What are Gemini 3.7 Flash's benchmark scores?

As of 24 Oct 2026, Gemini 3.7 Flash (high effort) scores 63.5 ±3.5 on the BenchLeader Index, top 100, strongest in composite (73) and weakest in agents & tools (48). That rests on 28 independent benchmark results, and every score on this page links to its publisher.

How does Gemini 3.7 Flash rank?

Gemini 3.7 Flash (high effort) is #64 of 785 ranked configurations on 24 Oct 2026 (top 100). 140 other configurations score within its margin, so the exact position matters less than the band. The leader is Claude Opus 5.5 (max effort) at 72.6.

How much does Gemini 3.7 Flash cost per 1M tokens?

As of 24 Oct 2026, Gemini 3.7 Flash costs $0.750 per 1M input tokens and $3.75 per 1M output tokens, $1.50 blended (three input tokens per output token). The list price last changed on 22 Oct 2026.

Is Gemini 3.7 Flash better than GLM 5.3?

As of 24 Oct 2026, Gemini 3.7 Flash (high) and GLM 5.3 (max) are neck and neck on quality (63.5 vs 63.5 on the BenchLeader Index, inside the ±4.7 margin of error), and Gemini 3.7 Flash (high) costs 1.4× less, which makes it the better buy for most workloads. The head-to-head page has the benchmark-by-benchmark detail.

When was Gemini 3.7 Flash released?

Google released Gemini 3.7 Flash on 13 Aug 2026. BenchLeader has results for it at 4 reasoning-effort settings.

GoogleReasoning modelCompare with another model

Gemini 3.7 Flash

Reasoning effort

Gemini 3.7 Flash is a Google proprietary reasoning model, released 13 Aug 2026. Its best configuration (high reasoning effort) ranks #64 of 785 on the BenchLeader Index at 63.5 ±3.5, in the upper half. The ± is the point: 140 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 (73) and lowest in agents & tools (48). At $1.50 per million tokens blended it is pricier than most ranked models. Output speed of 88 tokens per second puts it faster than most, with a first token in 2.5 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 22 Oct 2026.

Blended price: $1.50/M; $0.750 in · $3.75 out
Output speed: 88 tok/s; OpenRouter traffic, 7-day median; not yet measured by Artificial Analysis
First answer: 2.45 s
Context: 1.0M
Full answer: –
Cost per run: –
Released: 13 Aug 2026

How it scores by categoryDashed line = average model (50). One step of 15 = one standard deviation.

Overall index64
Reasoning67
Coding63
Agents & tools48
Maths63
Knowledge68
Human preference68
Multimodal68
Long context66
Composite73

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.

Model	Released	Index	Rank
Gemini 3.8 Flashhigh	2 Sept 2026	64.4	#50
Gemini 3.7 Flashhighthis page	13 Aug 2026	63.5	#64
Gemini 3.6 Flashhigh	21 Jul 2026	62.0	#89
Gemini 3.5 Flashhigh	19 May 2026	63.1	#70
Gemini 3 Flashthinking	17 Dec 2025	59.1	#138
Gemini 2.5 Flash 09 2025thinking	25 Sept 2025	51.1	#351
Gemini 2.5 Flash	17 Apr 2025	52.7	#306
Gemini 2.0 Flash	5 Feb 2025	43.2	#574
Gemini 1.5 Flash 002	24 Sept 2024	38.3	#716
Gemini 1.5 Flash 001	23 May 2024	–	–
Gemini 1.5 Flash	14 May 2024	44.3	#541
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.

Effort	Index	Rank	Speed	First answer	Chat reply cost	Categories
low	60.3	#113	88 tok/s	2.45 s	$0.0014	Agents & tools 58 · Coding 59 · Composite 71 · Knowledge 72 · Long context 64 · Multimodal 68 · Reasoning 55
medium	63.1	#71	88 tok/s	2.45 s	$0.0014	Agents & tools 64 · Coding 68 · Composite 74 · Knowledge 72 · Long context 67 · Multimodal 68 · Reasoning 60
highbest	63.5	#64	88 tok/s	2.45 s	$0.0014	Agents & tools 48 · Coding 63 · Composite 73 · Human preference 68 · Knowledge 68 · Long context 66 · Maths 63 · Multimodal 68 · Reasoning 67
not stated	53.4	#289	88 tok/s	2.45 s	$0.0014	Agents & tools 57 · Coding 57 · Composite 50 · Maths 58

How its index has moved

8 Sept 2026 to 23 Oct 2026

high
medium
low
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

Benchmark	low	medium	high	not stated	Source
GPQA Diamond	–	–	94.8%#6	–	Epoch AI Benchmarking Hub
Arena Hard Prompts	–	–	1509#19	–	Arena (LMArena)
LiveBench 2.0 Reasoningnot in indexeffort not stated	–	–	–	87.8%#24	LiveBench
LiveBench 1.0 Reasoningnot in index	–	–	87.8%#25	–	LiveBench
GPQA Diamond (AA)not in index	90.1%#64	92.1%#39	94.5%#6	–	Artificial Analysis
Humanity's Last Exam (AA)not in index	35.1%#119	39.0%#95	47.9%#37	–	Artificial Analysis
GPQA Diamond (Vals)not in index	–	–	93.9%#5	–	Vals AI
ARC-AGI-1	85.2%#112	91.2%#67	95.5%#33	–	ARC Prize
ARC-AGI-2	52.9%#104	63.8%#77	84.6%#31	–	ARC Prize
CritPt	5.7%#148	9.4%#124	14.3%#98	–	Artificial Analysis
Chess Puzzlesnot in index	–	–	66.0%#6	–	Epoch AI Benchmarking Hub
Mystery Game Puzzlesnot in index	–	–	37.0%#19	–	Epoch AI Benchmarking Hub
LMCAnot in index	47.1%#81	49.2%#72	50.4%#63	–	Epoch AI Benchmarking Hub
DTBenchnot in index	93.3%#64	94.7%#52	96.8%#21	–	Epoch AI Benchmarking Hub
ForecastBenchnot in index	–	–	66.2%#5	–	ForecastBench

Coding

Benchmark	low	medium	high	not stated	Source
SciCode	53.6%#72	59.8%#11	57.2%#32	–	SciCode
FrontierCodeeffort not stated	–	–	–	43.6%#18	Cognition
Arena Coding	–	–	1520#36	–	Arena (LMArena)
Arena WebDev	–	–	1591#30	–	Arena (LMArena)
LiveBench 2.0 Codingnot in indexeffort not stated	–	–	–	80.5%#32	LiveBench
LiveBench 1.0 Codingnot in index	–	–	78.9%#29	–	LiveBench
SciCode (AA)not in index	55.7%#48	59.8%#13	57.2%#32	–	Artificial Analysis
LiveCodeBench	–	–	88.7%#5	–	Vals AI
IOI	–	–	67.8%#14	–	Vals AI
SWE-bench (Vals)not in index	–	–	80.8%#23	–	Vals AI
Code Migrationnot in index	–	–	34.8%#37	–	Vals AI
ProgramBenchnot in index	–	–	0.0%#32	–	Vals AI
Vibe Code Bench v1.1not in index	–	–	70.4%#41	–	Vals AI
DeepSWE v1.1not in index	53.8%#42	65.5%#25	65.3%#27	–	Epoch AI Benchmarking Hub
ALE-Benchnot in index	–	–	904.3#65	–	Epoch AI Benchmarking Hub
FrontierSWEnot in index	–	–	20.3%#15	–	Epoch AI Benchmarking Hub
Terminal-Bench 4.0 (AA)not in index	–	–	13.6%#76	–	Artificial Analysis
Terminal-Bench 2.1 (AA)not in index	79.8%#46	78.3%#52	85.8%#24	–	Artificial Analysis

Agents & tools

Benchmark	low	medium	high	not stated	Source
Terminal-Bench	–	–	11.2%#82	–	Terminal-Bench
Remote Labor Indexeffort not stated	–	–	–	5.0%#8	Scale AI / CAIS
APEX-Agentseffort not stated	–	–	–	67.8%#5	Mercor
Arena Agent	–	–	-1#31	–	Arena (LMArena)
LiveBench Long Agenticnot in indexeffort not stated	–	–	–	28.3%#35	LiveBench
LiveBench Browsernot in indexeffort not stated	–	–	–	56.0%#30	LiveBench
LiveBench Agentic Codingnot in index	–	–	58.3%#19	–	LiveBench
Terminal-Bench 2.1 (Vals)	–	–	77.5%#14	–	Vals AI
τ³-Banking (AA)	29.5%#70	35.5%#46	32.8%#57	–	Artificial Analysis
Analyst Agent (AA)not in index	–	–	60.0%#1	–	Artificial Analysis
CyberBenchnot in index	–	–	43.8%#40	–	Vals AI
Finance Agent v2not in index	–	–	59.0%#7	–	Vals AI
Harvey's Legal Agent Benchmark v1not in index	–	–	8.8%#25	–	Vals AI
Legal Research Benchnot in index	–	–	34.6%#43	–	Vals AI
SkillsBenchnot in index	–	–	65.9%#3	–	Vals AI
SREBenchnot in index	–	–	4.6%#17	–	Vals AI
Tax Agent Benchnot in index	–	–	22.8%#41	–	Vals AI
Terminal-Bench 4.0 (Vals)not in index	–	–	12.1%#36	–	Vals AI
Terminal-Bench Sciencenot in index	–	–	7.1%#20	–	Vals AI
AutomationBenchnot in index	–	–	62.0%#29	–	Artificial Analysis
GDP.pdf (AA)not in index	–	–	23.6%#43	–	Artificial Analysis
EnterpriseOps-Gymnot in index	–	50.4%#2	–	–	Artificial Analysis
FACTS Searchnot in indexeffort not stated	–	–	–	80.9%#12	Google

Maths

Benchmark	high	not stated	Source
FrontierMath Tiers 1–3	71.6%#26	–	Epoch AI Benchmarking Hub
FrontierMath Tier 4	36.6%#34	–	Epoch AI Benchmarking Hub
OTIS Mock AIME	97.2%#37	–	Epoch AI Benchmarking Hub
ProofBencheffort not stated	–	58.0%#21	Vals AI
LiveBench Mathematicsnot in index	93.5%#22	–	LiveBench
Arena Maths	1505#11	–	Arena (LMArena)

Knowledge

Benchmark	low	medium	high	not stated	Source
SimpleQA Verified	–	–	69.2%#10	–	Epoch AI Benchmarking Hub
LiveBench 2.0 Data Analysisnot in indexeffort not stated	–	–	–	68.0%#37	LiveBench
LiveBench Researchnot in indexeffort not stated	–	–	–	76.5%#24	LiveBench
LiveBench 1.0 Data Analysisnot in index	–	–	68.0%#54	–	LiveBench
AA-Omniscience	22.1#58	23.7#55	26.5#49	–	Artificial Analysis
MMLU-Pro	–	–	90.1%#6	–	Vals AI
LegalBench	–	–	87.3%#6	–	Vals AI
TaxEval	–	–	74.7%#30	–	Vals AI
Excel Modeling Benchmarknot in index	–	–	71.3%#13	–	Vals AI
MedCodenot in index	–	–	53.4%#9	–	Vals AI
MedScribenot in index	–	–	83.9%#39	–	Vals AI
GDP.pdfnot in index	–	21.8%#29	23.8%#21	–	Epoch AI Benchmarking Hub
AA-Omniscience: accuracynot in index	53.6%#46	54.0%#42	55.3%#37	–	Artificial Analysis
AA-Omniscience: non-hallucinationnot in index	32.3%#210	34.1%#195	35.5%#186	–	Artificial Analysis

Instruction following

Benchmark	high	Source
LiveBench Languagenot in index	85.5%#13	LiveBench
LiveBench Instruction Followingnot in index	79.9%#2	LiveBench
Arena Instruction Followingnot in index	1487#16	Arena (LMArena)

Human preference

Benchmark	high	Source
Arena Text	1490#16	Arena (LMArena)
Arena Creative Writingnot in index	1492#6	Arena (LMArena)
Arena Multi-turnnot in index	1489#23	Arena (LMArena)
Arena Longer Queriesnot in index	1501#15	Arena (LMArena)

Multimodal

Benchmark	low	medium	high	not stated	Source
Arena Vision	–	–	1314#8	–	Arena (LMArena)
LiveBench Designnot in indexeffort not stated	–	–	–	73.2%#33	LiveBench
MMMU-Pro	84.9%#14	84.7%#15	85.5%#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

Benchmark	low	medium	high	Source	Trend
AA-LCR	78.7%#110	83.0%#26	81.7%#52	Artificial Analysis
MLCRnot in index	–	–	12.2%#96	Artificial Analysis

Composite

Benchmark	low	medium	high	not stated	Source
Epoch Capabilities Indexnot in indexeffort not stated	–	–	–	158.0#18	Epoch AI Benchmarking Hub
LiveBench 2.0effort not stated	–	–	–	67.2%#30	LiveBench
LiveBench 1.0not in index	–	–	78.8%#16	–	LiveBench
AA Intelligence Index v4.3.2	37.0#85	39.6#67	39.1#72	–	Artificial Analysis
Vals RSI Indexnot in index	–	–	18.3%#20	–	Vals AI

Where it wins

Benchmarks where this configuration ranks in the top five of every configuration measured.

#5LiveCodeBench88.7% · of 137

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.

Provider	Speed	First token	Input $/M	Output $/M	Context	Quantisation
Google Vertex	96 tok/s	1.61 s	$0.825	$4.13	1.0M	–
Google Vertex	90 tok/s	2.45 s	$0.750	$3.75	1.0M	–
Google AI Studio	88 tok/s	2.38 s	$0.750	$3.75	1.0M	–
Google Vertex	77 tok/s	1.68 s	$1.35	$6.75	1.0M	–
Google AI Studio	67 tok/s	1.86 s	$0.375	$1.88	1.0M	–
Google AI Studio	54 tok/s	1.53 s	$1.35	$6.75	1.0M	–
Google Vertex	25 tok/s	45 s	$0.375	$1.88	1.0M	–

Price history

The price above, per 1M tokens, every day it changed.

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.

Workload	Tokens in / out	Cost	With caching	Time
Chat reply	400 / 300	$0.0014	$0.0012	5.9 s
Summarize a 30-page report	12,000 / 600	$0.011	$0.0052	9.3 s
Code edit	6,000 / 1,500	$0.010	$0.0071	19.5 s
Agentic coding session	60,000 / 4,000	$0.060	$0.030	47.9 s
Structured extraction	2,000 / 200	$0.0023	$0.0012	4.7 s

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Questions people ask

What are Gemini 3.7 Flash's benchmark scores?: As of 24 Oct 2026, Gemini 3.7 Flash (high effort) scores 63.5 ±3.5 on the BenchLeader Index, top 100, strongest in composite (73) and weakest in agents & tools (48). That rests on 28 independent benchmark results, and every score on this page links to its publisher.
How does Gemini 3.7 Flash rank?: Gemini 3.7 Flash (high effort) is #64 of 785 ranked configurations on 24 Oct 2026 (top 100). 140 other configurations score within its margin, so the exact position matters less than the band. The leader is Claude Opus 5.5 (max effort) at 72.6.
How much does Gemini 3.7 Flash cost per 1M tokens?: As of 24 Oct 2026, Gemini 3.7 Flash costs $0.750 per 1M input tokens and $3.75 per 1M output tokens, $1.50 blended (three input tokens per output token). The list price last changed on 22 Oct 2026.
Is Gemini 3.7 Flash better than GLM 5.3?: As of 24 Oct 2026, Gemini 3.7 Flash (high) and GLM 5.3 (max) are neck and neck on quality (63.5 vs 63.5 on the BenchLeader Index, inside the ±4.7 margin of error), and Gemini 3.7 Flash (high) costs 1.4× less, which makes it the better buy for most workloads. The head-to-head page has the benchmark-by-benchmark detail.
When was Gemini 3.7 Flash released?: Google released Gemini 3.7 Flash on 13 Aug 2026. BenchLeader has results for it at 4 reasoning-effort settings.

Data as of 24 Oct 2026. Compare these configurations.

Cite as: BenchLeader, “Gemini 3.7 Flash: benchmarks, pricing, speed and rank”, data as of 24 Oct 2026, https://www.benchleader.com/models/gemini-3-7-flash.