Explore AI

Know what each model is built to do.

Current models, practical strengths, pricing, context, and capabilities — organized around decisions instead of hype.

5 providers

A structure built to grow.

7 current models

Pricing lives in the data layer.

Live catalog

Every fact links to its source.

Providers

A

Anthropic

2 current models

C

Cursor

AI coding platform

G

Google

1 current models

O

OpenAI

3 current models

x

xAI

1 current models

Model catalog

Current models

Compare benchmark signals
A

Anthropic

Claude Opus 4.8

Featured

Anthropic's most capable model for demanding agentic work.

Context

1,000K

Input / 1M

$5

Long tasksCodingCareful reasoning
Verified 2026-07-27Official source
O

OpenAI

GPT-5.6 Sol

Featured

Frontier model for complex professional reasoning and coding.

Context

1,050K

Input / 1M

$5

Complex codingAgentsDeep reasoning
Verified 2026-07-27Official source
G

Google

Gemini 3.6 Flash

Featured

Google's intelligent, speed-focused model with search and grounding.

Context

1,000K

Input / 1M

$1.5

SpeedMultimodalGrounding
Verified 2026-07-27Official source
x

xAI

Grok 4.5

Featured

xAI's flagship model for reasoning with a large context window.

Context

500K

Input / 1M

$2

ReasoningCurrent informationLarge context
Verified 2026-07-27Official source
O

OpenAI

GPT-5.6 Terra

Balanced frontier intelligence for cost-aware production work.

Context

1,050K

Input / 1M

$2.5

Balanced valueCodingReasoning
Verified 2026-07-27Official source
O

OpenAI

GPT-5.6 Luna

Cost-sensitive model for high-volume production workloads.

Context

1,050K

Input / 1M

$1

High volumeFast iterationLow cost
Verified 2026-07-27Official source
A

Anthropic

Claude Sonnet 4.6

A strong balance of intelligence, speed, and practical cost.

Context

1,000K

Input / 1M

$3

DebuggingImplementationWriting
Verified 2026-07-27Official source
T

What is a token?

A token is a small piece of text a model reads or writes. API providers often price usage by millions of input and output tokens.

Token volume is not the same as useful work. A long answer can use more tokens while saving less time, which is why AI Stack puts developer-equivalent hours and successful outcomes first.