BenchLeader

Gemini 2.5 Flash

Reasoning effort

Gemini 2.5 Flash is a Google proprietary reasoning model, released 17 Apr 2025. Its best configuration ranks #306 of 785 on the BenchLeader Index at 52.7 ±3.9, in the upper half. The ± is the point: 289 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 instruction following (63) and lowest in reasoning (41). At $0.850 per million tokens blended it is mid-priced. It has been measured at 3 reasoning-effort settings; this summary describes the best-scoring one, and the tabs above switch between them. Last measured 22 Oct 2026.

Blended price
$0.850/M
$0.300 in · $2.50 out
Output speed
–
First answer
–
Context
1.0M
Full answer
–
Cost per run
–
Released
17 Apr 2025
How it scores by categoryDashed line = average model (50). One step of 15 = one standard deviation.
  1. Overall index53
  2. Reasoning41
  3. Coding44
  4. Agents & tools48
  5. Maths55
  6. Knowledge48
  7. Instruction following63
  8. Human preference59
  9. Multimodal62
  10. Long context58

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 202664.4#50
Gemini 3.7 Flashhigh13 Aug 202663.5#64
Gemini 3.6 Flashhigh21 Jul 202662.0#89
Gemini 3.5 Flashhigh19 May 202663.1#70
Gemini 3 Flashthinking17 Dec 202559.1#138
Gemini 2.5 Flash 09 2025thinking25 Sept 202551.1#351
Gemini 2.5 Flashthis page17 Apr 202552.7#306
Gemini 2.0 Flash5 Feb 202543.2#574
Gemini 1.5 Flash 00224 Sept 202438.3#716
Gemini 1.5 Flash 00123 May 2024––
Gemini 1.5 Flash14 May 202444.3#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.

EffortIndexRankSpeedFirst answerChat reply costCategories
no reasoning46.0#501206 tok/s0.36 s$0.0009Agents & tools 44 · Composite 40 · Instruction following 42 · Knowledge 42 · Long context 49 · Multimodal 48 · Reasoning 43
thinking47.7#448224 tok/s17 s$0.0009Agents & tools 46 · Coding 36 · Composite 44 · Instruction following 52 · Knowledge 51 · Long context 57 · Multimodal 52 · Reasoning 40
not statedbest52.7#306––$0.0009Agents & tools 48 · Coding 44 · Human preference 59 · Instruction following 63 · Knowledge 48 · Long context 58 · Maths 55 · Multimodal 62 · Reasoning 41

How its index has moved

8 Sept 2026 to 23 Oct 2026
425057
  • not stated
  • thinking
  • no reasoning

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

Benchmarkno reasoningthinkingnot statedSourceTrend
Humanity's Last Exameffort not stated––12.1%#28Scale AI / CAIS
SimpleBencheffort not stated––41.2%#58SimpleBench
Arena Hard Promptseffort not stated––1420#147Arena (LMArena)
GPQA Diamond (AA)not in index68.3%#31479.0%#203–Artificial Analysis
Humanity's Last Exam (AA)not in index4.7%#47712.1%#281–Artificial Analysis
GPQA Diamond (Vals)not in indexeffort not stated––57.6%#109Vals AI
Kagi LLM Benchmark–56.8%#6044.1%#97Kagi LLM Benchmark
GPQA Diamond (HELM)not in indexeffort not stated––39.0%#50HELM Capabilities
ARC-AGI-1–33.3%#20233.3%#202ARC Prize
ARC-AGI-2–2.5%#1991.7%#210ARC Prize
CritPt1.4%#2111.1%#223–Artificial Analysis
LMCAnot in indexeffort not stated––27.5%#145Epoch AI Benchmarking Hub
DTBenchnot in indexeffort not stated––76.5%#145Epoch AI Benchmarking Hub
ForecastBenchnot in indexeffort not stated––62.9%#50ForecastBench

Coding

Benchmarkthinkingnot statedSourceTrend
WeirdML41.0%#9941.0%#99WeirdML
Arena Codingeffort not stated–1427#176Arena (LMArena)
LiveCodeBench46.9%#11456.9%#105Vals AI
Aider Polygloteffort not stated–55.1%#19Aider polyglot leaderboard
SWE-bench Verified (bash only)effort not stated–28.7%#38SWE-bench
SWE-bench Verified (any scaffold)not in indexeffort not stated–28.7%#52SWE-bench
ALE-Benchnot in indexeffort not stated–661.9#90Epoch AI Benchmarking Hub

Agents & tools

Benchmarkno reasoningthinkingnot statedSourceTrend
Terminal-Bencheffort not stated––17.1%#76Terminal-Bench
Terminal-Bench Hard12.1%#21013.6%#201–Artificial Analysis
τ²-Bench Telecom (AA)not in index14.9%#36331.6%#251–Artificial Analysis
BFCL Overalleffort not stated––56.2%#13Berkeley Function Calling Leaderboard
BALROGnot in indexeffort not stated––33.5%#13BALROG
Vending-Bench 2not in indexeffort not stated––548.8#58Andon Labs
BrowseComp-Plusnot in indexeffort not stated––16.3%#11BrowseComp-Plus
BrowseCompnot in indexeffort not stated––2.5%#9OpenAI
DeepSearchQAnot in indexeffort not stated––25.9%#8Google

Maths

Benchmarkthinkingnot statedSourceTrend
OTIS Mock AIMEeffort not stated–73.1%#134Epoch AI Benchmarking Hub
Omni-MATH (HELM)effort not stated–38.4%#32HELM Capabilities
MATH 500not in index91.8%#1991.6%#21Vals AI
Arena Mathseffort not stated–1409#136Arena (LMArena)

Knowledge

Benchmarkno reasoningthinkingnot statedSourceTrend
AA-Omniscience-42.6#336-29.8#279–Artificial Analysis
LegalBencheffort not stated––83.8%#47Vals AI
CorpFineffort not stated––54.2%#92Vals AI
TaxEval–70.5%#8771.2%#79Vals AI
MedQA–91.0%#4286.7%#56Vals AI
PRBench Financeeffort not stated––38.4%#31Scale AI SEAL
PRBench Legaleffort not stated––41.0%#28Scale AI SEAL
MMLU-Pro (HELM)not in indexeffort not stated––63.9%#43HELM Capabilities
MedCodenot in index–40.4%#6338.4%#69Vals AI
MedScribenot in index–83.0%#4682.9%#48Vals AI
AA-Omniscience: accuracynot in index26.1%#21925.9%#222–Artificial Analysis
AA-Omniscience: non-hallucinationnot in index7.0%#49324.7%#245–Artificial Analysis

Instruction following

Benchmarkno reasoningthinkingnot statedSourceTrend
IFBench39.0%#27450.3%#173–Artificial Analysis
IFEval (HELM)effort not stated––89.8%#10HELM Capabilities
WildBench (HELM)effort not stated––81.7%#25HELM Capabilities
Arena Instruction Followingnot in indexeffort not stated––1400#138Arena (LMArena)

Human preference

Benchmarkthinkingnot statedSourceTrend
Arena Texteffort not stated–1409#141Arena (LMArena)
Arena Creative Writingnot in indexeffort not stated–1394#113Arena (LMArena)
Arena Multi-turnnot in indexeffort not stated–1403#150Arena (LMArena)
Arena Longer Queriesnot in indexeffort not stated–1417#138Arena (LMArena)
Lech Mazur Creative Writingnot in index76.5%#22–Lech Mazur

Multimodal

Benchmarkno reasoningthinkingnot statedSourceTrend
Arena Visioneffort not stated––1254#61Arena (LMArena)
MMMU-Pro65.5%#17469.1%#158–Artificial Analysis
VISTAeffort not stated––49.1%#13Scale AI SEAL
MMMU (validation)effort not stated––79.7%#6MMMU
MMMU-Pro (Vals)not in index–71.9%#6468.0%#71Vals AI
MortgageTaxnot in index–57.5%#7158.4%#68Vals AI
SAGEnot in index–39.6%#5944.8%#45Vals AI
AudioMCnot in index–40.0%#926.1%#15Scale AI SEAL
AudioMC (text output)not in index–40.0%#726.1%#10Scale AI SEAL
VTBnot in indexeffort not stated––4.7%#16Scale AI SEAL
GeoBenchnot in indexeffort not stated––76.0%#7GeoBench
VPCTnot in indexeffort not stated––38.0%#21Epoch AI Benchmarking Hub
SpatialViz-Benchnot in indexeffort not stated––36.9%#3Epoch AI Benchmarking Hub

Long context

Benchmarkno reasoningthinkingnot statedSourceTrend
Fiction.LiveBench 120keffort not stated––68.8%#8Fiction.live
AA-LCR49.9%#32465.3%#250–Artificial Analysis

Composite

Benchmarkno reasoningthinkingnot statedSourceTrend
Epoch Capabilities Indexnot in indexeffort not stated––142.9#97Epoch AI Benchmarking Hub
AA Intelligence Index v4.3.29.8#38913.1#318–Artificial Analysis
HELM Capabilities meannot in indexeffort not stated––62.6%#39HELM Capabilities

Safety & honesty

Benchmarknot statedSourceTrend
MASKnot in indexpossible contamination49.1%#53Scale AI SEAL
PropensityBenchnot in index68.0%#4Scale AI SEAL

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 $0.030 per 1M. Reasoning tokens are not modelled.

WorkloadTokens in / outCostWith cachingTime
Chat reply400 / 300$0.0009$0.0008–
Summarize a 30-page report12,000 / 600$0.0051$0.0027–
Code edit6,000 / 1,500$0.0056$0.0043–
Agentic coding session60,000 / 4,000$0.028$0.016–
Structured extraction2,000 / 200$0.0011$0.0007–

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Questions people ask

What are Gemini 2.5 Flash's benchmark scores?
As of 24 Oct 2026, Gemini 2.5 Flash scores 52.7 ±3.9 on the BenchLeader Index, strongest in instruction following (63) and weakest in reasoning (41). That rests on 29 independent benchmark results, and every score on this page links to its publisher.
How does Gemini 2.5 Flash rank?
Gemini 2.5 Flash is #306 of 785 ranked configurations on 24 Oct 2026. 289 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 2.5 Flash cost per 1M tokens?
As of 24 Oct 2026, Gemini 2.5 Flash costs $0.300 per 1M input tokens and $2.50 per 1M output tokens, $0.850 blended (three input tokens per output token).
When was Gemini 2.5 Flash released?
Google released Gemini 2.5 Flash on 17 Apr 2025. BenchLeader has results for it at 3 reasoning-effort settings.

Data as of 24 Oct 2026. Compare these configurations.

Cite as: BenchLeader, “Gemini 2.5 Flash: benchmarks, pricing, speed and rank”, data as of 24 Oct 2026, https://www.benchleader.com/models/gemini-2-5-flash.