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Every AI model on the bench

All AI models

Every frontier AI model I've put through the Goldie Bench task set — same fixed prompts, one shot each, scored 0–10 by me. Click any card for the full model review, every demo, and direct head-to-head comparisons with the rest of the field.

Fusion OpenRouter
Multi-model panel — Fable 5 + GPT-5.5, ensembled. Beats Fable 5 at half the price.
8.59avg
47tasks
21🥇
3🥈
Claude Opus 5 Anthropic
The new Anthropic flagship — benched on all 45 one-shot builds the day it landed.
8.27avg
50tasks
13🥇
7🥈
Hermes MoA Hermes · Mixture of Agents
A panel of frontier models, merged by a chair. The model doesn't matter — the system does.
8.17avg
47tasks
4🥇
9🥈
GPT-5.6 Sol OpenAI
OpenAI's flagship — the Sun of the 5.6 lineup.
8.16avg
50tasks
3🥇
8🥈
Claude Fable 5 Anthropic
The newest Anthropic model — first Mythos-class made generally available.
8.10avg
47tasks
4🥇
2🥈
Qwen 3.8 Alibaba
Alibaba's 2.4T flagship — benched through Qoder.
8.10avg
45tasks
8🥇
7🥈
Grok xAI
Snappy + real-time — the X-native model.
8.09avg
47tasks
5🥇
1🥈
MiniMax M3 MiniMax
1M-context frontier model at $0.30/M tokens — cheapest big-context model on the bench.
7.97avg
47tasks
2🥇
1🥈
Fugu Ultra Sakana AI
Sakana's multi-agent answer to Fusion — frontier ensemble without single-vendor risk.
7.94avg
42tasks
5🥇
2🥈
Kimi K3 Moonshot AI
Moonshot's 2.8T flagship — 1M context, tuned for long-horizon agent work.
7.89avg
50tasks
8🥇
4🥈
GLM-5.2 Zhipu / Z.ai
The never-forgets agent — 1M context, open weights.
7.77avg
47tasks
5🥇
0🥈
Fugu Mini Sakana AI
Fugu's fast mini variant — single model, no panel, ~3 min per build.
7.75avg
37tasks
2🥇
0🥈
Opus 4.8 Anthropic
The reasoning king — deepest thinking, premium price.
7.51avg
47tasks
3🥇
1🥈
Kimi K2.7 Moonshot AI
The heavy lifter — frontier coder at flat-rate.
7.46avg
47tasks
1🥇
2🥈
Gemini 3.6 Flash Google
Google's launch-day Flash — faster, cheaper, fewer tokens.
7.08avg
50tasks
1🥇
2🥈
Claude Sonnet 5 Anthropic
The agentic SWE frontier — 82% SWE-bench Verified, Dev Team mode.
7.01avg
47tasks
0🥇
0🥈
Qwen 3.7 Alibaba
Multilingual open-weights — strong on Chinese reasoning.
7.00avg
47tasks
0🥇
0🥈
Fugu Ultra 1.1 Sakana AI
Sakana's multi-agent orchestrator, v1.1 — routes experts per request.
6.94avg
24tasks
0🥇
1🥈
Inkling Thinking Machines
A 975B open-weights frontier model — yours to own and run.
6.07avg
50tasks
0🥇
0🥈
LongCat-2.0 Meituan
The open 1.6T MoE that builds — a frontier coder trained on non-Nvidia ASIC superpods.
8.12avg
4tasks
0🥇
0🥈
Hy3 Tencent Hunyuan
Tencent's open-weights coder — Apache-2.0, cheap, beats GLM-5.1 on frontend in Tencent's blind eval.
6.76avg
7tasks
0🥇
0🥈
DeepSeek V4 Flash DeepSeek
DeepSeek's cheap tier, retrained for agents — same size, sharper loops.
avg
50tasks
0🥇
0🥈
DeepSeek V4 Pro DeepSeek
DeepSeek's flagship tier — benched head-to-head against its own cheap Flash.
avg
50tasks
0🥇
0🥈
Kimi K2.7 · Fast Moonshot AI
Fast mode — top speed, minimal thinking.
avg
47tasks
0🥇
0🥈
Kimi K2.7 · No-Think Moonshot AI
Pure execution mode — no chain of thought.
avg
47tasks
0🥇
0🥈
Kimi K2.7 · Quality Moonshot AI
Quality mode — deepest thinking, best output.
avg
47tasks
0🥇
0🥈
Claude Mythos 5 Anthropic
Restricted-access flagship — vetted partners only.
avg
0tasks
0🥇
0🥈
Kilo Code Kilo
Fable 5-class intelligence at ~59% less. The split-the-cost play.
avg
0tasks
0🥇
0🥈

How I pick which AI models to add to the bench

The criteria are simple: it has to be a frontier model — current generation, claimed as competitive with the others on this page — and it has to be runnable on Day Zero. If a vendor ships a model and I can dispatch the same fixed prompt set through it inside the Agent Operating System the same week, the bench gets a new column.

Recent additions: GLM-5.2 (Zhipu), Kimi K2.7 (Moonshot AI), Qwen 3.7 (Alibaba), Opus 4.8 (Anthropic). Currently unranked but on the bench: Grok.

What's not on this page

I don't bench every model that ships — I'm deliberately narrow. No GPT-3.5 or other generation-behind models. No tiny coder-tuned 7Bs unless they claim frontier coding capability. No vendor sandbox models that won't let me run my fixed prompts. The goal is a useful comparison across the 6–10 models a working operator would actually consider for an agent stack — not a leaderboard with 50 cells where most of them are decorative.

If you think I should add one — propose it inside the AI Profit Boardroom.

The same stack Julian uses

Run this stack yourself.

Every demo on this bench was built inside the Agent Operating System — one prompt, one shot, single HTML file out. The Agent OS, the prompts, the templates, the weekly walkthroughs and 4,000+ founders shipping with it every day all live inside the AI Profit Boardroom.

4,000+founders
258documented wins
38countries
$59/momonthly