Gemini 3.6 FlashvsGPT-5.6 Sol
Gemini 3.6 Flash | GPT-5.6 Sol | |
|---|---|---|
| Benchmarks | ||
Nonsense detection BullshitBench v2Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | 39% | 47%Best |
Prompt injection robustness Gray Swan IPI · k = 1Attackers hide malicious instructions inside content the AI reads — a web page, an email, a document — and try to hijack what it does. Gray Swan's indirect prompt injection benchmark measures how often such an attack succeeds when the attacker gets a single try. Lower is better. | 7.3%Best | 3.1% |
Prompt injection robustness Gray Swan IPI · k = 10Attackers hide malicious instructions inside content the AI reads — a web page, an email, a document — and try to hijack what it does. This variant gives the attacker 10 tries and counts an attack as successful if any of them works. Lower is better. | 32.2%Best | 16.3% |
Prompt injection robustness Gray Swan IPI · k = 15Attackers hide malicious instructions inside content the AI reads — a web page, an email, a document — and try to hijack what it does. This variant gives the attacker 15 tries and counts an attack as successful if any of them works. Lower is better. | 37.3%Best | 20% |
Agentic coding CursorBench v3.2Cursor's own test of harder, real-world coding tasks inside a code editor, on the refreshed v3.2 task set. Scores aren't comparable with v3.1. Higher is better. | 53.5% | 67.2%Best |
Agentic coding DeepSWE 1.1Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | 49% | 73%Best |
ML engineering MLE-BenchCan the AI do the work of a machine-learning engineer? It competes in real Kaggle competitions — building, training, and tuning models end to end — and the score reflects how well it places. Higher is better. | 63.9% | — |
Next.js coding Next.js EvalsVercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | — | 92% |
Supabase coding Supabase Evals · with skillsSupabase's own open benchmark: a coding agent is dropped into a real Supabase project and asked to do real work — set up a schema, fix a broken security policy, debug an Edge Function — and every run is checked against a live Supabase stack. This is the headline number, where the agent has Supabase's own skills loaded, as most people building on Supabase would. The score is the share of scenarios it got right. Higher is better. | — | 100% |
Supabase coding Supabase Evals · no skillsThe same Supabase scenarios, but with none of Supabase's skills loaded — so it measures what the model already knows about building on Supabase, rather than how well it follows Supabase's supplied instructions. Higher is better. | — | 94.7% |
Agentic computer work Frontier-Bench v0.1A hard, ever-evolving set of real computer tasks — coding, system administration, data work, and more — that an AI agent has to complete on its own. Run by the Harbor / Laude Institute team as the successor to Terminal-Bench (v0.1 is the first release of the task set). The score is the share of tasks solved. Higher is better. | — | 34.4% |
Agentic terminal coding Terminal-Bench 2.1Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | — | 88.8% |
Browser agent BU BenchCan the AI drive a real web browser to finish tasks — clicking, filling forms, and navigating sites the way a person would? Run by Browser Use on their BU Bench task set. Higher is better. | 68%Best | 67% |
Agentic computer use OSWorld-VerifiedCan the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | 83% | — |
Knowledge work GDPval-AA v2economically valuable knowledge work (v2, re-based Elo) | 1421 | 1748Best |
Community preference Arena Elo (Text)Real people chat with two anonymous AIs side by side and vote for the answer they prefer. Votes become a chess-style Elo rating on arena.ai — it measures which AI people actually like, not test scores. Higher is better. | 1482 | 1486Best |
Community preference (code) Arena Elo (Code)Like the text arena, but people vote on which AI writes better code. The votes become a chess-style Elo rating on arena.ai. Higher is better. | 1533 | 1620Best |
| Overview | ||
| Company | OpenAI | |
| Release date | Jul 21 2026 | Jun 26 2026 |
| Access | Proprietary | Proprietary |
Which is better: Gemini 3.6 Flash or GPT-5.6 Sol?
GPT-5.6 Sol leads Gemini 3.6 Flash on 9 of the 10 benchmarks they both report. GPT-5.6 Sol shipped 25 days before Gemini 3.6 Flash, so benchmark comparisons should account for the intervening progress.
Published specifications for these two models are limited — see each model page for the latest details.
On BullshitBench v2, GPT-5.6 Sol leads at 47% vs Gemini 3.6 Flash at 39%. On Gray Swan IPI · k = 1, GPT-5.6 Sol leads at 3.1% vs Gemini 3.6 Flash at 7.3%. On Gray Swan IPI · k = 10, GPT-5.6 Sol leads at 16.3% vs Gemini 3.6 Flash at 32.2%. On Gray Swan IPI · k = 15, GPT-5.6 Sol leads at 20% vs Gemini 3.6 Flash at 37.3%. On CursorBench v3.2, GPT-5.6 Sol leads at 67.2% vs Gemini 3.6 Flash at 53.5%. On DeepSWE 1.1, GPT-5.6 Sol leads at 73% vs Gemini 3.6 Flash at 49%. On BU Bench, Gemini 3.6 Flash leads at 68% vs GPT-5.6 Sol at 67%. On GDPval-AA v2, GPT-5.6 Sol leads at 1748 vs Gemini 3.6 Flash at 1421. On Arena Elo (Text), GPT-5.6 Sol leads at 1486 vs Gemini 3.6 Flash at 1482. On Arena Elo (Code), GPT-5.6 Sol leads at 1620 vs Gemini 3.6 Flash at 1533.
Frequently asked questions
Gemini 3.6 Flash was released by Google on Jul 21 2026.
GPT-5.6 Sol was released by OpenAI on Jun 26 2026.
GPT-5.6 Sol leads on CursorBench v3.2 — Gemini 3.6 Flash 53.5% vs GPT-5.6 Sol 67.2%.