[whatmodel.sh](../../README.md) / Gemini 3.6 Flash # Gemini 3.6 Flash Google · released 2026-07 · closed weights **score 72.3** ±0.6 · #58 of 732 · from 27 benchmark results across 8 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Gemini 3.6 Flash among every model on that task or test, worst to best. 
task                vs the others         rank  counts  tests
agent tasks         ▁▁▃▆█▄▅▅●▄▂▁    #67 of 313     15%      4
coding              ▁▁▁▃▅▇█▇▅●▃▁   #45 of 469*    7.9%      2
chat & writing      ▁▁▁▃▆▄▆█▆▇▇●   #18 of 402*    3.4%      1
math                ▁▁▃▅█▆▄█●▂▁▁    #47 of 431     49%      8
vision & documents  ▁▁▂▂▁▃▄▂▆▇●▄    #20 of 153    8.3%      5
agent tasks · #67 of 313 · 15% of its score Trails [GLM-5.1](glm-5-1.md), [Beam 1.0 501B A23B](beam-1-0-501b-a23b.md), [Qwen3.7 Plus](qwen3-7-plus.md) here, though they rank lower overall. 
test                                     result  vs the others in its run  difficulty            counts  why
Arena Agent (October 2026 aggrega…  -0.0768 IPS  ▁▁▁●▃▂█▁▂▂▁▁ beats 7/59   avg -0.379 IPS          8.6%  sharp
Terminal-Bench 2.1 (maker)                73.0%  ▁·▁·▁▁▄▃▆█●█ beats 42/63  hard · avg 25%          3.9%  sharp · small run · several runs
E2E-SWE (build a working codebase…        11.7%  ●··▅··▃█▃▅▃▅ beats 0/12   very hard · avg 3.7%    1.9%  sharp · at its level
Terminal-Bench 3.0 (maker)                 5.4%  ●·█··██····█ beats 0/4    very hard · avg 1.9%    0.6%  small run
coding · #45 of 469 · 7.9% of its score Beats [GPT-5.5](gpt-5-5.md), [GPT-5.6 Terra](gpt-5-6-terra.md), [GPT-5.4](gpt-5-4.md) here, though they rank higher overall. Trails [Qwen3.8 27B](qwen3-8-27b.md), [GLM-5.2](glm-5-2.md) here, though they rank lower overall. 
test                                result  vs the others in its run   difficulty            counts  why
Arena WebDev (Code Arena)           1537.5  ▁▁▃▂▅█▃●▅▅▁▂ beats 83/129  avg 981                 6.0%  sharp
E2E-SWE (build a working codebase…   11.7%  ●··▅··▃█▃▅▃▅ beats 0/12    very hard · avg 3.7%    1.9%  sharp · at its level
chat & writing · #18 of 402 · 3.4% of its score Beats [GPT-6 Astra](gpt-6-astra.md), [GPT-6.1 Sol](gpt-6-1-sol.md), [Claude Sonnet 5.5](claude-sonnet-5-5.md) here, though they rank higher overall. 
test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1483.2  ▁▁▁▂▃▃▃▆▆▇█● beats 366/385  avg 1260      3.4%  blunt
math · #47 of 431 · 49% of its score Beats [Grok 4.7](grok-4-7.md), [GLM-5.3-Flash](glm-5-3-flash.md) here, though they rank higher overall. Trails [Kimi K2.6](kimi-k2-6.md), [GLM-5.2](glm-5-2.md) here, though they rank lower overall. 
test                                result  vs the others in its run    difficulty            counts  why
FrontierMath tiers 1–3 v2            58.9%  ▂··▁▁▃▄▄●▆▄▃ beats 45/86    very hard · avg 1.2%     16%  sharp · at its level
FrontierMath tier 4 v2               22.0%  ▁▂▁▂●▅▂▃▂▁▁▁ beats 23/67    very hard · avg 0.1%     11%  sharp
MathArena ArXivMath June 2026 (48…   56.9%  ▂···▄·●█▆▆▄▂ beats 6/19     very hard · avg 5.1%    7.8%  sharp · at its level
MathArena ArXivMath April 2026 (4…   52.5%  ▁·····▅●█▄·▁ beats 8/20     very hard · avg 7.6%    7.0%  sharp · at its level
MathArena ArXivMath May 2026 (40 …   54.2%  ▁··▁▄●▄▁·▃·▁ beats 7/17     very hard · avg 11%     2.4%  at its level
MathArena BrokenArXiv April 2026 …   16.0%  ▂▂●█▃▃▂▂▂··▂ beats 3/18     very hard · avg 0.8%    2.2%  sharp · at its level · noisy
MathArena BrokenArXiv June 2026 (…   12.0%  ▄··●▂▂▂▆▄··▂ beats 2/14     very hard · avg 1.3%    1.6%  sharp · at its level · noisy
OTIS mock AIME 2024–2025             94.2%  ▃▃▄▃▃▃▇▇█●▂▅ beats 158/197  very hard · avg 7.2%    0.7%  sharp · maxed out · noisy
vision & documents · #20 of 153 · 8.3% of its score Beats [Claude Sonnet 5.5](claude-sonnet-5-5.md), [GPT-6 Sol](gpt-6-sol.md), [GPT-5.5](gpt-5-5.md) here, though they rank higher overall. 
test                                result  vs the others in its run    difficulty        counts  why
CharXiv (reasoning questions) (ma…   85.2%  ▃·▃·▃▃▃·▃▃█● beats 10/11    medium · avg 38%    3.7%  typical
Arena vision (style controlled)     1282.0  ▁▁▂▂▂▂▃▄▅▅█● beats 132/152  avg 1061            2.2%  blunt
Arena document (style controlled)   1453.4  ▁▁▃▃▄▃●█▅▇▄▅ beats 15/37    avg 1331            1.2%  blunt
SciDocBench scientific-document w…   59.9%  ▃▃···█▃▅▃·●▅ beats 9/11     hard · avg 25%      1.1%  blunt · at its level
PureDocBench document parsing (Av…   78.3%  ▂·▁·▂▂▅█·▁▂● beats 21/22    medium · avg 62%    0.1%  blunt · at its level
other tests (7) 
test                            result  vs the others in its run    difficulty            counts  why
Furniture Assembly (Epoch)       23.3%  ●▆▆▆▆▂▆▂▂▂▂▅ beats 4/32     very hard · avg 1.2%    5.1%  sharp · at its level
Mystery Game Puzzles (Epoch)     30.0%  █··▂▃▇▄●▂▄▂▁ beats 49/77    very hard · avg 9.4%    4.1%  sharp · noisy
GPQA Diamond                     94.1%  ▁▁▁▁▂▄▆▄▅▇█● beats 202/211  hard · avg 39%          2.8%  sharp
Chess Puzzles (Epoch)            40.0%  █▁▁▁▁▂▃▃●▂▁▁ beats 120/137  very hard · avg 5.8%    2.7%  sharp · noisy
SimpleQA Verified                66.2%  ▁▁▃▂▃▃▆▆█▄●▅ beats 70/81    very hard · avg 8.0%    1.3%  at its level
LVBench (maker)                  84.2%  ▇·█▅▅▄▄··●▁▁ beats 25/28    medium · avg 59%        1.1%  typical
MRCR v2 8-needle, 128k (maker)   91.8%  █▄▄█·▄···●·▄ beats 7/8      medium · avg 53%        1.0%  small run
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
Epoch AI                                        8   44% ▇▇▇▇▇
LMArena leaderboard dataset                     5   21% ▇▇▇
MathArena ArXivMath / BrokenArXiv / ArX…        5   21% ▇▇▇
Google DeepMind Gemini model page and m…        5    9% ▇
E2E-SWE: Benchmarking LLMs on Building …        1    2% ▇
Google Cloud developer's guides to Gemi…        1    2% ▇
SciDocBench: A Workflow-Centered Benchm…        1    1% ▇
How Far Is Document Parsing from Solved…        1    0% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 72.3
Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · MathArena ArXivMath / BrokenArXiv / ArXivLean model-output datasets ([CC BY-SA 4.0](../../licenses/matharena-sharealike-NOTICE.txt)) · E2E-SWE: Benchmarking LLMs on Building Working Codebases from Scratch ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Google Cloud developer's guides to Gemini 3.8 Flash and 3.7 Flash ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · SciDocBench: A Workflow-Centered Benchmark and Data Pipeline for Scientific Document Understanding ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · How Far Is Document Parsing from Solved? PureDocBench: A Source-Traceable Benchmark across Clean ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) ## How to run it ### Through an API api (Google) · $1.50/M blended · $0.75 in / $3.75 out per 1M tokens · [ai.google.dev](https://ai.google.dev/gemini-api/docs/models) [price source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/providers/google/models/gemini-3.6-flash.toml): Standard base token rate from models.dev. Provider-specific tiers, long-context surcharges, caching and promotions may differ. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/google/gemini-3.6-flash.toml) [interactive version](https://whatmodel.sh/model/gemini-3-6-flash) · [JSON](https://whatmodel.sh/api?format=json&all=1)