Curious about all the buzz around AI but don’t know where to start?
You’re in the right place. This guide breaks down the latest AI models in plain, everyday language—no tech background needed. Whether you’re exploring for fun, work, or just to stay ahead of the curve, we’ve got you covered.
All the AI model websites we mention are already organized for you here:
👉 focuspage.app/p/Paige/Latest-AI-Models-Explained
Want to save them for later? Just click “Add to my Focus Page” in the top-right corner. Focus Page is completely FREE to use!
Key AI Terms: Definitions & Examples
Token
A “token” is like a piece of a word or a symbol. When you type something, the AI breaks it down into small parts (tokens) to understand it.
Example:
The sentence “I love ice cream” might be broken into:
- “I” (1 token)
- ” love” (1 token)
- ” ice” (1 token)
- ” cream” (1 token)
→ Total: 4 tokens.
(Note: Short words are often 1 token each, but longer words or punctuation can split into more.)
Token Usage
This refers to how many tokens (word pieces) you or the AI use when asking a question or getting an answer. This can be divided into:
- Input Token: The words you send to the AI.
- Output Token: The words the AI responds with. (Longer answers = more output tokens)
Example:
- You ask: “What’s the weather today?” (5 tokens).
- The AI replies: “It’s sunny and 75°F.” (6 tokens).
→ Total token usage: 11 tokens (your input + AI’s output).
COT TOKENS
This stands for “Chain-of-Thought Tokens.” It’s like the AI showing its “work” as it thinks through a problem step by step (like math homework) before giving the final answer. This uses extra tokens because the AI is explaining its reasoning.
Example:
You ask: “If Alice has 3 apples and gives Bob 1, how many does she have left?”
AI’s COT response (extra tokens used):
- “Alice starts with 3 apples.”
- “She gives 1 to Bob.”
- “3 – 1 = 2 apples left.”
→ Final answer: “Alice has 2 apples.”
(Without COT, the AI would just say “2 apples.” COT adds steps but clarifies how it got the answer.)
Rate Limit
This is like a speed limit for how many questions or requests you can send in a certain time. If you ask too many questions too quickly, you might have to wait a bit before asking more.
Example:
Imagine you’re at a library with a rule: “You can only ask the librarian 5 questions per hour.”
If you ask too quickly (e.g., 10 questions in 10 minutes), the librarian says: “Please wait an hour.”
→ Rate limits work like this—they prevent overloading the system.
Cache Hit
A “cache” is like a shortcut memory. If the AI has seen a similar question before, it can quickly pull the answer from its memory (a “cache hit”), making things faster.
Example:
Imagine you’re at a burger place, and you overhear this:
Customer A orders: “Cheeseburger, no pickles, extra ketchup.”
- The cook makes it fresh (takes time).
Customer B (right after) orders: “Same thing as the last guy!”
- The cook already has that exact burger ready (since it’s identical to Customer A’s).
- They hand it over instantly—no need to remake it!
→ This is a CACHE HIT!
The “same order” was saved (cached) for quick reuse. Just like the AI reusing a stored answer for identical questions.
Cache Miss
If the AI hasn’t seen a question before and can’t use its shortcut memory, it has to figure out the answer from scratch (a “cache miss”), which might take slightly longer.
Example:
- You ask: “What’s the population of a tiny town called Frogville, Oklahoma?”
- The AI has never heard this before, so it researches and takes longer to reply.
→ Cache miss (no shortcut available).
Why It Matters:
- Cache hits = faster answers, like getting a burger handed to you immediately.
- Cache misses = slower, like waiting for the cook to start from scratch.
Pricing (1M Input Tokens)
This means how much it costs to process 1 million tokens of input (your questions or text). Since tokens are like word pieces, 1 million tokens could be roughly a few hundred pages of text.
What 1M tokens ≈ in real life:
- ~1,300 pages of a book (like Harry Potter and the Sorcerer’s Stone).
- ~2 hours of someone talking nonstop.
- ~500,000 words (a very long novel).
Example:
If a company charges $10 per 1M input tokens, here’s what that means:
| What You’re Processing | Token Count | Approximate Cost |
|---|---|---|
| A short email (300 words) | ~400 tokens | $0.004 (less than a penny) |
| A college essay (5 pages) | ~12,500 tokens | $0.12 |
| Entire Harry Potter Book 1 | ~1M tokens | $10 |
Why It Matters:
Just like you pay for cell phone data by the GB, AI tools often charge by tokens used.
Shorter questions/responses = cheaper. Long documents = cost more.
(Fun fact: 1M tokens = ~2 hours of spoken words!)
Latest AI Models Snapshot (Categorized by Modalities)
🔍 AI Multimodal Models
Just Text Output
|
Model Name
|
Company
|
Latest Update
|
Function
|
Input
|
Output
|
Pricing (1M input)
|
Knowledge Cutoff
|
|---|---|---|---|---|---|---|---|
|
GPT-4.5 Preview
|
OpenAI
|
Feb 27, 2025
|
Largest and most capable GPT model
|
Text, image
|
Text
|
$75
|
Sep 30, 2023
|
|
GPT-4o
|
OpenAI
|
Nov 20, 2024
|
Fast, intelligent, flexible GPT model
|
Text, image
|
Text
|
$2.5
|
Sep 30, 2023
|
|
Claude 3.7 Sonnet
|
Anthropic
|
Feb 19, 2025
|
Highest level of intelligence and capability with toggleable extended thinking
|
Text, image
|
Text
|
$3.00
|
Nov 2024
|
|
Gemini 2.5 Pro Experimental
|
Google DeepMind
|
March 2025
|
Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more
|
Audio, images, video, and text
|
Text
|
Unknown
|
Jan 2025
|
All links above are grouped under Focus Group “🔍 AI Multimodal Models – Just Text Output“.
Multiple Format Output
|
Model Name
|
Company
|
Latest Update
|
Function
|
Input
|
Output
|
Pricing (1M input)
|
Knowledge Cutoff
|
|---|---|---|---|---|---|---|---|
|
Gemini 2.0 Flash
|
Google DeepMind
|
Feb 2025
|
Next generation features, speed, thinking, realtime streaming, and multimodal generation
|
Audio, images, videos, and text
|
Text, image (experimental), and audio (coming soon)
|
$0.10 (text / image / video)
$0.70 (audio) |
Aug 2024
|
|
Qwen2.5-Omni
|
Alibaba Cloud
|
Mar 26, 2025
|
Understanding text, audio, vision, video, and performing real-time speech generation
|
Text, images, audio, and video
|
Text, audio
|
Unknown
|
Unknown
|
|
Adobe Firefly
|
Adobe
|
Mar 18, 2025
|
Generate images, edit existing photos, apply artistic styles, create social media content, flyers, and more using text descriptions
|
Text
|
Image, video
|
Limited free plan + subscriptions
|
Unknown
|
All links above are grouped under Focus Group “🔍 AI Multimodal Models – Multiple Format Output“.
📝 Text Models
|
Model Name
|
Company
|
Latest Update
|
Function
|
Input
|
Output
|
Pricing (1M input)
|
Knowledge Cutoff
|
|---|---|---|---|---|---|---|---|
|
o3-mini
|
OpenAI
|
Jan 31, 2025
|
Fast, flexible, intelligent reasoning model
|
Text
|
Text
|
$1.10
|
Sep 30, 2023
|
|
deepseek-chat (DeepSeek-V3)
|
DeepSeek
|
June 2024
|
A strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. It trained from 14.8 trillion tokens, requires only 2.788M H800 GPU hours for its full training.
|
Text
|
Text
|
$0.07
|
July 2023
|
|
deepseek-reasoner (DeepSeek-R1)
|
DeepSeek
|
April 2024
|
DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks.
|
Text
|
Text
|
$0.14
|
Oct 2023
|
|
Llama 3.3
|
Meta
|
Dec 6, 2024
|
Text only model is optimized for multilingual dialogue use cases
|
Text
|
Text
|
Unknown
|
Dec 2023
|
All links above are grouped under Focus Group “📝 Text Models“.
🖼️ Image Models
|
Model Name
|
Company
|
Latest Update
|
Function
|
Input
|
Output
|
Pricing (1M input)
|
Knowledge Cutoff
|
|---|---|---|---|---|---|---|---|
|
DALL·E 3
|
OpenAI
|
Unknown
|
OpenAI latest image generation model
|
Text
|
Image
|
$0.08 (1024x1024), $0.12 (1024x1792)
|
Unknown
|
|
Version 6.1
|
Midjourney
|
Jul 30, 2024
|
It produces more coherent images with more precise details and textures, and generates images approximately 25% faster than Version 6
|
Text
|
Image
|
Subscriptions
|
Unknown
|
|
Stable Diffusion 3.5
|
Stability AI
|
Oct 22, 2024
|
Deploy Stable Diffusion 3.5 on your own infrastructure, integrate it via our API, or start creating now with our web-based applications
|
Text
|
Image
|
Free for community, custom pricing for enterprise
|
Unknown
|
|
Imagen 3
|
Google DeepMind
|
Aug 2024
|
Google highest quality text-to-image model
|
Text
|
Image
|
$0.03 per image on the Gemini API
|
Unknown
|
All links above are grouped under Focus Group “🖼️ Image Models“.
🎥 Video Models
|
Model Name
|
Company
|
Latest Update
|
Function
|
Input
|
Output
|
Pricing (1M input)
|
Knowledge Cutoff
|
|---|---|---|---|---|---|---|---|
|
Sora
|
OpenAI
|
Dec 2024
|
Sora is able to generate complex scenes with multiple characters, specific types of motion, and accurate details of the subject and background
|
Text
|
Video
|
Subscriptions
|
Unknown
|
|
Gen-4
|
Runway
|
Mar 31, 2025
|
Runway next-generation series of AI models for media generation and world consistency
|
Text, image
|
Video
|
Limited free plan + subscriptions
|
Unknown
|
|
Pika Labs 2.2
|
Pika AI
|
Feb 27, 2025
|
This update introduces cutting-edge features designed to provide greater control, flexibility, and quality in AI-generated videos
|
Text, image
|
Video
|
Limited free plan + subscriptions
|
Unknown
|
|
Stable Video Diffusion
|
Stability AI
|
Dec 20, 2023
|
Deploy Stable Video Diffusion on your own infrastructure, integrate it via our API, or start creating now with our web-based applications.
|
Text
|
Video
|
Free for community, custom pricing for enterprise
|
Unknown
|
All links above are grouped under Focus Group “🎥 Video Models“.
🎙️ Voice/Speech Models
- ElevenLabs – Hyper-realistic text-to-speech and voice cloning.
- Whisper v3 (OpenAI) – Best-in-class speech-to-text.
- Voicebox (Meta) – Multilingual speech synthesis.
- Suno AI (Bark) – AI music/voice generation.
- Deepgram – Low-latency speech recognition.
Treading AI - All Models & Pricing
ChatGPT | OpenAI
- All models: https://platform.openai.com/docs/models
- Pricing: https://platform.openai.com/docs/pricing
ChatGPT stands for Chat Generative Pretrained Transformer. Here’s a breakdown of the name:
- Chat: Refers to the AI’s ability to engage in conversations with users.
- Generative: The model can generate text based on the input it receives.
- Pretrained: It was trained on large amounts of text data before being fine-tuned for specific tasks.
- Transformer: Refers to the deep learning architecture used to process and generate language, allowing the model to understand context and relationships in text.
In short, it’s a model designed to generate human-like text based on a deep understanding of language.
DeepSeek
- Models & Pricing: https://api-docs.deepseek.com/quick_start/pricing
Claude | Anthropic
- Models & Pricing: https://docs.anthropic.com/en/docs/about-claude/models/all-models
Gemini | Google DeepMind
Meta
- Models and libraries: https://ai.meta.com/resources/models-and-libraries/
All links above are grouped under Focus Group “All Models & Pricing“.
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