🤖 AI Models Cost Guide for Software Engineers

Published data: 31 March 2026

💸 Prices shown are
per 1M tokens.
Always check the
vendor’s pricing page
for the latest rates.

Every time I open Cursor or fire up a script that calls an LLM API, I feel the silent tick of a meter running. Tokens in, tokens out — and the bill at the end of the month can surprise you if you haven’t thought carefully about which model you’re calling and when.

This post is my attempt to map out the landscape: what the major models cost today, where they genuinely shine, and a set of opinionated recipes for common software-engineering tasks so you can pick the right tool without burning your budget.

The Pricing Landscape

📌 Models are grouped
by tier: Fast (green),
Balanced (blue),
Smart (yellow),
Power (red).

Below is a live, sortable table of the most relevant models. Click any column header to sort. Use the filter pills to narrow by tier.

Model ↕ Provider ↕ Input $/1M ↑ Output $/1M ↕ Context ↕ Tier ↕ Relative cost

Interactive Cost Calculator

🧮 Tokens vary by task.
A typical diff for a
commit message is
≈ 500 input tokens.
A full file review
can be 8 000+.

Estimate your monthly API spend before you commit to a model. Adjust the sliders to match your workflow.

💰 Monthly cost estimator

Daily cost
—
Monthly cost
—
Annual cost
—

Visual cost comparison

📊 The chart shows
total cost for a
typical request:
1 000 input + 400
output tokens.

Cost per typical request (1k input + 400 output tokens)

Capability Radar

⚡ Toggle models on/off
to compare them across
five dimensions.
Scores are opinionated
but research-backed.

How do the models stack up beyond price? Toggle models to compare across five dimensions: Speed, Reasoning, Coding, Context handling, and Cost-efficiency.

🕸 Model capability radar

Use-case Recipes

🎯 Click any card to
expand the full recipe
with recommended
model, prompt tips,
and token budgets.

The real question isn’t “which model is best” but “which model is best for this specific task”. Here are the eight tasks I reach for AI on most often as a software engineer.

Cursor-specific tips

🖱️ Cursor now has
first-party models
(Composer 1/1.5/2)
trained specifically
for agentic coding.

Cursor (as of March 2026) ships its own first-party Composer model family alongside access to frontier models from Anthropic, OpenAI, and Google. Here is how to map them to tasks:

Tab completion & inline edits — always use Cursor's built-in tab model. It's near-instant and included in every plan. Zero API cost.
Agent / Composer loop — Composer 2 (Fast) is the new default and the best all-rounder for multi-file coding tasks. It was trained specifically on long-horizon agentic coding and beats Claude Opus 4.1 on SWE-bench Multilingual at a fraction of the price ($1.50/$7.50 per MTok vs $15/$75). Use Composer 2 Standard when you have a tight budget and can tolerate slightly slower throughput.
Chat / Ask — Claude Sonnet 4.6 remains excellent for reasoning-heavy questions. GPT-5.1 is a strong alternative. For quick "what does this do?" queries, Claude Haiku 4.5 is fast and cheap.
⚠️ Cursor usage pools — Composer requests and frontier model (Claude/GPT/Gemini) requests come from separate usage pools. Heavy Composer use won't eat your Claude quota. Check Settings → Cursor Account → Usage to see both pools. Pro plan includes generous allowances; Pro+ gives 3× usage on all pools.
Large codebase refactors — consider GPT-4.1 (1M context window) when you need to pass entire repositories as context. It's cheaper than GPT-5.1 and handles massive context significantly better than 128k models.

The rule of thumb

💡 “Use the cheapest
model that can reliably
do the job” is almost
always the right call.

Think of AI models like renting a car:

  • You don’t take a Ferrari to the supermarket → don’t use Claude Opus 4.6 to write a three-word commit message.
  • You don’t drive a hatchback on a track day → don’t use Haiku 4.5 to design your distributed system.
  • A mid-range saloon covers 90 % of journeys → Composer 2 / Claude Sonnet 4.6 cover 90 % of dev tasks.

Build a habit: before you invoke an LLM, ask yourself “Does this really need a power model, or will a fast one do?” Your wallet — and your monthly invoice — will thank you. 🙏