BenchLeader

Gemini 3.6 Flash

Reasoning effort

Gemini 3.6 Flash is a Google proprietary reasoning model, released 21 Jul 2026. Its best configuration (high reasoning effort) ranks #89 of 775 on the BenchLeader Index at 62.1 ±2.9, in the upper half. The ± is the point: 170 other configurations score within that range, so they and this one cannot be told apart on quality alone — price and speed are what separate them. It scores highest in human preference (68) and lowest in coding (57). At $1.50 per million tokens blended it is pricier than most ranked models. Output speed of 214 tokens per second puts it in the fastest quarter, with a first answer in 15.7 s. It has been measured at 5 reasoning-effort settings; this summary describes the best-scoring one, and the tabs above switch between them. Last measured 19 Oct 2026.

Blended price
$1.50/M
$0.750 in · $3.75 out
Output speed
214 tok/s
measured by Artificial Analysis
First answer
16 s
first token 1.15 s
Context
1.0M
Full answer
18 s
median, reasoning included
Cost per run
$1.60
one full Intelligence Index run
Released
21 Jul 2026
How it scores by categoryDashed line = average model (50). One step of 15 = one standard deviation.
  1. Overall index62
  2. Reasoning63
  3. Coding57
  4. Agents & tools59
  5. Maths60
  6. Knowledge66
  7. Human preference68
  8. Multimodal65
  9. Long context65
  10. Composite68

Versions

Google has shipped 12 models under this name. Each is ranked on its own results; a newer version often has fewer results so far, which holds its index nearer the average until more arrive.

ModelReleasedIndexRank
Gemini 3.8 Flashhigh2 Sept 202664.5#48
Gemini 3.7 Flashhigh13 Aug 202663.5#61
Gemini 3.6 Flashhighthis page21 Jul 202662.1#89
Gemini 3.5 Flashhigh19 May 202663.2#68
Gemini 3 Flashthinking17 Dec 202559.2#134
Gemini 2.5 Flash 09 2025thinking25 Sept 202551.1#348
Gemini 2.5 Flash17 Apr 202552.7#302
Gemini 2.0 Flash5 Feb 202543.3#560
Gemini 1.5 Flash 00224 Sept 202440.2#650
Gemini 1.5 Flash 00123 May 202437.2#726
Gemini 1.5 Flash14 May 2024––
Gemini Flash 2.0–––

Reasoning-effort configurations

The same model behaves differently depending on how much it is allowed to think. Each row is one setting, scored only on the benchmarks that were run at that setting. “Not stated” collects results from publishers that did not say which setting they used; for a reasoning model that is usually its thinking mode, but we do not assume it. Pick a setting here or at the top of the page to see its price, speed and category scores.

EffortIndexRankSpeedFirst answerChat reply costCategories
minimal––119 tok/s1.15 s$0.0014Maths 59 · Reasoning 46
low––119 tok/s1.15 s$0.0014Maths 59 · Reasoning 55
medium––119 tok/s1.15 s$0.0014Reasoning 57
highbest62.1#89214 tok/s16 s$0.0014Agents & tools 59 · Coding 57 · Composite 68 · Human preference 68 · Knowledge 66 · Long context 65 · Maths 60 · Multimodal 65 · Reasoning 63
not stated49.5#392119 tok/s1.15 s$0.0014Agents & tools 47 · Coding 47 · Maths 54

How its index has moved

8 Sept 2026 to 19 Oct 2026
475665
  • high
  • not stated

The index is recomputed from scratch every day, so a line moves when a new benchmark result lands, when a publisher revises a score, or when the models it is normalised against change. Early movement usually means the score is still settling.

Benchmark results

See all LLM benchmarks. One column per reasoning effort. Rank is among every configuration of every model on that benchmark. Hover a score for the run it came from.

Reasoning

BenchmarkminimallowmediumhighSourceTrend
GPQA Diamond85.9%#8386.4%#77–94.1%#10Epoch AI Benchmarking Hub
Arena Hard Prompts–––1501#32Arena (LMArena)
LiveBench 1.0 Reasoningnot in index–––85.2%#38LiveBench
GPQA Diamond (AA)not in index–––92.8%#28Artificial Analysis
Humanity's Last Exam (AA)not in index–––40.8%#81Artificial Analysis
GPQA Diamond (Vals)not in index–––93.4%#7Vals AI
ARC-AGI-134.5%#18776.5%#12083.2%#10991.2%#61ARC Prize
ARC-AGI-22.6%#18630.4%#12450.4%#10260.4%#81ARC Prize
CritPt–––10.6%#115Artificial Analysis
Chess Puzzlesnot in index29.0%#6842.0%#35–40.0%#41Epoch AI Benchmarking Hub
Mystery Game Puzzlesnot in index25.0%#4623.0%#50–30.0%#33Epoch AI Benchmarking Hub
LMCAnot in index–––44.9%#91Epoch AI Benchmarking Hub
DTBenchnot in index–––95.5%#38Epoch AI Benchmarking Hub
ForecastBenchnot in index––65.9%#8–ForecastBench

Coding

Benchmarkhighnot statedSourceTrend
SciCode53.4%#78–SciCode
WeirdML56.1%#57–WeirdML
FrontierCodeeffort not stated–34.4%#30Cognition
Arena Coding1518#40–Arena (LMArena)
Arena WebDev1538#45–Arena (LMArena)
LiveBench 1.0 Codingnot in index77.9%#35–LiveBench
SciCode (AA)not in index53.4%#76–Artificial Analysis
LiveCodeBench88.1%#8–Vals AI
IOI35.1%#38–Vals AI
SWE-bench (Vals)not in index79.6%#25–Vals AI
Code Migrationnot in index30.9%#40–Vals AI
ProgramBenchnot in index0.0%#32–Vals AI
Vibe Code Bench v1.1not in index64.0%#49–Vals AI
DeepSWE v1.1not in index46.7%#51–Epoch AI Benchmarking Hub
ALE-Benchnot in index715.5#85–Epoch AI Benchmarking Hub
Terminal-Bench 4.0 (AA)not in index7.1%#93–Artificial Analysis
Terminal-Bench 2.1 (AA)not in index77.5%#57–Artificial Analysis

Agents & tools

Benchmarkhighnot statedSourceTrend
APEX-Agentseffort not stated–46.9%#34Mercor
LiveBench Agentic Codingnot in index43.4%#57–LiveBench
Terminal-Bench 2.1 (Vals)73.8%#21–Vals AI
GDPval-AA v2.1not in index39.2%#104–Artificial Analysis
τ³-Banking (AA)29.9%#65–Artificial Analysis
CyberBenchnot in index45.4%#39–Vals AI
Finance Agent v2not in index56.3%#20–Vals AI
Legal Research Benchnot in index25.0%#55–Vals AI
Tax Agent Benchnot in index18.0%#48–Vals AI
Terminal-Bench Sciencenot in index4.3%#25–Vals AI
AutomationBenchnot in index53.0%#68–Artificial Analysis
GDP.pdf (AA)not in index17.4%#80–Artificial Analysis
FACTS Searchnot in indexeffort not stated–68.2%#18Google

Maths

Benchmarkminimallowhighnot statedSourceTrend
FrontierMath Tiers 1–3––59.0%#41–Epoch AI Benchmarking Hub
FrontierMath Tier 4––21.9%#51–Epoch AI Benchmarking Hub
OTIS Mock AIME80.0%#11382.2%#10794.2%#55–Epoch AI Benchmarking Hub
ProofBencheffort not stated–––36.0%#39Vals AI
LiveBench Mathematicsnot in index––86.4%#51–LiveBench
AIME 2026effort not statedpossible contamination–––96.7%#6MathArena
HMMT February 2026effort not statedpossible contamination–––89.4%#14MathArena
MathArena Apexeffort not statedpossible contamination–––26.0%#12MathArena
Arena Maths––1503#13–Arena (LMArena)

Knowledge

Benchmarkhighnot statedSourceTrend
SimpleQA Verified66.2%#12–Epoch AI Benchmarking Hub
LiveBench 1.0 Data Analysisnot in index63.0%#59–LiveBench
AA-Omniscience22.1#57–Artificial Analysis
MMLU-Pro89.3%#13–Vals AI
LegalBench86.7%#12–Vals AI
CorpFin63.3%#45–Vals AI
TaxEval74.9%#25–Vals AI
BioMysteryBenchnot in index58.5%#26–Vals AI
Excel Modeling Benchmarknot in index65.4%#24–Vals AI
MedCodenot in index53.1%#11–Vals AI
MedScribenot in index79.7%#60–Vals AI
Public Benefits Benchnot in index56.6%#39–Vals AI
GDP.pdfnot in index14.0%#4214.0%#42Epoch AI Benchmarking Hub
AA-Omniscience: accuracynot in index50.0%#61–Artificial Analysis
AA-Omniscience: non-hallucinationnot in index44.4%#144–Artificial Analysis

Instruction following

BenchmarkhighSourceTrend
LiveBench Languagenot in index83.9%#17LiveBench
LiveBench Instruction Followingnot in index75.4%#9LiveBench
Arena Instruction Followingnot in index1473#32Arena (LMArena)

Human preference

BenchmarkhighSourceTrend
Arena Text1483#23Arena (LMArena)
Arena Creative Writingnot in index1471#15Arena (LMArena)
Arena Multi-turnnot in index1487#24Arena (LMArena)
Arena Longer Queriesnot in index1485#36Arena (LMArena)

Multimodal

Benchmarkhighnot statedSourceTrend
Arena Vision1299#20–Arena (LMArena)
MMMU-Pro83.2%#26–Artificial Analysis
MMMU-Pro (Vals)not in index88.4%#7–Vals AI
MortgageTaxnot in index67.6%#24–Vals AI
SAGEnot in index48.7%#25–Vals AI
Blueprint-Bench 2not in indexeffort not stated–31.2%#15Epoch AI Benchmarking Hub

Long context

BenchmarkhighSourceTrend
AA-LCR80.0%#77Artificial Analysis
Arena Documentnot in index1453#28Arena (LMArena)
MLCRnot in index14.4%#51Artificial Analysis

Composite

Benchmarkhighnot statedSourceTrend
Epoch Capabilities Indexnot in indexeffort not stated–154.5#40Epoch AI Benchmarking Hub
LiveBench 1.0not in index73.6%#44–LiveBench
AA Intelligence Index v4.3.234.0#98–Artificial Analysis
Vals Multimodal Indexnot in index65.1%#13–Vals AI

Where to run it

Every provider serving this model through OpenRouter, with throughput and first-token latency measured on live traffic over the last 30 minutes and each provider’s own price. Purple marks the best in each column.

ProviderSpeedFirst tokenInput $/MOutput $/MContextQuantisation
Google AI Studio120 tok/s1.44 s$0.750$3.751.0M–
Google AI Studio115 tok/s1.15 s$0.375$1.881.0M–
Google Vertex102 tok/s1.26 s$0.750$3.751.0M–
Google Vertex101 tok/s1.61 s$1.35$6.751.0M–
Google Vertex59 tok/s7.55 s$0.375$1.881.0M–

Price history

Listed price per 1M tokens over time, as recorded by OpenRouter for the provider with the longest history.

$0.00$2.27$4.54$6.81$9.08Aug 26Sept 26Oct 26
input outputnow $0.825 in · $4.13 out

What a task costs

Estimates from list price, output speed and time to first answer for the best configuration. “With caching” assumes three-quarters of the input is served from the prompt cache at $0.075 per 1M. Reasoning tokens are not modelled.

WorkloadTokens in / outCostWith cachingTime
Chat reply400 / 300$0.0014$0.001217.1 s
Summarize a 30-page report12,000 / 600$0.011$0.005218.5 s
Code edit6,000 / 1,500$0.010$0.007122.7 s
Agentic coding session60,000 / 4,000$0.060$0.03034.4 s
Structured extraction2,000 / 200$0.0023$0.001216.6 s

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See also

Data as of 21 Oct 2026. Compare these configurations.

Cite as: BenchLeader, “Gemini 3.6 Flash: benchmarks, pricing, speed and rank”, https://www.benchleader.com/models/gemini-3-6-flash, data as of 21 Oct 2026.