Gemini 3.8 Flash vs GPT-5.4

Updated October 21, 2026. 91 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace Gemini 3.8 Flash Replace GPT-5.4

Gemini 3.8 FlashGoogle

Coding: 70.5
Overall: 40.9
Output $/M: $4.125

GPT-5.4OpenAI

Coding: 37.7
Overall: 39.0
Output $/M: $15.00

vs boards won of 91 shared

Decision reading

Pick Gemini 3.8 Flash for coding: it scores 70.5 on the Vibe Coding Index against 37.7 for GPT-5.4.

Pick GPT-5.4 when a solved task costs $1.02 against $2.74, or when the context window is larger, 1.05M against 1.05M tokens.

Model AGemini 3.8 FlashGoogle70.5/1002 independent benchmarks$2.74 per solved task Model BGPT-5.4OpenAI37.7/1001 independent benchmark$1.02 per solved task

913518

Shared results: 91
Gemini 3.8 Flash only: 35
GPT-5.4 only: 18
Like-for-like categories: 8 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

Coding
Vibe Coding Index, four public coding benchmarks
Gemini 3.8 Flash
Gemini 3.8 Flash scores 70.5 against 37.7.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
Gemini 3.8 Flash
Gemini 3.8 Flash leads, 40.9 against 39.0.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Gemini 3.8 Flash has a AA Agentic Index result, 40.2.
Directional only
Reasoning
GPQA Diamond
Gemini 3.8 Flash
Gemini 3.8 Flash leads, 94.4% against 91.7%.
Same board, both measured
Math
AIME
No call
Only GPT-5.4 has a AIME result, 96.7%.
Directional only
Knowledge
MMLU Pro
Gemini 3.8 Flash
Gemini 3.8 Flash leads, 90.2% against 87.5%.
Same board, both measured
Web design
Design Arena: websites
Gemini 3.8 Flash
Gemini 3.8 Flash leads, 1301 against 1199.
Same board, both measured
General
Vals Index
No call
Only Gemini 3.8 Flash has a Vals Index result, 56.0%.
Directional only
Long documents
Listed context window
GPT-5.4
GPT-5.4 takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemini 3.8 Flash
Gemini 3.8 Flash $0.0029, GPT-5.4 $0.010 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.4
Gemini 3.8 Flash $2.74, GPT-5.4 $1.02.
Published attempt cost and pass rate

At a glance

Coding index: Gemini 3.8 Flash 70.5 from 2 benchmarks, GPT-5.4 37.7 from 1 benchmark.
Widest category: Agentic work, 15.7 points apart on average over 7 shared benchmarks.
Widest single gap: Design Arena: 3D: Gemini 3.8 Flash 1295 against GPT-5.4 1065, 28.8 points apart on a 0 to 100 scale.
Output price: Gemini 3.8 Flash $4.125 against GPT-5.4 $15.00 per million output tokens, 3.6x apart.
Context window: Gemini 3.8 Flash takes 1.05M tokens, GPT-5.4 1.05M.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
Category	Gemini 3.8 Flash	GPT-5.4
Coding	14	1
Intelligence	2	0
Agentic work	6	1
Reasoning	14	0
Math	3	3
Knowledge	9	1
Web design	8	0
General	27	2
All shared boards	83	8

CodingLike-for-likeFour of these boards feed the Vibe Coding Index141

Coding benchmarks for Gemini 3.8 Flash and GPT-5.4
Benchmark	Gemini 3.8 Flash	GPT-5.4	Leader
LiveBench coding	82.2%	Not measured	Only Gemini 3.8 Flash measured
LMArena WebDev	1585	1464	Gemini 3.8 Flash
AA Coding Index	76.3	71.1	Gemini 3.8 Flash
LiveCodeBench (Vals AI)	89.5%	84.1%	Gemini 3.8 Flash
SWE-bench Verified (Vals AI)	80.0%	78.2%	Gemini 3.8 Flash
Terminal-Bench 4 (Vals AI)	19.7%	Not measured	Only Gemini 3.8 Flash measured
Vibe Code Bench (Vals AI)	78.7%	67.4%	Gemini 3.8 Flash
Code migration (Vals AI)	36.5%	35.0%	Gemini 3.8 Flash
ProgramBench (Vals AI)	1.0%	0.5%	Gemini 3.8 Flash
IOI (Vals AI)	56.9%	Not measured	Only Gemini 3.8 Flash measured
Terminal-Bench 2.1 (Vals AI)	81.3%	Not measured	Only Gemini 3.8 Flash measured
SWE-bench Multimodal (Vals AI)	40.8%	Not measured	Only Gemini 3.8 Flash measured
Vibe Code Bench 1-100 (Vals AI)	18.8%	Not measured	Only Gemini 3.8 Flash measured
SkillsBench (Vals AI)	58.0%	51.7%	Gemini 3.8 Flash
CyberBench (Vals AI)	43.8%	Not measured	Only Gemini 3.8 Flash measured
SciCode	56.6%	Not measured	Only Gemini 3.8 Flash measured
Terminal-Bench Hard (Artificial Analysis run)	Not measured	57.6%	Only GPT-5.4 measured
Terminal-Bench	Not measured	81.8%	Only GPT-5.4 measured
FrontierCode	41.2%	Not measured	Only Gemini 3.8 Flash measured
FrontierSWE	19.6%	Not measured	Only Gemini 3.8 Flash measured
GSO-Bench	Not measured	31.4%	Only GPT-5.4 measured
WeirdML	84.8%	77.7%	Gemini 3.8 Flash
MirrorCode	Not measured	15.6%	Only GPT-5.4 measured
ALE-Bench	1269.7	1607.0	GPT-5.4
LMArena Text: coding	1508	1496	Gemini 3.8 Flash
LMArena Text: software and IT	1507	1491	Gemini 3.8 Flash
LMArena WebDev: React	1577	1454	Gemini 3.8 Flash
LMArena WebDev: HTML	1616	1478	Gemini 3.8 Flash
LMArena WebDev: from an image	1593	1436	Gemini 3.8 Flash
Neither model has a result on the other 9 boards here

IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20

Intelligence benchmarks for Gemini 3.8 Flash and GPT-5.4
Benchmark	Gemini 3.8 Flash	GPT-5.4	Leader
AA Intelligence Index	40.9	39.0	Gemini 3.8 Flash
Epoch Capabilities Index	157.4	157.3	Gemini 3.8 Flash

Agentic workLike-for-likePublic boards, shown as published, kept out of the index61

Agentic work benchmarks for Gemini 3.8 Flash and GPT-5.4
Benchmark	Gemini 3.8 Flash	GPT-5.4	Leader
AA Agentic Index	40.2	Not measured	Only Gemini 3.8 Flash measured
Design Arena: agents building web apps	1238	1059	Gemini 3.8 Flash
Design Arena: agents building full-stack apps	1244	1017	Gemini 3.8 Flash
Design Arena: agents building mobile apps	1236	1087	Gemini 3.8 Flash
Design Arena: agents building Android apps	1078	1037	Gemini 3.8 Flash
Design Arena: agents building Godot games	1273	1135	Gemini 3.8 Flash
Design Arena: PowerPoint slides	1178	Not measured	Only Gemini 3.8 Flash measured
Design Arena: spreadsheets	1052	Not measured	Only Gemini 3.8 Flash measured
LiveBench research	79.1%	Not measured	Only Gemini 3.8 Flash measured
LiveBench browser	64.0%	Not measured	Only Gemini 3.8 Flash measured
LiveBench long agentic	30.1%	Not measured	Only Gemini 3.8 Flash measured
SREBench (Vals AI)	11.1%	Not measured	Only Gemini 3.8 Flash measured
Finance Agent (Vals AI)	61.4%	Not measured	Only Gemini 3.8 Flash measured
Tax Agent Bench (Vals AI)	32.1%	Not measured	Only Gemini 3.8 Flash measured
Legal Research Bench (Vals AI)	38.9%	Not measured	Only Gemini 3.8 Flash measured
Harvey's Legal Agent Benchmark (Vals AI)	12.1%	Not measured	Only Gemini 3.8 Flash measured
Excel Modeling Benchmark (Vals AI)	72.2%	Not measured	Only Gemini 3.8 Flash measured
BioMysteryBench (Vals AI)	62.2%	Not measured	Only Gemini 3.8 Flash measured
Terminal-Bench-Science (Vals AI)	8.6%	Not measured	Only Gemini 3.8 Flash measured
Vals Science Index	35.8%	Not measured	Only Gemini 3.8 Flash measured
Time Horizon Index (Vals AI)	18.8%	Not measured	Only Gemini 3.8 Flash measured
CUA-bench (Vals AI)	4.2%	Not measured	Only Gemini 3.8 Flash measured
τ²-Bench Telecom	Not measured	87.1%	Only GPT-5.4 measured
APEX-Agents	64.3%	52.4%	Gemini 3.8 Flash
METR time horizons	Not measured	74.3%	Only GPT-5.4 measured
Remote Labor Index	5.8%	Not measured	Only Gemini 3.8 Flash measured
DeepResearch Bench	Not measured	35.1%	Only GPT-5.4 measured
PostTrainBench	Not measured	19.0%	Only GPT-5.4 measured
Vending-Bench 2	$5,094	$6,144	GPT-5.4
Neither model has a result on the other 10 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index140

Reasoning benchmarks for Gemini 3.8 Flash and GPT-5.4
Benchmark	Gemini 3.8 Flash	GPT-5.4	Leader
GPQA Diamond	94.4%	91.7%	Gemini 3.8 Flash
LiveBench reasoning	89.3%	Not measured	Only Gemini 3.8 Flash measured
MysteryMechanism (Vals AI)	36.5%	Not measured	Only Gemini 3.8 Flash measured
MMMU Pro (Vals AI)	89.1%	87.5%	Gemini 3.8 Flash
GPQA Diamond (Artificial Analysis run)	95.3%	92.0%	Gemini 3.8 Flash
Humanity's Last Exam (Artificial Analysis run)	47.8%	43.7%	Gemini 3.8 Flash
GPQA Diamond (Epoch AI run)	95.4%	93.3%	Gemini 3.8 Flash
ARC-AGI	98.5%	93.7%	Gemini 3.8 Flash
ARC-AGI-2	89.2%	74.0%	Gemini 3.8 Flash
Chess puzzles	58.0%	48.0%	Gemini 3.8 Flash
Mystery game puzzles	47.0%	37.0%	Gemini 3.8 Flash
EBR-bench	Not measured	25.4%	Only GPT-5.4 measured
LMArena Text: hard prompts	1509	1485	Gemini 3.8 Flash
LMArena Text: expert	1526	1507	Gemini 3.8 Flash
LMArena Text: hard prompts in English	1504	1479	Gemini 3.8 Flash
LMArena Vision: diagrams	1345	1327	Gemini 3.8 Flash
LMArena Vision: homework	1351	1336	Gemini 3.8 Flash
Neither model has a result on the other 4 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index33

Math benchmarks for Gemini 3.8 Flash and GPT-5.4
Benchmark	Gemini 3.8 Flash	GPT-5.4	Leader
AIME	Not measured	96.7%	Only GPT-5.4 measured
OTIS Mock AIME	98.9%	97.8%	Gemini 3.8 Flash
FrontierMath, tiers 1 to 3	68.4%	78.6%	GPT-5.4
FrontierMath, tier 4	22.0%	49.0%	GPT-5.4
ProofBench	48.0%	56.0%	GPT-5.4
LMArena Text: math	1532	1488	Gemini 3.8 Flash
LMArena Text: mathematical work	1519	1497	Gemini 3.8 Flash
Neither model has a result on the other 6 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index91

Knowledge benchmarks for Gemini 3.8 Flash and GPT-5.4
Benchmark	Gemini 3.8 Flash	GPT-5.4	Leader
MMLU Pro	90.2%	87.5%	Gemini 3.8 Flash
LegalBench (Vals AI)	87.0%	86.0%	Gemini 3.8 Flash
CaseLaw (Vals AI)	Not measured	63.8%	Only GPT-5.4 measured
CorpFin (Vals AI)	Not measured	65.3%	Only GPT-5.4 measured
TaxEval (Vals AI)	74.5%	74.0%	Gemini 3.8 Flash
MortgageTax (Vals AI)	65.3%	68.3%	GPT-5.4
MedQA (Vals AI)	Not measured	96.1%	Only GPT-5.4 measured
MedCode (Vals AI)	48.1%	41.3%	Gemini 3.8 Flash
SimpleQA Verified	69.7%	45.1%	Gemini 3.8 Flash
LMArena Text: science	1523	1479	Gemini 3.8 Flash
LMArena Text: medicine and healthcare	1508	1458	Gemini 3.8 Flash
LMArena Text: legal and government	1497	1486	Gemini 3.8 Flash
LMArena Text: business and finance	1479	1474	Gemini 3.8 Flash
Neither model has a result on the other 4 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index80

Web design benchmarks for Gemini 3.8 Flash and GPT-5.4
Benchmark	Gemini 3.8 Flash	GPT-5.4	Leader
Design Arena: websites	1301	1199	Gemini 3.8 Flash
Design Arena: UI components	1318	1215	Gemini 3.8 Flash
Design Arena: game dev	1305	1195	Gemini 3.8 Flash
Design Arena: data viz	1269	1196	Gemini 3.8 Flash
Design Arena: 3D	1295	1065	Gemini 3.8 Flash
Design Arena: all code categories	1304	1180	Gemini 3.8 Flash
Design Arena: SVG	1295	1141	Gemini 3.8 Flash
Design Arena: ASCII art	1264	1188	Gemini 3.8 Flash
LiveBench design	72.7%	Not measured	Only Gemini 3.8 Flash measured

GeneralLike-for-likePublic boards, shown as published, kept out of the index272

General benchmarks for Gemini 3.8 Flash and GPT-5.4
Benchmark	Gemini 3.8 Flash	GPT-5.4	Leader
Vals Index	56.0%	Not measured	Only Gemini 3.8 Flash measured
LiveBench data analysis	54.0%	Not measured	Only Gemini 3.8 Flash measured
Public Benefits Bench (Vals AI)	65.3%	Not measured	Only Gemini 3.8 Flash measured
SAGE (Vals AI)	35.1%	43.3%	GPT-5.4
MedScribe (Vals AI)	84.5%	77.5%	Gemini 3.8 Flash
IFBench	Not measured	73.9%	Only GPT-5.4 measured
Long context reasoning (AA-LCR)	84.0%	82.0%	Gemini 3.8 Flash
DTBench	95.7%	94.4%	Gemini 3.8 Flash
LMCA	52.9%	52.0%	Gemini 3.8 Flash
CL-bench	Not measured	27.9%	Only GPT-5.4 measured
Surface Evolver Bench	76.9%	Not measured	Only Gemini 3.8 Flash measured
Furniture assembly	31.7%	37.5%	GPT-5.4
CL-bench Life	Not measured	21.7%	Only GPT-5.4 measured
LMArena Text	1499	1469	Gemini 3.8 Flash
LMArena Text: instruction following	1492	1469	Gemini 3.8 Flash
LMArena Text: creative writing	1488	1439	Gemini 3.8 Flash
LMArena Vision	1315	1303	Gemini 3.8 Flash
LMArena Text: English	1498	1467	Gemini 3.8 Flash
LMArena Text: non-English	1491	1465	Gemini 3.8 Flash
LMArena Text: Chinese	1553	1520	Gemini 3.8 Flash
LMArena Text: German	1501	1472	Gemini 3.8 Flash
LMArena Text: Japanese	1507	1485	Gemini 3.8 Flash
LMArena Text: Korean	1464	1449	Gemini 3.8 Flash
LMArena Text: French	1496	1493	Gemini 3.8 Flash
LMArena Text: Spanish	1487	1452	Gemini 3.8 Flash
LMArena Text: Russian	1514	1480	Gemini 3.8 Flash
LMArena Text: Polish	1497	1472	Gemini 3.8 Flash
LMArena Text: multi-turn	1503	1482	Gemini 3.8 Flash
LMArena Text: longer queries	1507	1473	Gemini 3.8 Flash
LMArena Text: writing and language	1494	1458	Gemini 3.8 Flash
LMArena Text: entertainment and media	1480	1438	Gemini 3.8 Flash
LMArena Vision: English	1318	1301	Gemini 3.8 Flash
LMArena Vision: Chinese	1416	1354	Gemini 3.8 Flash
LMArena Vision: reading text	1333	1312	Gemini 3.8 Flash
LMArena Vision: humor	1321	1284	Gemini 3.8 Flash
LMArena Vision: recognizing things	Not measured	1251	Only GPT-5.4 measured
LMArena Vision: creative writing	1299	1283	Gemini 3.8 Flash
LMArena Document	Not measured	1471	Only GPT-5.4 measured
Neither model has a result on the other 8 boards here

Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared

Image generation benchmarks for Gemini 3.8 Flash and GPT-5.4
Benchmark	Gemini 3.8 Flash	GPT-5.4	Leader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

Gemini 3.8 Flash$0.0029Fits in one request

GPT-5.4$0.010Fits in one request

Gemini 3.8 Flash costs less

Repository review

50K input + 3K output tokens

Gemini 3.8 Flash$0.054Fits in one request

GPT-5.4$0.170Fits in one request

Gemini 3.8 Flash costs less

Agent loop

200K input + 10K output tokens

Gemini 3.8 Flash$0.206Fits in one request

GPT-5.4$0.650Fits in one request

Gemini 3.8 Flash costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of Gemini 3.8 Flash and GPT-5.4
Specification	Google Gemini 3.8 Flash	OpenAI GPT-5.4
Maker	Google	OpenAI
Weights	No public weights listed	No public weights listed
Released	September 2, 2026	March 5, 2026
Context window	1.05M	1.05M
Input $/Mtok	$0.825	$2.50
Output $/Mtok	$4.125	$15.00
Coding index	70.5	37.7
Independent measurements behind it	2	1
Cost per solved task	$2.74	$1.02
Source	OpenRouter listing	OpenRouter listing

Questions

Is Gemini 3.8 Flash better than GPT-5.4 for coding?

Pick Gemini 3.8 Flash for coding: it scores 70.5 on the Vibe Coding Index against 37.7 for GPT-5.4. Pick GPT-5.4 when a solved task costs $1.02 against $2.74, or when the context window is larger, 1.05M against 1.05M tokens.

Which is cheaper, Gemini 3.8 Flash or GPT-5.4?

Gemini 3.8 Flash costs $0.825 per million input tokens and $4.125 per million output tokens; GPT-5.4 costs $2.50 and $15.00. Per solved SWE-bench Verified task, Gemini 3.8 Flash costs $2.74 and GPT-5.4 $1.02.

Which has the bigger context window, Gemini 3.8 Flash or GPT-5.4?

GPT-5.4, with 1.05M tokens against 1.05M.

How many benchmarks is the Gemini 3.8 Flash vs GPT-5.4 comparison based on?

91 benchmarks have a result for both models as of October 21, 2026. Gemini 3.8 Flash's coding index rests on 2 independent benchmarks and GPT-5.4's on 1. Where only one model has a result, the page says so instead of naming a winner.

Decision reading

Which one for your work

Coding

Intelligence

Agentic work

Reasoning

Math

Knowledge

Web design

General

Long documents

Everyday cost

Cost per solved task

At a glance

Benchmark by benchmark

What each workload costs

Chat turn

Repository review

Agent loop

Specification differences

Questions

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