I tested 30 AI tools so you don't have to: the price-per-task numbers

Decoding AI — Tue, 13 Oct 2026 09:25:29 +0000

Everyone has an "AI tools" list. Nobody shows their work.

So we did something unfashionable: we took 30 of the most-used AI tools — ChatGPT, Claude,
Gemini, Grok, Midjourney, Runway, ElevenLabs, Cursor, Notion AI, and 21 more — and scored each
one on the same 5 dimensions: output quality (published benchmarks like LMArena, Artificial
Analysis, and SWE-bench — not vibes), measured speed in tokens/sec where published, true price
per real task, ease of use, and trust. The trust and ease scores are labeled as judgment. The
prices are arithmetic.

Here is what the arithmetic says. These are the numbers that changed how we build.

The price-per-task leaderboard (the part nobody publishes)

Forget per-month pricing. Nobody buys "a month of AI." You buy summaries, images, minutes of
narration, decks, automation runs. So we did the division:

Voice narration: ElevenLabs costs $0.18 per minute on the Creator plan. If your app narrates 1,000 minutes a month, that is $180 — budget it like infrastructure, because it is.
One image: Midjourney $0.05 · DALL-E 3 $0.04 · Stable Diffusion $0.002. Read that again. Self-hosted Stable Diffusion is 25x cheaper per image than Midjourney. At 10 images a day you will never notice. At 10,000 images a day inside a pipeline, that ratio is an architecture decision: API convenience vs. self-hosted GPUs.
One slide deck: Gamma $0.90. One Zapier automation run: $0.027. A Zapier run costs less than the electricity to think about it — which is exactly why automation bills surprise people. They never surprise anyone who did the multiplication first.
Full range across all 30 tools: $0 (NotebookLM, Microsoft Copilot free tier) to $6.25 (a Lovable app build). Three orders of magnitude between the cheapest and priciest "one task." "AI tools cost about the same" is the most expensive sentence in tech.

Speed is a feature, and it has a number

Gemini's flagship runs at 277 tokens/sec — roughly 5x faster than GPT-6-class models.
Speed is not a vibe; it is the difference between a chatbot that feels instant and one where
users start a second tab. If latency is your product (agents, coding assistants, voice), the
tokens/sec column matters more than the benchmark column.

And on benchmarks: Claude Opus 5.5 tops the published reasoning benchmarks we tracked
(Oct 2026). When the task is reasoning-heavy — code review, agentic planning, multi-step
analysis — that is the model family to beat.

How to use these numbers on Monday morning

Three architecture decisions these numbers settle:

Image pipelines: API vs self-hosted. Midjourney at $0.05/image vs self-hosted Stable Diffusion at $0.002/image is a 25x ratio. Multiply your monthly image volume by each number, add your GPU cost to the second column, and the answer stares back at you. Below a few thousand images a month, API convenience wins. Above it, you are donating money.
Voice features: budget like infrastructure. ElevenLabs at $0.18/minute means 1,000 narrated minutes a month is $180. That is a line item, not a rounding error — price it into the feature before you ship it, not after the invoice arrives.
Automation: do the multiplication first. A Zapier run at $0.027 sounds like nothing. Ten thousand runs a month is $270. Every automation bill that ever surprised anyone was just this multiplication, performed too late.

Steal this: measure tokens/sec yourself in 20 lines

Don't take any speed claim on faith — including ours. Time the stream:

import time

def measure_tps(stream):
    """stream: an iterable of text chunks from any streaming LLM API."""
    first_token_at = None
    chars = 0
    start = time.perf_counter()
    for chunk in stream:
        if first_token_at is None:
            first_token_at = time.perf_counter()
        chars += len(chunk)
    elapsed = time.perf_counter() - start
    # rough: ~4 chars per token for English
    return (chars / 4) / elapsed, first_token_at - start

tps, ttft = measure_tps(your_stream)
print(f"{tps:.0f} tok/s, time-to-first-token {ttft:.2f}s")

Two numbers, one script, zero trust required. Run it against every model you ship with.

The honesty contract

Every number in the full scorecard is sourced and dated. Where no public measurement exists,
we wrote N/A instead of inventing one. A benchmark table with no N/As is a fiction table —
if someone shows you 30 tools with 30 confident numbers and zero gaps, they made some of them up.

The free 14-page scorecard has the full ranking (cheapest to priciest per task), a Top 10
overall ranked on evidence, and one-line verdicts plus "best for / skip if" for every tool.
v2.0 runs all 5 test tasks hands-on, per product, in our lab — subscribers get it first.