Gemini 3.8 Flash

Made by Google DeepMind, released 2 Sep 2026. Epoch AI lists its access as “API access”. Appears strongest at coding, knowledge and writing among the models listed here.

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In short

What it does well

  • Coding: Top tier (12th of 77 listed models)
  • Knowledge: Top tier (7th of 43 listed models)
  • Writing: Top tier (7th of 77 listed models)

Where it falls short

  • Maths: Middle (23rd of 43 listed models)

What we don't know yet

  • Fixing real software bugs: Not enough data

The four ratings

  • Top tier
  • Strong
  • Middle
  • Behind the leaders
  • Not enough data

Ratings compare only the 77 models listed here, in quarters, best first. “Not enough data” means no result yet. It is never a low score.

Writing
Top tierHow often people preferred its answers to writing prompts. 1,491 on Arena's scale · Source for Gemini 3.8 Flash writing score (lmarena)
Coding
Top tierHow often people preferred its answers to coding questions. 1,509 on Arena's scale · Source for Gemini 3.8 Flash coding score (lmarena)
Maths
MiddleHow many very hard maths problems it solved. 68.4% · Source for Gemini 3.8 Flash maths score (epoch)
Knowledge
Top tierHow many short factual questions it answered correctly. 69.7% · Source for Gemini 3.8 Flash knowledge score (epoch)

Evidence completeness: Broad evidence, 12 of 13 tests have a result.

Where it is relatively stronger or weaker

This compares each kind of request with the model's own overall position among the 77 listed models, using Arena's votes. It is about performance on different languages, kinds of request and topics, not about political or social bias.

Relatively stronger

No category stands out as clearly stronger than its overall position.

Relatively weaker

No category stands out as clearly weaker than its overall position.

Show every category we can see
Position in each Arena category
CategoryPositionArena scoreVotesSource
Expert-level prompts
Kind of request
9th of 771,526
95% range 1,517 to 1,535
5,549Source for Expert-level prompts
Hard prompts
Kind of request
7th of 771,508
95% range 1,503 to 1,514
30,731Source for Hard prompts
Long prompts
Kind of request
7th of 771,509
95% range 1,503 to 1,514
21,802Source for Long prompts
Longer conversations
Kind of request
5th of 771,504
95% range 1,496 to 1,512
7,190Source for Longer conversations
Chinese
Language
7th of 761,550
95% range 1,540 to 1,561
3,978Source for Chinese
English prompts
Language
8th of 771,498
95% range 1,492 to 1,504
18,714Source for English prompts
French
Language
17th of 721,494
95% range 1,478 to 1,511
1,682Source for French
German
Language
4th of 551,507
95% range 1,484 to 1,529
760Source for German
Japanese
Language
5th of 541,508
95% range 1,486 to 1,531
887Source for Japanese
Korean
Language
5th of 541,477
95% range 1,455 to 1,500
844Source for Korean
Non-English prompts
Language
5th of 771,492
95% range 1,486 to 1,497
29,077Source for Non-English prompts
Russian
Language
4th of 771,516
95% range 1,508 to 1,525
6,256Source for Russian
Spanish
Language
12th of 701,487
95% range 1,472 to 1,502
1,649Source for Spanish
Entertainment, sport and media
Topic
7th of 771,481
95% range 1,474 to 1,488
12,638Source for Entertainment, sport and media
Law and government
Topic
10th of 761,500
95% range 1,489 to 1,510
4,087Source for Law and government
Medicine and health
Topic
6th of 751,506
95% range 1,495 to 1,517
3,353Source for Medicine and health
Science
Topic
4th of 771,523
95% range 1,516 to 1,531
7,897Source for Science
Software and IT
Topic
12th of 771,507
95% range 1,501 to 1,513
17,809Source for Software and IT

Bias: what we can and can't see

Our sources do not measure political lean, stereotyping, flattery (agreeing with you too readily) or safety behaviour, so we cannot say how any model does on those. What we can show is above: where a model is relatively stronger or weaker across languages, kinds of request and topics. Also, people voting in Arena can favour answer style, such as length and formatting, and our numbers do not adjust for that. See what AI bias means and what these tests can tell you.

Best-supported use cases

This model is not among the options we recommend for any of the five tasks. That reflects the rules and the models listed, not a verdict on the model.

Where an alternative might be more suitable

What it is

Maker
Google DeepMind
Release date (Epoch AI)
2 Sep 2026
Access, as listed by Epoch AI
API access
Licence, as listed by Arena
Proprietary
Maker's country (Epoch AI)
United States of America
Last observation in our data
28 Oct 2026
Data last changed
29 Oct 2026, 14:36 UTC

We have not verified an official product page for this model, so we do not link to one.

How you can use it

Proprietary. The model's files are not published, so it is generally available only through its maker or partners the maker chooses.

How you can reach it (an app, a website, a developer API or a third-party tool) and what that costs are not covered by our benchmark data. Where we could verify prices from the maker's own pages, they are shown under Price. Epoch AI lists access as “API access”. See models, apps and harnesses and how AI pricing works.

Price

Price not independently verified

See Gemini plans and pricing (checked 28 Oct 2026). Page opened and read.

We never infer subscription prices from API prices. New to “tokens” and “per million”? See how AI pricing works.

Good to remember

  • Benchmarks are samples of tasks. None proves performance in every situation.
  • Arena scores come from people choosing between answers. That measures preference, not correctness.
  • Ratings are relative to the 77 models listed here. A different set would change them.
  • Sources test models at different settings. Each result shows the setting used, and we do not adjust it.

The evidence, for the curious

Show all 13 tests with settings and sources
All published results for Gemini 3.8 Flash. Ratings compare only the 77 models on this site.
TestResultRatingSetting usedSource identifierObservedLinks
Arena text: overall preference
Arena 'text' leaderboard (Bradley-Terry rating, no style control)
1,498
Arena score
95% range 1,494 to 1,503, 47,803 votes
Top tier effort: High, bradley-terry rating Gemini 3.8 Flash (High)
lmarena · text/latest
28 Oct 2026 Source for Gemini 3.8 Flash Arena text: overall preference score (lmarena)
Arena text: creative writing
Arena 'text' leaderboard, category creative_writing
1,491
Arena score
95% range 1,484 to 1,498, 10,581 votes
Top tier effort: High, bradley-terry rating Gemini 3.8 Flash (High)
lmarena · text/latest
28 Oct 2026 Source for Gemini 3.8 Flash Arena text: creative writing score (lmarena)
Arena text: coding
Arena 'text' leaderboard, category coding
1,509
Arena score
95% range 1,503 to 1,516, 12,376 votes
Top tier effort: High, bradley-terry rating Gemini 3.8 Flash (High)
lmarena · text/latest
28 Oct 2026 Source for Gemini 3.8 Flash Arena text: coding score (lmarena)
Arena text: maths
Arena 'text' leaderboard, category math
1,530
Arena score
95% range 1,517 to 1,542, 2,496 votes
Top tier effort: High, bradley-terry rating Gemini 3.8 Flash (High)
lmarena · text/latest
28 Oct 2026 Source for Gemini 3.8 Flash Arena text: maths score (lmarena)
Arena text: following instructions
Arena 'text' leaderboard, category instruction_following
1,493
Arena score
95% range 1,487 to 1,499, 17,179 votes
Top tier effort: High, bradley-terry rating Gemini 3.8 Flash (High)
lmarena · text/latest
28 Oct 2026 Source for Gemini 3.8 Flash Arena text: following instructions score (lmarena)
Arena text: business and management
Arena 'text' leaderboard, category industry_business_and_management_and_financial_operations
1,480
Arena score
95% range 1,473 to 1,487, 8,607 votes
Top tier effort: High, bradley-terry rating Gemini 3.8 Flash (High)
lmarena · text/latest
28 Oct 2026 Source for Gemini 3.8 Flash Arena text: business and management score (lmarena)
Arena WebDev: building web apps
Arena 'webdev' leaderboard (Code Arena), category overall
1,583
Arena score
95% range 1,576 to 1,590, 12,342 votes
Strong effort: High, bradley-terry rating Gemini 3.8 Flash (High)
lmarena · webdev/latest
28 Oct 2026 Source for Gemini 3.8 Flash Arena WebDev: building web apps score (lmarena)
FrontierMath Tiers 1-3 (v2)
FrontierMath Tiers 1-3 v2 (private set), benchmark released 2026-06-12
68.4%
share of problems solved
standard error ±2.8 points
Middle effort: high, best score across scorers gemini-3.8-flash_high
epoch · frontiermath_tiers_1_3_v2.csv
2 Sep 2026 Source for Gemini 3.8 Flash FrontierMath Tiers 1-3 (v2) score (epoch)
No public run log in the data file.
OTIS Mock AIME 2024-2025
OTIS Mock AIME 2024-2025, benchmark released 2024-12-19
98.9%
share of problems solved
standard error ±0.9 points
Strong effort: high, best score across scorers gemini-3.8-flash_high
epoch · otis_mock_aime_2024_2025.csv
2 Sep 2026 Source for Gemini 3.8 Flash OTIS Mock AIME 2024-2025 score (epoch)
No public run log in the data file.
GPQA Diamond
GPQA Diamond, benchmark released 2023-11-20
95.4%
share of questions correct
standard error ±1.4 points
Top tier effort: high, best score across scorers gemini-3.8-flash_high
epoch · gpqa_diamond.csv
2 Sep 2026 Source for Gemini 3.8 Flash GPQA Diamond score (epoch)
No public run log in the data file.
SimpleQA Verified
SimpleQA Verified, benchmark released 2025-09-09
69.7%
share of questions correct
standard error ±1.5 points
Top tier effort: high, best score across scorers gemini-3.8-flash_high
epoch · simpleqa_verified.csv
2 Sep 2026 Source for Gemini 3.8 Flash SimpleQA Verified score (epoch)
No public run log in the data file.
SWE-bench Verified (Epoch runs)Not enough data
FrontierCode (reported by Cognition)
FrontierCode, benchmark released 2026-06-08, aggregation Mean@5
41.2%
mean score over 5 attempts
no uncertainty figure published
Not rated (results use different tools) effort: medium, tool: devin-cli, mean@5 gemini-3.8-flash_medium
epoch · frontiercode_external.csv
not provided Source for Gemini 3.8 Flash FrontierCode (reported by Cognition) score (epoch) · Run details for FrontierCode (reported by Cognition)

Missing evidence

Not enough data for these tests:

  • SWE-bench Verified (Epoch runs)

Missing results are not low scores and do not count against the model.

Where a source gives no public run log, we cannot show the run behind the score. See data sources.

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