AI Jupyter Test Map
Start with the test record, then choose the model.
A start page for AI Jupyter local LLM test records, machine-fit pages, hosted model checks, and official API pricing.
What changed in this map
Put local LLM test records and machine limits before hosted model pages and pricing.
Linked every row back to the page where the test record, source snapshot, or price row can be checked.
Separated real hardware tests from hosted-model checks, API cost checks, and local model fit.
Start with what you need to choose
How to read this map
Open the page that matches the choice in front of you. A model page can give you candidates to try, but your computer, bill, files, prompts, and latency target decide what survives.
Test a hosted model
Start from a task model page, then try the top candidates on your own prompt, latency target, and failure cases.
Open model checksEstimate API cost
Compare official token rows with workload assumptions such as long prompts, output size, retries, cache hits, and batch jobs.
Open pricingFind what your computer can run
Start with the homepage-style local LLM finder: RAM, VRAM, machine type, workload, and realistic install paths.
Open local LLM finderChoose a local AI tool
Use this when the question is whether to install Ollama, LM Studio, Jan, or Open WebUI first.
Open tool pickerCheck a target model
Start from a named model such as Qwen3 32B when the question is what RAM, VRAM, or GPU is required.
Open model requirementsMatch a GPU to a model
Start from RTX 4060 Ti 16GB, RTX 3060 12GB, 32GB RAM, or MacBook scenarios when the question is whether Qwen3 or DeepSeek fits.
Open GPU scenariosRead real LLM tests
Start from measured local LLM tests when hardware, speed, memory pressure, or model fit matters more than model news.
Open LLM TestsWhich page should I open first?
Start with the page that matches the thing you are choosing.
A model can look good for one task and still be wrong for your app, budget, or computer. Use this table to open the page that matches the choice in front of you.
Task model pages
Find hosted models to test for a task
Check: Snapshot date, your own prompt, latency, bad answers, and price.
API pricing pages
Choose a model your product can afford
Check: Cost per successful workflow, retries, output length, cache hit rate, and batch eligibility.
Which local LLM tool
Choose what can run locally
Check: RAM, VRAM, quantization, runtime support, context length, and whether the computer stays usable.
Runtime and tool picker
Choose which local AI tool to install
Check: Command-line repeatability, desktop UI, open-source assistant workflow, browser UI, and whether the runtime already works.
Model requirement pages
Check hardware for one target model
Check: RAM floor, VRAM target, Q4 size, install hint, and whether a smaller model should be tested first.
Device and model scenarios
Match a GPU or laptop to a model family
Check: Which model to install first, what becomes a stretch test, and when an upgrade is more honest.
LLM Tests
Check what actually ran on real hardware
Check: Hardware setup, screenshots, memory pressure, measured speed, and slow boundaries.
Where to start
Start with the test record, then choose the model.
Hosted model to compare first
Start here
Claude Fable 5
Anthropic
Highest adjusted score among the current task pages, worth comparing against your own prompts.
OpenCoding model to test
Task pick
Claude Fable 5
Anthropic
Current strong pick for repository fixes, agentic coding, code review, and production engineering tasks.
OpenWriting model to test
Task pick
Claude Fable 5
Anthropic
Current strong pick for editing, rewriting, brand voice, and long-form content workflows.
OpenMath model to test
Task pick
Claude Fable 5
Anthropic
Current strong pick for quantitative reasoning, proofs, tutoring, and solution-checking tasks.
OpenImage model to test
Task pick
GPT Image 2
OpenAI
Current strong pick for text-to-image quality, prompt following, and creative production workflows.
OpenLocal model path
Local fit
Qwen3 14B
Qwen
Open the machine picker, then check real 8GB RAM and 8GB VRAM test records before installing.
OpenCheap API row to check
Cost watch
Ministral 3 - 3B
Mistral
Lowest normalized paid text API row with official token prices captured.
OpenAgent model to test
Agent watch
Claude Fable 5
Anthropic
Current proxy for agentic coding and tool-heavy workflows.
OpenRAG API row to check
Cost watch
Gemini 3.1 Flash-Lite
Low-cost, long-context-friendly text API candidate for retrieval-heavy apps.
Open8GB RAM CPU-only record
Test record
8GiB Docker CPU-only
Ollama
Shows what still feels usable when Ollama is capped to 8GiB RAM with no GPU.
Open8GB VRAM GPU record
Test record
8GB VRAM fit check
Ollama + NVIDIA
Shows tokens per second and peak GPU memory change under an 8GB VRAM budget.
OpenWhat to verify before choosing
Real workload shape
Prompt length, output length, retries, cache behavior, and batch usage can change the cheapest or best-looking option.
Source freshness
Check the score snapshot date, official price row, and whether the model is still available where you need it.
Where it will run
Region, latency, rate limits, privacy rules, and local hardware can matter more than a small score difference.
Test before default
Use the map to narrow choices, then test with your own prompts, files, users, and failure cases before making a model your default.
How the map stays useful
No single winner
A fast local model, a cheap API row, and a strong writing model solve different problems. The map keeps those paths apart.
Open the page behind the pick
Every row should lead to something checkable: a test record, screenshot, source date, official price row, or hardware guide.
Recheck before spending
Use the map to choose what to try next. Before buying time, changing code, or publishing a comparison, open the linked page again.
Limits stay visible
Slow runs, missing rows, stale prices, weak hardware, and unclear availability should stay visible beside the model pick.
How to use this map
- 1Start with the row that matches what you need: real test data, local hardware, hosted model check, or price check.
- 2Open the linked page and check the test record, screenshots, measured speed, snapshot date, official price row, or hardware guide.
- 3Compare a few picks against your real workload: prompts, files, latency target, region, privacy, and budget.
- 4Recheck the relevant page before purchase, launch, or deployment because provider data changes quickly.
AI Jupyter Test Map FAQ
What is the AI Jupyter Test Map?
It is the start page for AI Jupyter local LLM test records, local model guides, hosted model pages, and API pricing pages.
Does this page choose one winner for everyone?
No. The map points different questions to different pages because the best coding model, cheapest API, local model, and hardware test result are not the same thing.
How often should I recheck the index?
Recheck it before any purchase, deployment, or public comparison. Model scores, official pricing, and API availability can change quickly.
Why does the index link to pricing and local model pages?
A high-ranked hosted model may be too expensive for a workload, while a local model may be limited by RAM, VRAM, speed, or context length. The index keeps those tradeoffs visible.
How should teams use the index for a real project?
Use it to pick a few models to try, then run private tests with your prompts, files, latency targets, safety requirements, and budget before making one the default.