Guides

Stop guessing with vague prompts. Learn how Google Nano Banana 2.1 turns intent into a spec you can test, refine, and compare side‑by‑side on CoreAI.

A scanned page isn’t “just an image”—it’s structured data waiting to be extracted. This guide shows you exactly how to run inclusionAI Ling 3.1 Flash on CoreAI, compare it to Ling 3.0 Flash VL, and ge

Google Nano Banana 2.1 is fast enough to explore and disciplined enough to refine—if you prompt it the right way. Here’s how to run it on CoreAI with side-by-side comparisons, vision inputs, and an it

GPT-6.1 Sol Pro shines when you treat prompting like editing: roles, constraints, and an output you can grade. On CoreAI, you can compare it against similar models using the same prompt, files, and ru

TheDrummer's Cydonia 24B and Skyfall 36B handle the same prompt very differently. Here's how to test both, pick the right one for your task, and build prompts that actually produce reliable output.

Picking the right TheDrummer model isn't about gut feeling — it's about running the same prompt across candidates and measuring what actually holds up. Here's the evaluation workflow that replaces gue

Xiaomi's MiMo lineup now spans five models optimized for different speed-vs-fidelity tradeoffs. Here's how to pick the right one for your screenshots, PDFs, and document workflows—and how to test them

Most prompt guides give you tips. This one gives you a system: role, constraints, rubric, and a runbook you can paste into CoreAI and verify across 300+ models.

Cohere's Command models are built for one thing: answers that stick to the evidence you provide. Here's how to evaluate Command R and R7B on CoreAI—and when each one earns its place in your stack.

ByteDance's Seed lineup is lean, but picking the wrong model still costs you hours. Here's how to match Seed 2.1 Turbo, Seed-2.0-Code, and the rest to your actual work — with a test suite you can run

ByteDance's Seed lineup spans five models, and picking the wrong one wastes time and budget. This guide maps each Seed model to the tasks it actually handles best—then shows you how to prove it.

Meta's Muse Spark and Glimmer models promise structured output and document understanding—but which variant actually holds up on messy real-world inputs? Here's how to test them side-by-side without j