BenchLeader
GoogleReasoning model

Gemini 3.7 Flash

Reasoning effort

Gemini 3.7 Flash is a Google proprietary reasoning model, released 13 Aug 2026. Its best configuration (medium reasoning effort) ranks #64 of 756 on the BenchLeader Index at 63.4 ±3.4, in the upper half. The ± is the point: 133 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 composite (75) and lowest in reasoning (60). At $1.50 per million tokens blended it is pricier than most ranked models. Output speed of 283 tokens per second puts it in the fastest quarter, with a first answer in 5.6 s. It has been measured at 4 reasoning-effort settings; this summary describes the best-scoring one, and the tabs above switch between them. Last measured 13 Aug 2026.

Blended price
$1.50/M
$0.750 in · $3.75 out
Output speed
283 tok/s
measured by Artificial Analysis
First answer
5.62 s
first token 2.50 s
Context
1.0M
Full answer
7.39 s
median, reasoning included
Cost per run
–
Released
13 Aug 2026
How it scores by categoryDashed line = average model (50). One step of 15 = one standard deviation.
  1. Overall index63
  2. Reasoning60
  3. Coding68
  4. Agents & tools64
  5. Knowledge73
  6. Multimodal68
  7. Long context67
  8. Composite75

Versions

Google has shipped 13 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 Flashmedium2 Sept 202664.7#48
Gemini 3.7 Flashmediumthis page13 Aug 202663.4#64
Gemini 3.6 Flashhigh21 Jul 202660.5#105
Gemini 3.5 Flashmedium19 May 202663.1#69
Gemini 3 Flashthinking17 Dec 202559.3#125
Gemini 2.5 Flash 09 202525 Sept 202551.9#318
Gemini 2.5 Flash17 Apr 202552.8#289
Gemini 2.0 Flashthinking5 Feb 202545.0#499
Gemini 1.5 Flash 00224 Sept 202440.7#628
Gemini 1.5 Flash 00123 May 202436.4#721
Gemini 1.5 Flash 051414 May 2024––
Gemini 1.5 Flash–––
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
low60.6#104290 tok/s1.18 s$0.0014Agents & tools 58 · Coding 60 · Composite 72 · Knowledge 72 · Long context 65 · Multimodal 68 · Reasoning 55
mediumbest63.4#64283 tok/s5.62 s$0.0014Agents & tools 64 · Coding 68 · Composite 75 · Knowledge 73 · Long context 67 · Multimodal 68 · Reasoning 60
high63.1#67303 tok/s12 s$0.0014Agents & tools 47 · Coding 63 · Composite 68 · Human preference 69 · Knowledge 68 · Long context 66 · Maths 64 · Multimodal 68 · Reasoning 68
not stated––81 tok/s2.50 s$0.0014Agents & tools 57 · Coding 58 · Maths 59

How its index has moved

8 Sept 2026 to 5 Oct 2026
576368
  • medium
  • high
  • low

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

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

Benchmarklowmediumhighnot statedSourceTrend
GPQA Diamond––94.8%#5–Epoch AI Benchmarking Hub
LMArena Hard Prompts––1508#19–LMArena
LiveBench Reasoningnot in index––87.8%#24–LiveBench
GPQA Diamond (AA)not in index90.1%#6392.1%#3994.5%#6–Artificial Analysis
Humanity's Last Exam (AA)not in index35.1%#10739.0%#8747.9%#33–Artificial Analysis
GPQA Diamond (Vals)not in index––93.9%#5–Vals AI
ARC-AGI-185.2%#9491.2%#5895.5%#29–ARC Prize
ARC-AGI-252.9%#8963.8%#6884.6%#29–ARC Prize
CritPt5.7%#1299.4%#10814.3%#87–Artificial Analysis
Chess Puzzlesnot in index––47.0%#14–Epoch AI Benchmarking Hub
Mystery Game Puzzlesnot in index––37.0%#15–Epoch AI Benchmarking Hub
LMCAnot in index47.1%#6549.2%#5750.4%#48–Epoch AI Benchmarking Hub
DTBenchnot in index93.3%#5094.7%#4096.8%#16–Epoch AI Benchmarking Hub

Coding

Benchmarklowmediumhighnot statedSourceTrend
SciCode53.6%#6659.8%#1056.8%#29–SciCode
FrontierCode–––43.6%#15Cognition
LMArena Coding––1518#41–LMArena
LMArena WebDev––1592#26–LMArena
LiveBench Codingnot in index––78.9%#28–LiveBench
SciCode (AA)not in index55.7%#4259.8%#1057.2%#28–Artificial Analysis
LiveCodeBench––88.7%#5–Vals AI
IOI––67.8%#13–Vals AI
SWE-bench (Vals)not in index––80.8%#23–Vals AI
Code Migrationnot in index––34.8%#32–Vals AI
ProgramBenchnot in index––0.0%#23–Vals AI
Vibe Code Bench v1.1not in index––70.4%#32–Vals AI
DeepSWEnot in index53.8%#4265.5%#2565.3%#27–Epoch AI Benchmarking Hub
ALE-Benchnot in index––904.3#58–Epoch AI Benchmarking Hub
FrontierSWEnot in index––20.3%#13–Epoch AI Benchmarking Hub
Terminal-Bench 4.0 (AA)not in index––13.6%#64–Artificial Analysis
Terminal-Bench 2.1 (AA)not in index79.8%#4678.3%#5285.8%#24–Artificial Analysis

Agents & tools

Benchmarklowmediumhighnot statedSourceTrend
Terminal-Bench––11.2%#73–Terminal-Bench
Remote Labor Index–––5.0%#7Scale AI / CAIS
APEX-Agents–––67.8%#5Mercor
LMArena Agent––-1.5#35–LMArena
LiveBench Agentic Codingnot in index––58.3%#18–LiveBench
Terminal-Bench 2.1 (Vals)––77.5%#14–Vals AI
GDPval (AA)not in index40.3%#8742.0%#7943.6%#67–Artificial Analysis
τ²-Bench Banking (AA)29.5%#6735.5%#4532.8%#56–Artificial Analysis
Analyst Agent (AA)not in index––60.0%#1–Artificial Analysis
CyberBenchnot in index––43.8%#36–Vals AI
Finance Agent v2not in index––59.0%#5–Vals AI
Harvey's Legal Agent Benchmarknot in index––8.8%#18–Vals AI
Legal Research Benchnot in index––34.6%#35–Vals AI
SkillsBenchnot in index––65.9%#3–Vals AI
SREBenchnot in index––4.6%#9–Vals AI
Tax Agent Benchnot in index––57.7%#35–Vals AI
Terminal-Bench 4.0 (Vals)not in index––12.1%#27–Vals AI
Terminal-Bench Sciencenot in index––7.1%#15–Vals AI
EnterpriseOps-Gymnot in index–50.4%#2––Artificial Analysis
FACTS Searchnot in index–––80.9%#10Google

Maths

Benchmarklowmediumhighnot statedSourceTrend
FrontierMath Tiers 1–3––71.6%#23–Epoch AI Benchmarking Hub
FrontierMath Tier 4––36.6%#30–Epoch AI Benchmarking Hub
OTIS Mock AIME––97.2%#30–Epoch AI Benchmarking Hub
ProofBench–––58.0%#19Vals AI
LiveBench Mathematicsnot in index––93.5%#20–LiveBench
LMArena Maths––1503#12–LMArena

Knowledge

Benchmarklowmediumhighnot statedSourceTrend
SimpleQA Verified––69.2%#9–Epoch AI Benchmarking Hub
LiveBench Data Analysisnot in index––68.0%#52–LiveBench
AA-Omniscience22.1#5423.7#5126.5#47–Artificial Analysis
MMLU-Pro––90.1%#6–Vals AI
LegalBench––87.3%#5–Vals AI
TaxEval––74.7%#30–Vals AI
Excel Modeling Benchmarknot in index––71.3%#11–Vals AI
MedCodenot in index––53.4%#9–Vals AI
MedScribenot in index––83.9%#37–Vals AI
GDP.pdfnot in index–21.8%#2523.8%#17–Epoch AI Benchmarking Hub
AA-Omniscience: accuracynot in index53.6%#4554.0%#4155.3%#36–Artificial Analysis
AA-Omniscience: non-hallucinationnot in index32.3%#19534.1%#18135.5%#172–Artificial Analysis

Instruction following

Benchmarklowmediumhighnot statedSourceTrend
LiveBench Languagenot in index––85.5%#13–LiveBench
LiveBench Instruction Followingnot in index––79.9%#2–LiveBench
LMArena Instruction Followingnot in index––1485#17–LMArena

Human preference

Benchmarklowmediumhighnot statedSourceTrend
LMArena Text––1488#17–LMArena
LMArena Creative Writingnot in index––1493#5–LMArena
LMArena Multi-turnnot in index––1493#19–LMArena
LMArena Longer Queriesnot in index––1500#13–LMArena

Multimodal

Benchmarklowmediumhighnot statedSourceTrend
LMArena Vision––1315#7–LMArena
MMMU-Pro84.9%#1484.7%#1585.5%#10–Artificial Analysis
MMMU-Pro (Vals)not in index––89.0%#5–Vals AI
MortgageTaxnot in index––66.7%#32–Vals AI
SAGEnot in index––49.2%#23–Vals AI

Long context

Benchmarklowmediumhighnot statedSourceTrend
AA-LCR78.7%#10183.0%#2481.7%#46–Artificial Analysis

Composite

Benchmarklowmediumhighnot statedSourceTrend
Epoch Capabilities Indexnot in index–––157.4#14Epoch AI Benchmarking Hub
LiveBench––78.8%#15–LiveBench
AA Intelligence Index37.0#7439.6#5939.1#64–Artificial Analysis
Vals Indexnot in index––51.3#24–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 Studio160 tok/s2.31 s$0.375$1.881.0M–
Google AI Studio144 tok/s2.31 s$1.35$6.751.0M–
Google AI Studio141 tok/s2.53 s$0.750$3.751.0M–
Google Vertex129 tok/s2.35 s$1.35$6.751.0M–
Google Vertex76 tok/s2.79 s$0.750$3.751.0M–
Google Vertex45 tok/s16 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$1.03$2.06$3.09$4.13Aug 26Sept 26
input outputnow $0.750 in · $3.75 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$0.00126.7 s
Summarise a 30-page report12,000 / 600$0.011$0.00527.7 s
Code edit6,000 / 1,500$0.010$0.007110.9 s
Agentic coding session60,000 / 4,000$0.060$0.03019.7 s
Structured extraction2,000 / 200$0.0023$0.00126.3 s

See also

Data as of 5 Oct 2026. Compare these configurations.

Cite as: BenchLeader, “Gemini 3.7 Flash: benchmarks, pricing, speed and rank”, https://www.benchleader.com/models/gemini-3-7-flash, data as of 5 Oct 2026.