AI Model Release Tracker

Gemini 3.6 Flash

Released

Gemini 3.6 Flash is an AI model released by Google on Tuesday, Jul 21 2026, 63 days after Gemini 3.5 Flash. It has a 1M token context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider.. Benchmark results (shown below) cover MLE-Bench, BullshitBench v2, Gray Swan IPI, ProgramBench, DeepSWE 1.1, BU Bench, and 4 more.

API pricing

InputWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it.
$0.75
Cached inputA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again.
$0.075
OutputWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one.
$3.75
  • Output prices include reasoning tokens.
USD per 1M tokensEvery price here is for one million tokens. A token is roughly three-quarters of a word, so a million tokens is about 750,000 words of text.Verified October 19, 2026Read from ai.google.dev. Click the check to open the source.

Available from

Google AI Studio—flex$0.375$1.8751M
Google AI Studio——$0.75$3.751M
Googleglobal—$0.75$3.751M
Googleus—$0.825$4.1251M
Google AI Studio—priority$1.35$6.751M
Googleglobalpriority$1.35$6.751M
USD per 1M tokensEvery price here is for one million tokens. A token is roughly three-quarters of a word, so a million tokens is about 750,000 words of text.Checked October 20, 2026Third-party rates from openrouter.ai, not the lab’s own list price.

Benchmarks

Coding

MLE-BenchML engineering — Can 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%
#1
ProgramBenchProgram reconstruction — The AI receives a working program and its documentation, then builds a replacement from scratch without the original source code, internet access or decompilation. The score is the percentage of 200 programs that pass every behavioral test. We record each model's best published mini-SWE-agent result, including higher reasoning efforts where available. Partial test-pass rates and almost-solved programs do not count toward this score. Equal scores share a rank here; the official board also uses partial progress to break ties. Higher is better.via ProgramBench
0.5%
#4 of 20
DeepSWE 1.1Agentic coding — Artificial 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%
#29 of 36

Agentic & tool use

BU BenchBrowser agent — Can 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%
#2 of 7
OSWorld-VerifiedAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better.
83%
#6 of 29

Reasoning & science

ARC-AGI-2Abstract reasoning — Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better.via BenchLM
60.4%
#24 of 37

Knowledge work

GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo)
1421
#16 of 21

Robustness

BullshitBench v2Nonsense detection — Given 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%
#49 of 97
Gray Swan IPIPrompt injection robustness — Attackers 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%k = 1
32.2%k = 10
37.3%k = 15
k = 1
#9 of 13
k = 10
#9 of 13
k = 15
#10 of 14

Community preference

threejsevalCommunity preference (Three.js) — Every model gets the same prompt — "the Eiffel Tower", "a glass fishbowl", "a robot arm picking toys into a box" — and builds a 3D scene in Three.js. Real people then see two scenes side by side, names hidden, and vote for the one they prefer. The votes become a chess-style Elo rating on threejseval.com, averaged across all the prompts. It measures whether the scene looks and moves right to a human eye, not whether the code passes a test. Higher is better.
1349
#21 of 27

Source: BenchLM, retrieved 28 September 2026 · Source: ProgramBench, retrieved 11 September 2026. Other sources are identified on the linked benchmark pages.

Compare Gemini 3.6 Flash with

Gemini 3.6 Flash

Suggested comparisons

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About

Gemini 3.6 Flash was announced on July 21, 2026 alongside Gemini 3.5 Flash-Lite and Gemini 3.5 Flash Cyber. Google emphasized more efficient reasoning for coding agents and professional tasks, including software engineering and machine-learning work.

The release built on the Flash line's focus on response speed and serving cost while improving Google's reported agent evaluations. It supplied the general-purpose model in that announcement; Flash-Lite targeted lighter workloads, while Flash Cyber was introduced for defensive security work.

Frequently asked questions

Gemini 3.6 Flash was released by Google on Tuesday, Jul 21 2026.

All Google releases

32 tracked