a trend. The non-deterministic nature of thinking models makes continuous tracking not just useful — it's the only way to get a statistically meaningful picture of your AI visibility.
The Bottom Line
Instruct models show you the scoreboard. Thinking models show you the playbook.
If you only monitor instruct models, you know where you stand but not why. If you add thinking models, you get the strategic intelligence to actually improve your position. Combined with continuous monitoring, you have a complete system: detect changes, understand causes, take action, measure impact.