Llama 4 vs GPT for Coding: Open Weights Against OpenAI

Meta and OpenAI have been circling each other since the first Llama leak, but this is the cleanest form of the fight: Llama 4 Maverick, an open-weight mixture-of-experts model you can download and serve yourself, against GPT-5.5, a closed frontier model that exists only behind OpenAI's API. Same coding prompts, one attempt each, real people voting blind. You can watch it happen live: Llama 4 Maverick vs GPT-5.5.

The matchup carries more weight than usual because of what happened after Llama 4. Meta's newer Muse family shipped closed, which quietly makes Maverick the company's last big open-weight coder. If your stack was built on the assumption that Meta will always publish the weights, this head-to-head is where that bet gets audited.

Open MoE against the closed frontier

Llama 4 Maverick is a mixture-of-experts design: roughly 400B total parameters with 17B active per token, which is what lets it serve at FP8 on a single H100-class host instead of a small cluster. The weights are downloadable under Meta's community license, so you can run it on your own GPUs or shop across hosted providers that race the price toward zero.

GPT-5.5 is the opposite proposition. No weights, one vendor, one price sheet: $1.25 in / $10 out per million tokens. What you get for accepting the lock-in is OpenAI's serving stack and whatever the model itself is worth — and that last part is exactly what the arena decides, not the marketing pages.

• Llama 4 Maverick — open weights, MoE efficiency, self-host or pick any provider.
• GPT-5.5 — closed weights, single vendor, fixed $1.25/$10 pricing.
• One of these can outlive its maker's product decisions; the other is an API subscription.

Spec sheet

The stable facts first. Performance is deliberately missing from this table, because that column is being filled in live by voters.

What the arena actually measures

Every matchup here is community-voted, one-shot, and live: both models get the same coding prompt, produce a single attempt, and voters pick the better result without knowing which side wrote what. No retries, no agent scaffolding, no vendor-run harness. That is also why this article quotes no win rates — the honest numbers live on the leaderboard, and the fastest way to calibrate your own judgment is to open the arena and vote a few rounds blind.

The last open Llama

Here is the part that outlasts any single vote. With Muse closed, Maverick is the final big coder Meta shipped with downloadable weights. Whatever it scores in the arena, those weights are already on disks around the world, and no product pivot can recall them. If Maverick holds its own against a closed model at GPT-5.5's price point, self-hosters keep a frontier-adjacent coder they own outright. If it loses badly, the open-weights argument loses its best remaining Meta exhibit.

A closed model can be deprecated by a business decision. An open one has to be beaten.

How to call it

Not from this page. Watch the live votes, then weigh them against your own constraints. If your code cannot leave your network, GPT-5.5 was never an option and the only question is how good Maverick really is. If you just want the strongest one-shot output per dollar, the $1.25/$10 sheet versus commodity open-weight hosting is a math problem the votes will price for you. For the wider field beyond these two, the best AI for coding guide maps the rest — but this particular fight is still being decided one vote at a time.