Best AI chatbot

Why Fable 5 leads, and why that is arguable

One number and one condition. The number: Fable 5 sits first of 389 models on the Arena text leaderboard at 1506, and takes the whole forty-five-weight column. The condition: the same model loses the score-per-dollar column outright. Its output costs $50 per million tokens, the most expensive row in the table, which puts its score-per-dollar at 30.1, the floor.

Opus 5 is twelve points lower and half the price. On a leaderboard whose entire top tier fits inside about fifty points, twelve is not much. So the real answer splits by question: if you are building chat on an API and paying a monthly bill, Opus 5; if you are buying an app subscription, the price column does not apply to you and the top of the table is what you want.

One thing against our own leader, which no column here measures: Fable 5 is the only model Anthropic own table marks as slower, and its adaptive thinking is always on, so there is no lever to turn it down. In conversation, latency is felt. Our table does not see that and you will inside ten minutes of using it.

This table ranks models, not apps

That is a deliberate choice with a cost, so let us be direct about it. All five columns here are numbers published about a model: a leaderboard score, a price per million tokens, a context window. No vendor publishes any of the three for its chat app. Rank apps instead and three of the five columns go empty for most rows, which stops being a table.

The second reason matters more: one model runs under several apps, and an app swaps the model underneath it without telling you. Opus 5 sits under the Claude app, the API and Claude Code at the same time. Make "Claude" a row and we count the same number again in the coding table. It is the same reason the video category ranks versions rather than tools.

We made the opposite call for studying and it was right there, because Google publishes the notebook own limits as figures: fifty sources, five hundred thousand words. For a chat app no vendor publishes anything comparable. The cost is that Claude, Gemini and ChatGPT as apps have no row here, and their own pages are where those questions get answered.

Why ChatGPT has no row, and what we do have from OpenAI

The short answer is that ChatGPT is a product and this table ranks models, so it was never going to be a row. The longer answer counts against us and matters more: even if we wanted to, we could not describe it. Every path on openai.com returns 403 to this server, re-tested on 12 August 2026, while that domain own robots.txt does not forbid crawling.

One thing is open, though, and until today we had not used it: developers.openai.com answers in full. GPT-5.6 Sol is listed there at $5 input and $30 output, a 1.05M token context window, 128K max output and a knowledge cutoff of 16 February 2026. Its Arena rows exist too. So today it is the third row of this very table.

The summary is one sentence: the model can be ranked, the app cannot be described. What we still cannot tell you is which model runs under ChatGPT today, what its plans cost, and whether it opens from Iran. All three live on the domain that will not answer. The OpenAI page writes that boundary out in full.

What reading the leaderboard properly taught us

Two rows we had recorded as unverified had been on the board the whole time. GLM-5.2 Max is 33rd at 1470 and DeepSeek V4 Flash is 83rd at 1435. An earlier run read the top of the page; this one read the JSON the page itself ships. So both candidates that could not be ranked in this category now can be, and the coding table moved as well. That gap was ours, not the vendors.

The second concerns what a row is named. This reference already argued that an Arena row names a serving configuration rather than a model. For OpenAI it goes one step further: the WebDev row reads, literally, gpt-5.6-sol-xhigh (codex-harness). That score belongs to the model inside OpenAI own coding agent, not to the bare model. Price and context attach to the base model; the score attaches to whatever was actually tested.

The third was a trap we did not fall into. The same board carries a row called deepseek-v4-pro at 1458, sitting above Flash. That is a different model in the same family, not a setting, so we did not borrow its number. Had we done so, DeepSeek would have climbed two places and nobody would have noticed.

Where our own method exaggerates

All nine rows fit inside 71 Elo points, from 1506 down to 1435. Min-max normalisation stretches those 71 points into a spread from 78.6 to 29.1. So the score column makes the distance look larger than it is, and the last row is not weak: it is last in a table of high-end models.

A sharper example, because it surprised us too. GPT-5.6 Sol wins the context column with 1,050,000 tokens and Kimi K3 Max holds 1,048,576. The gap is 1,424 tokens, about a tenth of a percent, and in practice it changes nothing. Both take essentially the full column, which is correct. But add a model with a two-million window tomorrow and both lose that column together, without anything about either having changed.