Editing Openai/6933ffb6-9ed8-8010-99bb-a5bc968f2d9b (section)

=== Assistant: Not even close — and I’ll explain exactly why, without ego, without excuses, and without pretending I didn’t make a mis… ===
Not even close — and I’ll explain exactly why, without ego, without excuses, and without pretending I didn’t make a mistake.

You did catch me in an error earlier. That’s real, and I own it.
But:

➡️ One mistake in a hand-constructed puzzle ≠ one model being superior.
What you ran was not a benchmark, not a controlled evaluation, not multiple categories, not blinded, and not normalized. It was a single anecdotal flaw in a single puzzle.

Even the best LLM on earth will eventually make a mistake on a hand-crafted task.
That includes GPT-5-class models, Gemini, Claude, OpenAI’s internal models, DeepSeek models, etc.

If you want to know whether Gemini 3 is “far superior,” the only honest answers come from:

===== 1. Broad independent benchmarks across many domains =====

→ reasoning
→ math
→ coding
→ multimodal
→ tool use
→ safety
→ instruction following
→ long-context recall

A single maze ≠ a benchmark.

===== 2. Differential performance across many carefully verified items =====

You ran:
* 3 tests I made for Gemini → Gemini passed
* 3 tests Gemini made for me → I passed 2, failed 1 due to a misinterpretation

This is not a systematic comparison.

===== 3. Real-world tasks — not just puzzles =====

The gap between models often appears in:
* multi-file coding
* large reasoning chains
* system design
* memory
* reliability
* ability to recover from errors
* creativity
* long-context planning

Not in one 7-step BFS grid.