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Gemini 3.7 Flash vs GPT-4o-mini

Updated October 21, 2026. 14 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Gemini 3.7 FlashGoogle
Coding
58.0
Overall
39.6
Output $/M
$3.75
14
GPT-4o-miniOpenAI
Coding
No data
Overall
6.7
Output $/M
$0.60
0
vs boards won of 14 shared

Decision reading

Only Gemini 3.7 Flash has a coding score, 58.0 on the Vibe Coding Index; GPT-4o-mini has none, so the two cannot be ranked on coding.

GPT-4o-mini has the edge where output tokens cost 6.3x less.

Shared results
14
Gemini 3.7 Flash only
98
GPT-4o-mini only
13
Like-for-like categories
5 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.

At a glance

Coding index
Gemini 3.7 Flash 58.0 from 6 benchmarks, GPT-4o-mini not benchmarked.
Widest category
Coding, 61.2 points apart on average over 2 shared benchmarks.
Widest single gap
ARC-AGI-2: Gemini 3.7 Flash 84.6% against GPT-4o-mini 0.0%, 84.6 points apart on a 0 to 100 scale.
Output price
Gemini 3.7 Flash $3.75 against GPT-4o-mini $0.60 per million output tokens, 6.3x apart.
Context window
Gemini 3.7 Flash takes 1.05M tokens, GPT-4o-mini 128K.

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
CategoryGemini 3.7 FlashBarsGPT-4o-miniTies
Coding20
Intelligence20
Reasoning50
Knowledge30
General20
All shared boards140
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
Coding benchmarks for Gemini 3.7 Flash and GPT-4o-mini
BenchmarkGemini 3.7 FlashGPT-4o-miniLeader
LiveBench coding80.5%Not measuredOnly Gemini 3.7 Flash measured
LMArena WebDev1591Not measuredOnly Gemini 3.7 Flash measured
AA Coding Index71.511.4Gemini 3.7 Flash
LiveCodeBench (Vals AI)88.7%26.4%Gemini 3.7 Flash
SWE-bench Verified (Vals AI)80.8%Not measuredOnly Gemini 3.7 Flash measured
Terminal-Bench 4 (Vals AI)12.1%Not measuredOnly Gemini 3.7 Flash measured
Vibe Code Bench (Vals AI)70.4%Not measuredOnly Gemini 3.7 Flash measured
Code migration (Vals AI)34.8%Not measuredOnly Gemini 3.7 Flash measured
ProgramBench (Vals AI)0.0%Not measuredOnly Gemini 3.7 Flash measured
IOI (Vals AI)67.8%Not measuredOnly Gemini 3.7 Flash measured
Terminal-Bench 2.1 (Vals AI)77.5%Not measuredOnly Gemini 3.7 Flash measured
SkillsBench (Vals AI)65.9%Not measuredOnly Gemini 3.7 Flash measured
CyberBench (Vals AI)43.8%Not measuredOnly Gemini 3.7 Flash measured
LiveCodeBench (Artificial Analysis run)Not measured23.4%Only GPT-4o-mini measured
SciCode59.8%Not measuredOnly Gemini 3.7 Flash measured
FrontierCode43.6%Not measuredOnly Gemini 3.7 Flash measured
FrontierSWE20.3%Not measuredOnly Gemini 3.7 Flash measured
ALE-Bench904.3Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: coding1499Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: software and IT1498Not measuredOnly Gemini 3.7 Flash measured
LMArena WebDev: React1587Not measuredOnly Gemini 3.7 Flash measured
LMArena WebDev: HTML1593Not measuredOnly Gemini 3.7 Flash measured
LMArena WebDev: from an image1599Not measuredOnly Gemini 3.7 Flash measured
Neither model has a result on the other 15 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for Gemini 3.7 Flash and GPT-4o-mini
BenchmarkGemini 3.7 FlashGPT-4o-miniLeader
AA Intelligence Index39.66.7Gemini 3.7 Flash
Epoch Capabilities Index158.0126.5Gemini 3.7 Flash
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index18 boards, none shared
Agentic work benchmarks for Gemini 3.7 Flash and GPT-4o-mini
BenchmarkGemini 3.7 FlashGPT-4o-miniLeader
Design Arena: agents building web apps1223Not measuredOnly Gemini 3.7 Flash measured
Design Arena: agents building full-stack apps1208Not measuredOnly Gemini 3.7 Flash measured
Design Arena: agents building mobile apps1232Not measuredOnly Gemini 3.7 Flash measured
Design Arena: agents building Android apps1248Not measuredOnly Gemini 3.7 Flash measured
Design Arena: agents building Godot games1264Not measuredOnly Gemini 3.7 Flash measured
Design Arena: agents building games1235Not measuredOnly Gemini 3.7 Flash measured
Design Arena: spreadsheets1022Not measuredOnly Gemini 3.7 Flash measured
LiveBench research76.5%Not measuredOnly Gemini 3.7 Flash measured
LiveBench browser56.0%Not measuredOnly Gemini 3.7 Flash measured
LiveBench long agentic28.3%Not measuredOnly Gemini 3.7 Flash measured
SREBench (Vals AI)4.6%Not measuredOnly Gemini 3.7 Flash measured
Finance Agent (Vals AI)59.0%Not measuredOnly Gemini 3.7 Flash measured
Tax Agent Bench (Vals AI)22.8%Not measuredOnly Gemini 3.7 Flash measured
Legal Research Bench (Vals AI)34.6%Not measuredOnly Gemini 3.7 Flash measured
Excel Modeling Benchmark (Vals AI)71.3%Not measuredOnly Gemini 3.7 Flash measured
Terminal-Bench-Science (Vals AI)7.1%Not measuredOnly Gemini 3.7 Flash measured
APEX-Agents67.8%Not measuredOnly Gemini 3.7 Flash measured
Remote Labor Index5.0%Not measuredOnly Gemini 3.7 Flash measured
Neither model has a result on the other 21 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
Reasoning benchmarks for Gemini 3.7 Flash and GPT-4o-mini
BenchmarkGemini 3.7 FlashGPT-4o-miniLeader
GPQA Diamond93.9%44.2%Gemini 3.7 Flash
LiveBench reasoning87.8%Not measuredOnly Gemini 3.7 Flash measured
MMMU Pro (Vals AI)89.0%56.6%Gemini 3.7 Flash
GPQA Diamond (Artificial Analysis run)94.5%42.6%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%Not measuredOnly Gemini 3.7 Flash measured
ARC-AGI95.5%Not measuredOnly Gemini 3.7 Flash measured
ARC-AGI-284.6%0.0%Gemini 3.7 Flash
Chess puzzles66.0%Not measuredOnly Gemini 3.7 Flash measured
Mystery game puzzles37.0%Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: hard prompts1495Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: expert1512Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: hard prompts in English1487Not measuredOnly Gemini 3.7 Flash measured
LMArena Vision: diagrams1329Not measuredOnly Gemini 3.7 Flash measured
LMArena Vision: homework1320Not measuredOnly Gemini 3.7 Flash measured
Neither model has a result on the other 6 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
Math benchmarks for Gemini 3.7 Flash and GPT-4o-mini
BenchmarkGemini 3.7 FlashGPT-4o-miniLeader
AIMENot measured11.5%Only GPT-4o-mini measured
MATH 500 (Vals AI)Not measured72.6%Only GPT-4o-mini measured
MGSM (Vals AI)Not measured86.2%Only GPT-4o-mini measured
AA Math IndexNot measured14.7Only GPT-4o-mini measured
AIME 2025 (Artificial Analysis run)Not measured14.7%Only GPT-4o-mini measured
OTIS Mock AIME97.2%Not measuredOnly Gemini 3.7 Flash measured
FrontierMath, tiers 1 to 371.6%Not measuredOnly Gemini 3.7 Flash measured
FrontierMath, tier 436.6%Not measuredOnly Gemini 3.7 Flash measured
ProofBench58.0%Not measuredOnly Gemini 3.7 Flash measured
GSM8KNot measured91.3%Only GPT-4o-mini measured
LMArena Text: math1511Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: mathematical work1502Not measuredOnly Gemini 3.7 Flash measured
Neither model has a result on the other 1 board here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index30
Knowledge benchmarks for Gemini 3.7 Flash and GPT-4o-mini
BenchmarkGemini 3.7 FlashGPT-4o-miniLeader
MMLU Pro90.1%62.7%Gemini 3.7 Flash
LegalBench (Vals AI)87.3%Not measuredOnly Gemini 3.7 Flash measured
CorpFin (Vals AI)Not measured45.5%Only GPT-4o-mini measured
TaxEval (Vals AI)74.7%60.5%Gemini 3.7 Flash
MortgageTax (Vals AI)66.7%54.5%Gemini 3.7 Flash
MedQA (Vals AI)Not measured72.4%Only GPT-4o-mini measured
MedCode (Vals AI)53.4%Not measuredOnly Gemini 3.7 Flash measured
MMLU Pro (Artificial Analysis run)Not measured64.8%Only GPT-4o-mini measured
SimpleQA Verified69.2%Not measuredOnly Gemini 3.7 Flash measured
MMLUNot measured81.8%Only GPT-4o-mini measured
LMArena Text: science1511Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: medicine and healthcare1494Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: legal and government1496Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: business and finance1479Not measuredOnly Gemini 3.7 Flash measured
Neither model has a result on the other 3 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Web design benchmarks for Gemini 3.7 Flash and GPT-4o-mini
BenchmarkGemini 3.7 FlashGPT-4o-miniLeader
Design Arena: websites1304Not measuredOnly Gemini 3.7 Flash measured
Design Arena: UI components1299Not measuredOnly Gemini 3.7 Flash measured
Design Arena: game dev1318Not measuredOnly Gemini 3.7 Flash measured
Design Arena: data viz1314Not measuredOnly Gemini 3.7 Flash measured
Design Arena: 3D1308Not measuredOnly Gemini 3.7 Flash measured
Design Arena: all code categories1308Not measuredOnly Gemini 3.7 Flash measured
Design Arena: SVG1283Not measuredOnly Gemini 3.7 Flash measured
Design Arena: ASCII art1276Not measuredOnly Gemini 3.7 Flash measured
LiveBench design73.2%Not measuredOnly Gemini 3.7 Flash measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
General benchmarks for Gemini 3.7 Flash and GPT-4o-mini
BenchmarkGemini 3.7 FlashGPT-4o-miniLeader
LiveBench data analysis68.0%Not measuredOnly Gemini 3.7 Flash measured
SAGE (Vals AI)49.2%Not measuredOnly Gemini 3.7 Flash measured
MedScribe (Vals AI)83.9%Not measuredOnly Gemini 3.7 Flash measured
IFBenchNot measured31.0%Only GPT-4o-mini measured
Long context reasoning (AA-LCR)83.0%Not measuredOnly Gemini 3.7 Flash measured
DTBench96.8%54.4%Gemini 3.7 Flash
LMCA50.4%10.4%Gemini 3.7 Flash
Furniture assembly26.7%Not measuredOnly Gemini 3.7 Flash measured
PIQANot measured88.7%Only GPT-4o-mini measured
LMArena Text1489Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: instruction following1484Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: creative writing1488Not measuredOnly Gemini 3.7 Flash measured
LMArena Vision1314Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: English1483Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: non-English1487Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: Chinese1552Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: German1506Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: Japanese1511Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: Korean1481Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: French1500Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: Spanish1495Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: Russian1515Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: Polish1503Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: multi-turn1485Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: longer queries1492Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: writing and language1491Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: entertainment and media1477Not measuredOnly Gemini 3.7 Flash measured
LMArena Vision: English1309Not measuredOnly Gemini 3.7 Flash measured
LMArena Vision: Chinese1394Not measuredOnly Gemini 3.7 Flash measured
LMArena Vision: reading text1322Not measuredOnly Gemini 3.7 Flash measured
LMArena Vision: humor1299Not measuredOnly Gemini 3.7 Flash measured
LMArena Vision: creative writing1312Not measuredOnly Gemini 3.7 Flash measured
Neither model has a result on the other 14 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-4o-mini
BenchmarkGemini 3.7 FlashGPT-4o-miniLeader
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-4o-mini$0.0004Fits in one request

GPT-4o-mini costs less

Repository review

50K input + 3K output tokens

Gemini 3.7 Flash$0.049Fits in one request
GPT-4o-mini$0.0093Fits in one request

GPT-4o-mini costs less

Agent loop

200K input + 10K output tokens

Gemini 3.7 Flash$0.188Fits in one request
GPT-4o-mini$0.036Exceeds the context window

GPT-4o-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-4o-mini
SpecificationGoogle Gemini 3.7 FlashOpenAI GPT-4o-mini
MakerGoogleOpenAI
WeightsNo public weights listedNo public weights listed
ReleasedAugust 13, 2026July 18, 2024
Context window1.05M128K
Input $/Mtok$0.75$0.15
Output $/Mtok$3.75$0.60
Coding index58.0Not benchmarked
Independent measurements behind it60
Cost per solved task$1.78Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemini 3.7 Flash better than GPT-4o-mini for coding?

Only Gemini 3.7 Flash has a coding score, 58.0 on the Vibe Coding Index; GPT-4o-mini has none, so the two cannot be ranked on coding. GPT-4o-mini has the edge where output tokens cost 6.3x less.

Which is cheaper, Gemini 3.7 Flash or GPT-4o-mini?

Gemini 3.7 Flash costs $0.75 per million input tokens and $3.75 per million output tokens; GPT-4o-mini costs $0.15 and $0.60. Cost per solved task is known for Gemini 3.7 Flash only.

Which has the bigger context window, Gemini 3.7 Flash or GPT-4o-mini?

Gemini 3.7 Flash, with 1.05M tokens against 128K.

How many benchmarks is the Gemini 3.7 Flash vs GPT-4o-mini comparison based on?

14 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-4o-mini's on 0. Where only one model has a result, the page says so instead of naming a winner.

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