Gemini 3.7 Flash vs GPT-4.1

Gemini 3.7 Flash leads GPT-4.1 on the Artificial Analysis Intelligence Index, 39.6 to 12.7, and Gemini 3.7 Flash costs 2.1x less per output token ($3.75 vs $8.00 per million). Updated October 21, 2026; 66 benchmarks have a result for both.

Replace Gemini 3.7 Flash Replace GPT-4.1

Gemini 3.7 FlashGoogle

Coding: 58.0
Overall: 65.7
Output $/M: $3.75

GPT-4.1OpenAI

Coding: No data
Overall: 36.5
Output $/M: $8.00

vs boards won of 66 shared

Gemini 3.7 Flash or GPT-4.1: which to pick

Only Gemini 3.7 Flash has a coding score, 58.0 on the Vibe Coding Index; GPT-4.1 has none, so the two cannot be ranked on coding.

Pick GPT-4.1 for a lower cost per solved task ($0.37 against $1.78) and work measured by TaxEval (Vals AI), where it leads.

Model AGemini 3.7 FlashGoogle58.0/1006 independent benchmarks$1.78 per solved task Model BGPT-4.1OpenAINot benchmarkedNo coding benchmark$0.37 per solved task

664826

Shared results: 66
Gemini 3.7 Flash only: 48
GPT-4.1 only: 26
Like-for-like categories: 7 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

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, six public coding benchmarks
No call
Only Gemini 3.7 Flash has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 39.6 against 12.7.
Same board, both measured
Reasoning
GPQA Diamond
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 93.9% against 65.4%.
Same board, both measured
Math
AIME
No call
Only GPT-4.1 has a AIME result, 39.6%.
Directional only
Knowledge
MMLU Pro
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 90.1% against 80.5%.
Same board, both measured
Web design
Design Arena: websites
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 1304 against 1039.
Same board, both measured
Long documents
Listed context window
Gemini 3.7 Flash
Gemini 3.7 Flash takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemini 3.7 Flash
Gemini 3.7 Flash $0.0026, GPT-4.1 $0.0060 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-4.1
Gemini 3.7 Flash $1.78, GPT-4.1 $0.37.
Published attempt cost and pass rate

At a glance

Coding index: Gemini 3.7 Flash 58.0 from 6 benchmarks, GPT-4.1 not benchmarked.
Widest category: Math, 43.0 points apart on average over 4 shared benchmarks.
Widest single gap: ARC-AGI: Gemini 3.7 Flash 95.5% against GPT-4.1 5.5%, 90.0 points apart on a 0 to 100 scale.
Output price: Gemini 3.7 Flash $3.75 against GPT-4.1 $8.00 per million output tokens, 2.1x apart.
Context window: Gemini 3.7 Flash takes 1.05M tokens, GPT-4.1 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 3.7 Flash	GPT-4.1
Coding	5	0
Intelligence	2	0
Reasoning	13	0
Math	4	0
Knowledge	9	1
Web design	6	0
General	26	0
All shared boards	65	1

CodingLike-for-likeSix of these boards feed the Vibe Coding Index50

Coding benchmarks for Gemini 3.7 Flash and GPT-4.1
Benchmark	Gemini 3.7 Flash	GPT-4.1	Leader
Aider polyglot	Not measured	52.4%	Only GPT-4.1 measured
SWE-bench Verified	Not measured	39.6%	Only GPT-4.1 measured
SWE-bench Multimodal	Not measured	31.1%	Only GPT-4.1 measured
LiveBench coding	80.5%	Not measured	Only Gemini 3.7 Flash measured
LMArena WebDev	1591	Not measured	Only Gemini 3.7 Flash measured
AA Coding Index	71.5	Not measured	Only Gemini 3.7 Flash measured
LiveCodeBench (Vals AI)	88.7%	54.7%	Gemini 3.7 Flash
SWE-bench Verified (Vals AI)	80.8%	Not measured	Only Gemini 3.7 Flash measured
Terminal-Bench 4 (Vals AI)	12.1%	Not measured	Only Gemini 3.7 Flash measured
Vibe Code Bench (Vals AI)	70.4%	Not measured	Only Gemini 3.7 Flash measured
Code migration (Vals AI)	34.8%	Not measured	Only Gemini 3.7 Flash measured
ProgramBench (Vals AI)	0.0%	Not measured	Only Gemini 3.7 Flash measured
IOI (Vals AI)	67.8%	Not measured	Only Gemini 3.7 Flash measured
Terminal-Bench 2.1 (Vals AI)	77.5%	Not measured	Only Gemini 3.7 Flash measured
SkillsBench (Vals AI)	65.9%	Not measured	Only Gemini 3.7 Flash measured
CyberBench (Vals AI)	43.8%	Not measured	Only Gemini 3.7 Flash measured
LiveCodeBench (Artificial Analysis run)	Not measured	45.7%	Only GPT-4.1 measured
SciCode	59.8%	Not measured	Only Gemini 3.7 Flash measured
Terminal-Bench Hard (Artificial Analysis run)	Not measured	13.6%	Only GPT-4.1 measured
FrontierCode	43.6%	Not measured	Only Gemini 3.7 Flash measured
FrontierSWE	20.3%	Not measured	Only Gemini 3.7 Flash measured
WeirdML	Not measured	39.0%	Only GPT-4.1 measured
ALE-Bench	904.3	558.1	Gemini 3.7 Flash
LMArena Text: coding	1499	1390	Gemini 3.7 Flash
LMArena Text: software and IT	1498	1391	Gemini 3.7 Flash
LMArena WebDev: React	1587	Not measured	Only Gemini 3.7 Flash measured
LMArena WebDev: HTML	1593	Not measured	Only Gemini 3.7 Flash measured
LMArena WebDev: from an image	1599	Not measured	Only Gemini 3.7 Flash measured
SciCode	13.8%	4.6%	Gemini 3.7 Flash
Neither model has a result on the other 10 boards here

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

Intelligence benchmarks for Gemini 3.7 Flash and GPT-4.1
Benchmark	Gemini 3.7 Flash	GPT-4.1	Leader
AA Intelligence Index	39.6	12.7	Gemini 3.7 Flash
Epoch Capabilities Index	158.0	136.7	Gemini 3.7 Flash

Agentic workDirectional onlyPublic boards, shown as published, kept out of the index21 boards, none shared

Agentic work benchmarks for Gemini 3.7 Flash and GPT-4.1
Benchmark	Gemini 3.7 Flash	GPT-4.1	Leader
Design Arena: agents building web apps	1223	Not measured	Only Gemini 3.7 Flash measured
Design Arena: agents building full-stack apps	1208	Not measured	Only Gemini 3.7 Flash measured
Design Arena: agents building mobile apps	1230	Not measured	Only Gemini 3.7 Flash measured
Design Arena: agents building Android apps	1242	Not measured	Only Gemini 3.7 Flash measured
Design Arena: agents building Godot games	1264	Not measured	Only Gemini 3.7 Flash measured
Design Arena: agents building games	1235	Not measured	Only Gemini 3.7 Flash measured
Design Arena: spreadsheets	1053	Not measured	Only Gemini 3.7 Flash measured
LiveBench research	76.5%	Not measured	Only Gemini 3.7 Flash measured
LiveBench browser	56.0%	Not measured	Only Gemini 3.7 Flash measured
LiveBench long agentic	28.3%	Not measured	Only Gemini 3.7 Flash measured
SREBench (Vals AI)	4.6%	Not measured	Only Gemini 3.7 Flash measured
Finance Agent (Vals AI)	59.0%	Not measured	Only Gemini 3.7 Flash measured
Tax Agent Bench (Vals AI)	22.8%	Not measured	Only Gemini 3.7 Flash measured
Legal Research Bench (Vals AI)	34.6%	Not measured	Only Gemini 3.7 Flash measured
Excel Modeling Benchmark (Vals AI)	71.3%	Not measured	Only Gemini 3.7 Flash measured
Terminal-Bench-Science (Vals AI)	7.1%	Not measured	Only Gemini 3.7 Flash measured
τ²-Bench Telecom	Not measured	47.1%	Only GPT-4.1 measured
APEX-Agents	67.8%	Not measured	Only Gemini 3.7 Flash measured
Remote Labor Index	5.0%	Not measured	Only Gemini 3.7 Flash measured
Berkeley Function Calling Leaderboard (BFCL)	Not measured	54.0%	Only GPT-4.1 measured
BrowseComp-Plus	Not measured	36.4%	Only GPT-4.1 measured
Neither model has a result on the other 20 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index130

Reasoning benchmarks for Gemini 3.7 Flash and GPT-4.1
Benchmark	Gemini 3.7 Flash	GPT-4.1	Leader
GPQA Diamond	93.9%	65.4%	Gemini 3.7 Flash
LiveBench reasoning	87.8%	Not measured	Only Gemini 3.7 Flash measured
MMMU Pro (Vals AI)	89.0%	72.4%	Gemini 3.7 Flash
GPQA Diamond (Artificial Analysis run)	94.5%	66.6%	Gemini 3.7 Flash
Humanity's Last Exam (Artificial Analysis run)	47.9%	4.2%	Gemini 3.7 Flash
GPQA Diamond (Epoch AI run)	94.8%	66.9%	Gemini 3.7 Flash
ARC-AGI	95.5%	5.5%	Gemini 3.7 Flash
ARC-AGI-2	84.6%	0.4%	Gemini 3.7 Flash
SimpleBench	Not measured	27.0%	Only GPT-4.1 measured
Chess puzzles	66.0%	3.0%	Gemini 3.7 Flash
Mystery game puzzles	37.0%	Not measured	Only Gemini 3.7 Flash measured
LMArena Text: hard prompts	1495	1384	Gemini 3.7 Flash
LMArena Text: expert	1512	1364	Gemini 3.7 Flash
LMArena Text: hard prompts in English	1487	1393	Gemini 3.7 Flash
LMArena Vision: diagrams	1329	1215	Gemini 3.7 Flash
LMArena Vision: homework	1320	1248	Gemini 3.7 Flash
Kagi LLM Benchmark	Not measured	52.3%	Only GPT-4.1 measured
Neither model has a result on the other 5 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index40

Math benchmarks for Gemini 3.7 Flash and GPT-4.1
Benchmark	Gemini 3.7 Flash	GPT-4.1	Leader
AIME	Not measured	39.6%	Only GPT-4.1 measured
MATH 500 (Vals AI)	Not measured	87.2%	Only GPT-4.1 measured
MGSM (Vals AI)	Not measured	87.7%	Only GPT-4.1 measured
AA Math Index	Not measured	34.7	Only GPT-4.1 measured
AIME 2025 (Artificial Analysis run)	Not measured	34.7%	Only GPT-4.1 measured
OTIS Mock AIME	97.2%	38.3%	Gemini 3.7 Flash
FrontierMath, tiers 1 to 3	71.6%	6.0%	Gemini 3.7 Flash
FrontierMath, tier 4	36.6%	Not measured	Only Gemini 3.7 Flash measured
ProofBench	58.0%	Not measured	Only Gemini 3.7 Flash measured
MATH level 5	Not measured	83.0%	Only GPT-4.1 measured
LMArena Text: math	1511	1369	Gemini 3.7 Flash
LMArena Text: mathematical work	1502	1360	Gemini 3.7 Flash
Neither model has a result on the other 1 board here

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

Knowledge benchmarks for Gemini 3.7 Flash and GPT-4.1
Benchmark	Gemini 3.7 Flash	GPT-4.1	Leader
MMLU Pro	90.1%	80.5%	Gemini 3.7 Flash
LegalBench (Vals AI)	87.3%	83.1%	Gemini 3.7 Flash
CaseLaw (Vals AI)	Not measured	69.9%	Only GPT-4.1 measured
CorpFin (Vals AI)	Not measured	63.0%	Only GPT-4.1 measured
TaxEval (Vals AI)	74.7%	75.1%	GPT-4.1
MortgageTax (Vals AI)	66.7%	65.9%	Gemini 3.7 Flash
MedQA (Vals AI)	Not measured	91.2%	Only GPT-4.1 measured
MedCode (Vals AI)	53.4%	Not measured	Only Gemini 3.7 Flash measured
MMLU Pro (Artificial Analysis run)	Not measured	80.6%	Only GPT-4.1 measured
SimpleQA Verified	69.2%	31.1%	Gemini 3.7 Flash
GeoBench	Not measured	72.0%	Only GPT-4.1 measured
LMArena Text: science	1511	1382	Gemini 3.7 Flash
LMArena Text: medicine and healthcare	1494	1379	Gemini 3.7 Flash
LMArena Text: legal and government	1496	1399	Gemini 3.7 Flash
LMArena Text: business and finance	1479	1369	Gemini 3.7 Flash
FACTS Benchmark Suite	71.2%	45.8%	Gemini 3.7 Flash
Neither model has a result on the other 2 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index60

Web design benchmarks for Gemini 3.7 Flash and GPT-4.1
Benchmark	Gemini 3.7 Flash	GPT-4.1	Leader
Design Arena: websites	1304	1039	Gemini 3.7 Flash
Design Arena: UI components	1299	1000	Gemini 3.7 Flash
Design Arena: game dev	1318	1080	Gemini 3.7 Flash
Design Arena: data viz	1315	1104	Gemini 3.7 Flash
Design Arena: 3D	1308	850	Gemini 3.7 Flash
Design Arena: all code categories	1308	1026	Gemini 3.7 Flash
Design Arena: SVG	1283	Not measured	Only Gemini 3.7 Flash measured
Design Arena: ASCII art	1276	Not measured	Only Gemini 3.7 Flash measured
LiveBench design	73.2%	Not measured	Only Gemini 3.7 Flash measured

GeneralLike-for-likePublic boards, shown as published, kept out of the index260

General benchmarks for Gemini 3.7 Flash and GPT-4.1
Benchmark	Gemini 3.7 Flash	GPT-4.1	Leader
LiveBench data analysis	68.0%	Not measured	Only Gemini 3.7 Flash measured
SAGE (Vals AI)	49.2%	Not measured	Only Gemini 3.7 Flash measured
MedScribe (Vals AI)	83.9%	Not measured	Only Gemini 3.7 Flash measured
IFBench	Not measured	43.0%	Only GPT-4.1 measured
Long context reasoning (AA-LCR)	83.0%	68.3%	Gemini 3.7 Flash
DTBench	96.8%	68.3%	Gemini 3.7 Flash
LMCA	50.4%	25.6%	Gemini 3.7 Flash
Fiction.LiveBench	Not measured	63.9%	Only GPT-4.1 measured
Furniture assembly	26.7%	Not measured	Only Gemini 3.7 Flash measured
LMArena Text	1489	1383	Gemini 3.7 Flash
LMArena Text: instruction following	1484	1367	Gemini 3.7 Flash
LMArena Text: creative writing	1488	1363	Gemini 3.7 Flash
LMArena Vision	1314	1210	Gemini 3.7 Flash
LMArena Text: English	1483	1392	Gemini 3.7 Flash
LMArena Text: non-English	1487	1370	Gemini 3.7 Flash
LMArena Text: Chinese	1552	1382	Gemini 3.7 Flash
LMArena Text: German	1506	1381	Gemini 3.7 Flash
LMArena Text: Japanese	1511	1319	Gemini 3.7 Flash
LMArena Text: Korean	1481	1338	Gemini 3.7 Flash
LMArena Text: French	1500	1382	Gemini 3.7 Flash
LMArena Text: Spanish	1495	1376	Gemini 3.7 Flash
LMArena Text: Russian	1515	1377	Gemini 3.7 Flash
LMArena Text: Polish	1503	1389	Gemini 3.7 Flash
LMArena Text: multi-turn	1485	1397	Gemini 3.7 Flash
LMArena Text: longer queries	1492	1385	Gemini 3.7 Flash
LMArena Text: writing and language	1491	1371	Gemini 3.7 Flash
LMArena Text: entertainment and media	1477	1365	Gemini 3.7 Flash
LMArena Vision: English	1309	1209	Gemini 3.7 Flash
LMArena Vision: Chinese	1394	1226	Gemini 3.7 Flash
LMArena Vision: reading text	1322	1218	Gemini 3.7 Flash
LMArena Vision: humor	1299	1196	Gemini 3.7 Flash
LMArena Vision: captioning	Not measured	1207	Only GPT-4.1 measured
LMArena Vision: recognizing things	Not measured	1208	Only GPT-4.1 measured
LMArena Vision: creative writing	1312	1210	Gemini 3.7 Flash
Neither model has a result on the other 12 boards here

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

Image generation benchmarks for Gemini 3.7 Flash and GPT-4.1
Benchmark	Gemini 3.7 Flash	GPT-4.1	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 3.7 Flash$0.0026Fits in one request

GPT-4.1$0.0060Fits in one request

Gemini 3.7 Flash costs less

Repository review

50K input + 3K output tokens

Gemini 3.7 Flash$0.049Fits in one request

GPT-4.1$0.124Fits in one request

Gemini 3.7 Flash costs less

Agent loop

200K input + 10K output tokens

Gemini 3.7 Flash$0.188Fits in one request

GPT-4.1$0.480Fits in one request

Gemini 3.7 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 3.7 Flash and GPT-4.1
Specification	Google Gemini 3.7 Flash	OpenAI GPT-4.1
Maker	Google	OpenAI
Weights	No public weights listed	No public weights listed
Released	August 13, 2026	April 14, 2025
Context window	1.05M	1.05M
Input $/Mtok	$0.75	$2.00
Output $/Mtok	$3.75	$8.00
Coding index	58.0	Not benchmarked
Independent measurements behind it	6	0
Cost per solved task	$1.78	$0.37
Source	OpenRouter listing	OpenRouter listing

Questions

Is Gemini 3.7 Flash better than GPT-4.1 for coding?

Only Gemini 3.7 Flash has a coding score, 58.0 on the Vibe Coding Index; GPT-4.1 has none, so the two cannot be ranked on coding. Pick GPT-4.1 for a lower cost per solved task ($0.37 against $1.78) and work measured by TaxEval (Vals AI), where it leads.

Which is cheaper, Gemini 3.7 Flash or GPT-4.1?

Gemini 3.7 Flash costs $0.75 per million input tokens and $3.75 per million output tokens; GPT-4.1 costs $2.00 and $8.00. Per solved SWE-bench Verified task, Gemini 3.7 Flash costs $1.78 and GPT-4.1 $0.37.

Which has the bigger context window, Gemini 3.7 Flash or GPT-4.1?

Gemini 3.7 Flash, with 1.05M tokens against 1.05M.

How many benchmarks is the Gemini 3.7 Flash vs GPT-4.1 comparison based on?

66 benchmarks have a result for both models as of October 21, 2026. Gemini 3.7 Flash's coding index rests on 6 independent benchmarks and GPT-4.1's on 0. Where only one model has a result, the page says so instead of naming a winner.

Gemini 3.7 Flash or GPT-4.1: which to pick

Which one for your work

Coding

Intelligence

Reasoning

Math

Knowledge

Web design

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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