Gemini 3.7 Flash vs GPT-5 Mini

Gemini 3.7 Flash leads GPT-5 Mini on coding, 58.0 to 34.2 on the Vibe Coding Index, and GPT-5 Mini costs 1.9x less per output token ($2.00 vs $3.75 per million). Updated October 21, 2026; 75 benchmarks have a result for both.

Replace Gemini 3.7 Flash Replace GPT-5 Mini

Gemini 3.7 FlashGoogle

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

GPT-5 MiniOpenAI

Coding: 34.2
Overall: 43.7
Output $/M: $2.00

vs boards won of 75 shared

Gemini 3.7 Flash or GPT-5 Mini: which to pick

Pick Gemini 3.7 Flash for coding: it scores 58.0 on the Vibe Coding Index against 34.2 for GPT-5 Mini.

Pick GPT-5 Mini for output tokens 1.9x cheaper ($2.00 vs $3.75 per million) and a lower cost per solved task ($0.08 against $1.78).

Model AGemini 3.7 FlashGoogle58.0/1006 independent benchmarks$1.78 per solved task Model BGPT-5 MiniOpenAI34.2/1002 independent benchmarks$0.08 per solved task

753926

Shared results: 75
Gemini 3.7 Flash only: 39
GPT-5 Mini 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
Gemini 3.7 Flash
Gemini 3.7 Flash scores 58.0 against 34.2.
6 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 39.6 against 20.6.
Same board, both measured
Reasoning
GPQA Diamond
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 93.9% against 80.3%.
Same board, both measured
Math
AIME
No call
Only GPT-5 Mini has a AIME result, 91.5%.
Directional only
Knowledge
MMLU Pro
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 90.1% against 82.2%.
Same board, both measured
Web design
Design Arena: websites
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 1304 against 1125.
Same board, both measured
Long documents
Listed context window
Gemini 3.7 Flash
Gemini 3.7 Flash takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5 Mini
Gemini 3.7 Flash $0.0026, GPT-5 Mini $0.0013 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5 Mini
Gemini 3.7 Flash $1.78, GPT-5 Mini $0.08.
Published attempt cost and pass rate

At a glance

Coding index: Gemini 3.7 Flash 58.0 from 6 benchmarks, GPT-5 Mini 34.2 from 2 benchmarks.
Widest category: Reasoning, 26.4 points apart on average over 14 shared benchmarks.
Widest single gap: ARC-AGI-2: Gemini 3.7 Flash 84.6% against GPT-5 Mini 4.4%, 80.1 points apart on a 0 to 100 scale.
Output price: Gemini 3.7 Flash $3.75 against GPT-5 Mini $2.00 per million output tokens, 1.9x apart.
Context window: Gemini 3.7 Flash takes 1.05M tokens, GPT-5 Mini 400K.

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-5 Mini
Coding	6	0
Intelligence	2	0
Reasoning	14	0
Math	6	0
Knowledge	9	2
Web design	8	0
General	28	0
All shared boards	73	2

CodingLike-for-likeSix of these boards feed the Vibe Coding Index60

Coding benchmarks for Gemini 3.7 Flash and GPT-5 Mini
Benchmark	Gemini 3.7 Flash	GPT-5 Mini	Leader
SWE-bench Verified	Not measured	59.8%	Only GPT-5 Mini measured
SWE-bench Multilingual	Not measured	39.7%	Only GPT-5 Mini 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%	86.6%	Gemini 3.7 Flash
SWE-bench Verified (Vals AI)	80.8%	60.8%	Gemini 3.7 Flash
Terminal-Bench 4 (Vals AI)	12.1%	Not measured	Only Gemini 3.7 Flash measured
Vibe Code Bench (Vals AI)	70.4%	14.2%	Gemini 3.7 Flash
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	83.8%	Only GPT-5 Mini measured
SciCode	59.8%	39.0%	Gemini 3.7 Flash
Terminal-Bench Hard (Artificial Analysis run)	Not measured	33.3%	Only GPT-5 Mini measured
Terminal-Bench	Not measured	34.8%	Only GPT-5 Mini 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	52.7%	Only GPT-5 Mini measured
ALE-Bench	904.3	Not measured	Only Gemini 3.7 Flash measured
LMArena Text: coding	1499	1406	Gemini 3.7 Flash
LMArena Text: software and IT	1498	1394	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%	Not measured	Only Gemini 3.7 Flash measured
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-5 Mini
Benchmark	Gemini 3.7 Flash	GPT-5 Mini	Leader
AA Intelligence Index	39.6	20.6	Gemini 3.7 Flash
Epoch Capabilities Index	158.0	145.3	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-5 Mini
Benchmark	Gemini 3.7 Flash	GPT-5 Mini	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	71.1%	Only GPT-5 Mini 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
Vending-Bench 2	Not measured	-$31	Only GPT-5 Mini measured
Berkeley Function Calling Leaderboard (BFCL)	Not measured	55.5%	Only GPT-5 Mini measured
Neither model has a result on the other 20 boards here

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

Reasoning benchmarks for Gemini 3.7 Flash and GPT-5 Mini
Benchmark	Gemini 3.7 Flash	GPT-5 Mini	Leader
GPQA Diamond	93.9%	80.3%	Gemini 3.7 Flash
LiveBench reasoning	87.8%	Not measured	Only Gemini 3.7 Flash measured
MMMU Pro (Vals AI)	89.0%	78.9%	Gemini 3.7 Flash
GPQA Diamond (Artificial Analysis run)	94.5%	82.8%	Gemini 3.7 Flash
Humanity's Last Exam (Artificial Analysis run)	47.9%	21.5%	Gemini 3.7 Flash
GPQA Diamond (Epoch AI run)	94.8%	75.0%	Gemini 3.7 Flash
ARC-AGI	95.5%	54.3%	Gemini 3.7 Flash
ARC-AGI-2	84.6%	4.4%	Gemini 3.7 Flash
Chess puzzles	66.0%	24.0%	Gemini 3.7 Flash
Mystery game puzzles	37.0%	10.0%	Gemini 3.7 Flash
VPCT	Not measured	40.2%	Only GPT-5 Mini measured
LMArena Text: hard prompts	1495	1380	Gemini 3.7 Flash
LMArena Text: expert	1512	1379	Gemini 3.7 Flash
LMArena Text: hard prompts in English	1487	1383	Gemini 3.7 Flash
LMArena Vision: diagrams	1329	1202	Gemini 3.7 Flash
LMArena Vision: homework	1320	1210	Gemini 3.7 Flash
Kagi LLM Benchmark	Not measured	70.3%	Only GPT-5 Mini measured
Neither model has a result on the other 5 boards here

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

Math benchmarks for Gemini 3.7 Flash and GPT-5 Mini
Benchmark	Gemini 3.7 Flash	GPT-5 Mini	Leader
AIME	Not measured	91.5%	Only GPT-5 Mini measured
MATH 500 (Vals AI)	Not measured	94.8%	Only GPT-5 Mini measured
MGSM (Vals AI)	Not measured	92.6%	Only GPT-5 Mini measured
AA Math Index	Not measured	90.7	Only GPT-5 Mini measured
AIME 2025 (Artificial Analysis run)	Not measured	90.7%	Only GPT-5 Mini measured
OTIS Mock AIME	97.2%	86.7%	Gemini 3.7 Flash
FrontierMath, tiers 1 to 3	71.6%	46.7%	Gemini 3.7 Flash
FrontierMath, tier 4	36.6%	12.2%	Gemini 3.7 Flash
ProofBench	58.0%	9.0%	Gemini 3.7 Flash
MATH level 5	Not measured	97.8%	Only GPT-5 Mini measured
LMArena Text: math	1511	1378	Gemini 3.7 Flash
LMArena Text: mathematical work	1502	1380	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 index92

Knowledge benchmarks for Gemini 3.7 Flash and GPT-5 Mini
Benchmark	Gemini 3.7 Flash	GPT-5 Mini	Leader
MMLU Pro	90.1%	82.2%	Gemini 3.7 Flash
LegalBench (Vals AI)	87.3%	81.8%	Gemini 3.7 Flash
CaseLaw (Vals AI)	Not measured	68.5%	Only GPT-5 Mini measured
CorpFin (Vals AI)	Not measured	60.2%	Only GPT-5 Mini measured
TaxEval (Vals AI)	74.7%	75.2%	GPT-5 Mini
MortgageTax (Vals AI)	66.7%	66.9%	GPT-5 Mini
MedQA (Vals AI)	Not measured	96.1%	Only GPT-5 Mini measured
MedCode (Vals AI)	53.4%	43.0%	Gemini 3.7 Flash
MMLU Pro (Artificial Analysis run)	Not measured	83.7%	Only GPT-5 Mini measured
SimpleQA Verified	69.2%	21.6%	Gemini 3.7 Flash
LMArena Text: science	1511	1371	Gemini 3.7 Flash
LMArena Text: medicine and healthcare	1494	1380	Gemini 3.7 Flash
LMArena Text: legal and government	1496	1375	Gemini 3.7 Flash
LMArena Text: business and finance	1479	1358	Gemini 3.7 Flash
FACTS Benchmark Suite	71.2%	38.6%	Gemini 3.7 Flash
Neither model has a result on the other 3 boards here

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

Web design benchmarks for Gemini 3.7 Flash and GPT-5 Mini
Benchmark	Gemini 3.7 Flash	GPT-5 Mini	Leader
Design Arena: websites	1304	1125	Gemini 3.7 Flash
Design Arena: UI components	1299	1106	Gemini 3.7 Flash
Design Arena: game dev	1318	1130	Gemini 3.7 Flash
Design Arena: data viz	1315	1130	Gemini 3.7 Flash
Design Arena: 3D	1308	1037	Gemini 3.7 Flash
Design Arena: all code categories	1308	1115	Gemini 3.7 Flash
Design Arena: SVG	1283	1091	Gemini 3.7 Flash
Design Arena: ASCII art	1276	1125	Gemini 3.7 Flash
LiveBench design	73.2%	Not measured	Only Gemini 3.7 Flash measured

GeneralLike-for-likePublic boards, shown as published, kept out of the index280

General benchmarks for Gemini 3.7 Flash and GPT-5 Mini
Benchmark	Gemini 3.7 Flash	GPT-5 Mini	Leader
LiveBench data analysis	68.0%	Not measured	Only Gemini 3.7 Flash measured
SAGE (Vals AI)	49.2%	43.0%	Gemini 3.7 Flash
MedScribe (Vals AI)	83.9%	80.6%	Gemini 3.7 Flash
IFBench	Not measured	75.4%	Only GPT-5 Mini measured
Long context reasoning (AA-LCR)	83.0%	72.3%	Gemini 3.7 Flash
DTBench	96.8%	80.5%	Gemini 3.7 Flash
LMCA	50.4%	34.2%	Gemini 3.7 Flash
Fiction.LiveBench	Not measured	69.4%	Only GPT-5 Mini measured
Creative writing (Lech Mazur)	Not measured	83.1%	Only GPT-5 Mini measured
Furniture assembly	26.7%	Not measured	Only Gemini 3.7 Flash measured
LMArena Text	1489	1373	Gemini 3.7 Flash
LMArena Text: instruction following	1484	1357	Gemini 3.7 Flash
LMArena Text: creative writing	1488	1325	Gemini 3.7 Flash
LMArena Vision	1314	1202	Gemini 3.7 Flash
LMArena Text: English	1483	1379	Gemini 3.7 Flash
LMArena Text: non-English	1487	1363	Gemini 3.7 Flash
LMArena Text: Chinese	1552	1386	Gemini 3.7 Flash
LMArena Text: German	1506	1366	Gemini 3.7 Flash
LMArena Text: Japanese	1511	1341	Gemini 3.7 Flash
LMArena Text: Korean	1481	1308	Gemini 3.7 Flash
LMArena Text: French	1500	1386	Gemini 3.7 Flash
LMArena Text: Spanish	1495	1354	Gemini 3.7 Flash
LMArena Text: Russian	1515	1362	Gemini 3.7 Flash
LMArena Text: Polish	1503	1378	Gemini 3.7 Flash
LMArena Text: multi-turn	1485	1363	Gemini 3.7 Flash
LMArena Text: longer queries	1492	1355	Gemini 3.7 Flash
LMArena Text: writing and language	1491	1344	Gemini 3.7 Flash
LMArena Text: entertainment and media	1477	1340	Gemini 3.7 Flash
LMArena Vision: English	1309	1216	Gemini 3.7 Flash
LMArena Vision: Chinese	1394	1214	Gemini 3.7 Flash
LMArena Vision: reading text	1322	1199	Gemini 3.7 Flash
LMArena Vision: humor	1299	1205	Gemini 3.7 Flash
LMArena Vision: captioning	Not measured	1217	Only GPT-5 Mini measured
LMArena Vision: recognizing things	Not measured	1226	Only GPT-5 Mini measured
LMArena Vision: creative writing	1312	1181	Gemini 3.7 Flash
Neither model has a result on the other 11 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-5 Mini
Benchmark	Gemini 3.7 Flash	GPT-5 Mini	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-5 Mini$0.0013Fits in one request

GPT-5 Mini costs less

Repository review

50K input + 3K output tokens

Gemini 3.7 Flash$0.049Fits in one request

GPT-5 Mini$0.018Fits in one request

GPT-5 Mini costs less

Agent loop

200K input + 10K output tokens

Gemini 3.7 Flash$0.188Fits in one request

GPT-5 Mini$0.070Fits in one request

GPT-5 Mini 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-5 Mini
Specification	Google Gemini 3.7 Flash	OpenAI GPT-5 Mini
Maker	Google	OpenAI
Weights	No public weights listed	No public weights listed
Released	August 13, 2026	August 7, 2025
Context window	1.05M	400K
Input $/Mtok	$0.75	$0.25
Output $/Mtok	$3.75	$2.00
Coding index	58.0	34.2
Independent measurements behind it	6	2
Cost per solved task	$1.78	$0.08
Source	OpenRouter listing	OpenRouter listing

Questions

Is Gemini 3.7 Flash better than GPT-5 Mini for coding?

Pick Gemini 3.7 Flash for coding: it scores 58.0 on the Vibe Coding Index against 34.2 for GPT-5 Mini. Pick GPT-5 Mini for output tokens 1.9x cheaper ($2.00 vs $3.75 per million) and a lower cost per solved task ($0.08 against $1.78).

Which is cheaper, Gemini 3.7 Flash or GPT-5 Mini?

Gemini 3.7 Flash costs $0.75 per million input tokens and $3.75 per million output tokens; GPT-5 Mini costs $0.25 and $2.00. Per solved SWE-bench Verified task, Gemini 3.7 Flash costs $1.78 and GPT-5 Mini $0.08.

Which has the bigger context window, Gemini 3.7 Flash or GPT-5 Mini?

Gemini 3.7 Flash, with 1.05M tokens against 400K.

How many benchmarks is the Gemini 3.7 Flash vs GPT-5 Mini comparison based on?

75 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-5 Mini's on 2. Where only one model has a result, the page says so instead of naming a winner.

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<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/gemini-3-7-flash-vs-gpt-5-mini">LLMBenchmark.ai, Gemini 3.7 Flash vs GPT-5 Mini</a></p>

Gemini 3.7 Flash or GPT-5 Mini: 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

Compare next