Tendril — AI Lessons for Real Life

Tendril

Start With the Job, Not the Leaderboard

The OpenAI model lineup changes quickly, but the decision pattern is stable: pick the smallest model that can reliably do the job, then upgrade only when evals show it is worth the latency and cost.

Need	Start here	Why
Complex coding, planning, professional reasoning	gpt-5.5	Flagship quality and strong default for hard work
Cost-sensitive coding or subagents	gpt-5.4-mini	Strong small model with lower latency and cost
Classification, extraction, ranking, simple routing	gpt-5.4-nano	Cheapest GPT-5.4-class option for high volume
Agentic coding inside Codex-like harnesses	gpt-5.3-codex	Optimized for coding-agent loops
Very hard slow analysis	pro variant or background mode	More compute, but design for waiting

Need

Start here

Why

Complex coding, planning, professional reasoning

gpt-5.5

Flagship quality and strong default for hard work

Cost-sensitive coding or subagents

gpt-5.4-mini

Strong small model with lower latency and cost

Classification, extraction, ranking, simple routing

gpt-5.4-nano

Cheapest GPT-5.4-class option for high volume

Agentic coding inside Codex-like harnesses

gpt-5.3-codex

Optimized for coding-agent loops

Very hard slow analysis

pro variant or background mode

More compute, but design for waiting

Build a tiny eval set from real user tasks.

Run the frontier model to establish a quality ceiling.

Run mini and nano to find the cost floor.

Measure latency, refusals, schema validity, and human acceptance.

Lock the chosen model ID in config, not scattered across code.

const MODEL_BY_TASK = { hardCoding: "gpt-5.5", routineSubagent: "gpt-5.4-mini", extraction: "gpt-5.4-nano", codexHarness: "gpt-5.3-codex", } as const;Centralize model choices so migrations are one config change instead of a repo-wide hunt.

The big idea: model selection is an economics problem wrapped in a quality problem. Measure both.

End-of-lesson check

8 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-openai-model-picker-creators

What is the main idea of "OpenAI Model Picker: GPT-5.5, GPT-5.4, Mini, Nano, and Codex"?

A practical picker for current OpenAI models: when to pay for the frontier model, when to use a.
Use AI as the final authority for the whole decision
Avoid checking the answer once it sounds polished
Focus only on speed instead of judgment

Which concept is most central to "OpenAI Model Picker: GPT-5.5, GPT-5.4, Mini, Nano, and Codex"?

GPT-5.4
GPT-5.5
GPT-5.4 mini
GPT-5.4 nano

Which use of AI fits this topic best?

Let the AI decide what matters without your review
Use the answer before checking whether it fits the situation
Build a tiny eval set from real user tasks.
Treat the AI output as automatically correct

What should a careful learner remember about "Docs checkpoint"?

Use AI to draft or organize ideas about GPT-5.5, then verify before acting.
Skip the context so the tool can guess faster
Treat the output as private even after sharing it online
Use the answer without checking the source

You want to use AI after this lesson. What is the safest next step?

Act immediately because the AI answer is written clearly
Use AI for drafting and comparison, but verify before publishing or relying on it.
Hide uncertainty so the final answer looks cleaner
Use private or sensitive details before checking permission

How should AI output about GPT-5.5 be treated?

As proof that no other source is needed
As a replacement for context, consent, or expert review
As a draft or helper output that still needs human judgment and verification
As something that becomes correct when it sounds confident

Name one way to verify an AI answer about GPT-5.5.

Which action would help you apply "OpenAI Model Picker: GPT-5.5, GPT-5.4, Mini, Nano, and Codex" responsibly?

Use the tool to avoid thinking through the tradeoff
Keep going even if the output conflicts with a trusted source
Treat the AI output as automatically correct
Run the frontier model to establish a quality ceiling.

Start With the Job, Not the Leaderboard

The OpenAI model lineup changes quickly, but the decision pattern is stable: pick the smallest model that can reliably do the job, then upgrade only when evals show it is worth the latency and cost.

Need	Start here	Why
Complex coding, planning, professional reasoning	gpt-5.5	Flagship quality and strong default for hard work
Cost-sensitive coding or subagents	gpt-5.4-mini	Strong small model with lower latency and cost
Classification, extraction, ranking, simple routing	gpt-5.4-nano	Cheapest GPT-5.4-class option for high volume
Agentic coding inside Codex-like harnesses	gpt-5.3-codex	Optimized for coding-agent loops
Very hard slow analysis	pro variant or background mode	More compute, but design for waiting

Need

Start here

Why

Complex coding, planning, professional reasoning

gpt-5.5

Flagship quality and strong default for hard work

Cost-sensitive coding or subagents

gpt-5.4-mini

Strong small model with lower latency and cost

Classification, extraction, ranking, simple routing

gpt-5.4-nano

Cheapest GPT-5.4-class option for high volume

Agentic coding inside Codex-like harnesses

gpt-5.3-codex

Optimized for coding-agent loops

Very hard slow analysis

pro variant or background mode

More compute, but design for waiting

Build a tiny eval set from real user tasks.

Run the frontier model to establish a quality ceiling.

Run mini and nano to find the cost floor.

Measure latency, refusals, schema validity, and human acceptance.

Lock the chosen model ID in config, not scattered across code.

The big idea: model selection is an economics problem wrapped in a quality problem. Measure both.

End-of-lesson check

8 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-openai-model-picker-creators

What is the main idea of "OpenAI Model Picker: GPT-5.5, GPT-5.4, Mini, Nano, and Codex"?

A practical picker for current OpenAI models: when to pay for the frontier model, when to use a.
Use AI as the final authority for the whole decision
Avoid checking the answer once it sounds polished
Focus only on speed instead of judgment

Which concept is most central to "OpenAI Model Picker: GPT-5.5, GPT-5.4, Mini, Nano, and Codex"?

GPT-5.4
GPT-5.5
GPT-5.4 mini
GPT-5.4 nano

Which use of AI fits this topic best?

Let the AI decide what matters without your review
Use the answer before checking whether it fits the situation
Build a tiny eval set from real user tasks.
Treat the AI output as automatically correct

What should a careful learner remember about "Docs checkpoint"?

Use AI to draft or organize ideas about GPT-5.5, then verify before acting.
Skip the context so the tool can guess faster
Treat the output as private even after sharing it online
Use the answer without checking the source

You want to use AI after this lesson. What is the safest next step?

Act immediately because the AI answer is written clearly
Use AI for drafting and comparison, but verify before publishing or relying on it.
Hide uncertainty so the final answer looks cleaner
Use private or sensitive details before checking permission

How should AI output about GPT-5.5 be treated?

As proof that no other source is needed
As a replacement for context, consent, or expert review
As a draft or helper output that still needs human judgment and verification
As something that becomes correct when it sounds confident

Name one way to verify an AI answer about GPT-5.5.

Which action would help you apply "OpenAI Model Picker: GPT-5.5, GPT-5.4, Mini, Nano, and Codex" responsibly?

Use the tool to avoid thinking through the tradeoff
Keep going even if the output conflicts with a trusted source
Treat the AI output as automatically correct
Run the frontier model to establish a quality ceiling.