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Gemini 3.7 Flash vs GPT-5.6 Sol

GPT-5.6 Sol leads Gemini 3.7 Flash on coding, 73.5 to 58.0 on the Vibe Coding Index, and Gemini 3.7 Flash costs 5.3x less per output token ($3.75 vs $20.00 per million). Updated October 22, 2026; 111 benchmarks have a result for both.

Replace Gemini 3.7 Flash Replace GPT-5.6 Sol
Gemini 3.7 FlashGoogle
Coding
58.0
Overall
65.7
Output $/M
$3.75
52
GPT-5.6 SolOpenAI
Coding
73.5
Overall
74.7
Output $/M
$20.00
59
vs boards won of 111 shared

Gemini 3.7 Flash or GPT-5.6 Sol: which to pick

Pick GPT-5.6 Sol for coding: it scores 73.5 on the Vibe Coding Index against 58.0 for Gemini 3.7 Flash.

Pick Gemini 3.7 Flash for output tokens 5.3x cheaper ($3.75 vs $20.00 per million) and work measured by LiveCodeBench (Vals AI) or SkillsBench (Vals AI), where it leads.

Model AGemini 3.7 FlashGoogle58.0/1006 independent benchmarks$1.78 per solved taskModel BGPT-5.6 SolOpenAI73.5/1006 independent benchmarks$1.20 per solved task
Shared results
111
Gemini 3.7 Flash only
3
GPT-5.6 Sol only
28
Like-for-like categories
8 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.

At a glance

Coding index
Gemini 3.7 Flash 58.0 from 6 benchmarks, GPT-5.6 Sol 73.5 from 6 benchmarks.
Widest category
Math, 16.5 points apart on average over 6 shared benchmarks.
Widest single gap
FrontierMath, tier 4: Gemini 3.7 Flash 36.6% against GPT-5.6 Sol 82.9%, 46.3 points apart on a 0 to 100 scale.
Output price
Gemini 3.7 Flash $3.75 against GPT-5.6 Sol $20.00 per million output tokens, 5.3x apart.
Context window
Gemini 3.7 Flash takes 1.05M tokens, GPT-5.6 Sol 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
CategoryGemini 3.7 FlashBarsGPT-5.6 SolTies
Coding617
Intelligence02
Agentic work411
Reasoning87
Math24
Knowledge83
Web design09
General246
All shared boards5259
CodingLike-for-likeSix of these boards feed the Vibe Coding Index617
Coding benchmarks for Gemini 3.7 Flash and GPT-5.6 Sol
BenchmarkGemini 3.7 FlashGPT-5.6 SolLeader
LiveBench coding80.5%89.7%GPT-5.6 Sol
LMArena WebDev15911616GPT-5.6 Sol
AA Coding Index71.577.4GPT-5.6 Sol
LiveCodeBench (Vals AI)88.7%82.6%Gemini 3.7 Flash
SWE-bench Verified (Vals AI)80.8%96.2%GPT-5.6 Sol
Terminal-Bench 4 (Vals AI)12.1%37.9%GPT-5.6 Sol
Vibe Code Bench (Vals AI)70.4%80.5%GPT-5.6 Sol
Code migration (Vals AI)34.8%52.9%GPT-5.6 Sol
ProgramBench (Vals AI)0.0%1.5%GPT-5.6 Sol
IOI (Vals AI)67.8%91.2%GPT-5.6 Sol
Terminal-Bench 2.1 (Vals AI)77.5%85.8%GPT-5.6 Sol
Vibe Code Bench 1-100 (Vals AI)Not measured20.0%Only GPT-5.6 Sol measured
SkillsBench (Vals AI)65.9%54.1%Gemini 3.7 Flash
CyberBench (Vals AI)43.8%76.3%GPT-5.6 Sol
SciCode59.8%57.8%Gemini 3.7 Flash
Terminal-Bench Hard (Artificial Analysis run)Not measured65.9%Only GPT-5.6 Sol measured
FrontierCode43.6%47.5%GPT-5.6 Sol
FrontierSWE20.3%32.2%GPT-5.6 Sol
GSO-BenchNot measured76.5%Only GPT-5.6 Sol measured
WeirdMLNot measured88.8%Only GPT-5.6 Sol measured
MirrorCodeNot measured20.0%Only GPT-5.6 Sol measured
ALE-Bench904.32176.9GPT-5.6 Sol
LMArena Text: coding14991496Gemini 3.7 Flash
LMArena Text: software and IT14981489Gemini 3.7 Flash
LMArena WebDev: React15871620GPT-5.6 Sol
LMArena WebDev: HTML15931618GPT-5.6 Sol
LMArena WebDev: from an image15991609GPT-5.6 Sol
SciCode13.8%10.8%Gemini 3.7 Flash
Neither model has a result on the other 11 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for Gemini 3.7 Flash and GPT-5.6 Sol
BenchmarkGemini 3.7 FlashGPT-5.6 SolLeader
AA Intelligence Index39.647.0GPT-5.6 Sol
Epoch Capabilities Index158.0162.8GPT-5.6 Sol
Agentic workLike-for-likePublic boards, shown as published, kept out of the index411
Agentic work benchmarks for Gemini 3.7 Flash and GPT-5.6 Sol
BenchmarkGemini 3.7 FlashGPT-5.6 SolLeader
AA Agentic IndexNot measured50.2Only GPT-5.6 Sol measured
Design Arena: agents building web apps12231245GPT-5.6 Sol
Design Arena: agents building full-stack apps12081261GPT-5.6 Sol
Design Arena: agents building mobile apps12291248GPT-5.6 Sol
Design Arena: agents building Android apps12431240Gemini 3.7 Flash
Design Arena: agents building Godot games12641260Gemini 3.7 Flash
Design Arena: agents building games12351266GPT-5.6 Sol
Design Arena: HTML slidesNot measured1236Only GPT-5.6 Sol measured
Design Arena: PowerPoint slidesNot measured1215Only GPT-5.6 Sol measured
Design Arena: spreadsheets1058Not measuredOnly Gemini 3.7 Flash measured
LiveBench research76.5%54.5%Gemini 3.7 Flash
LiveBench browser56.0%88.0%GPT-5.6 Sol
LiveBench long agentic28.3%48.5%GPT-5.6 Sol
SREBench (Vals AI)4.6%30.5%GPT-5.6 Sol
Finance Agent (Vals AI)59.0%53.8%Gemini 3.7 Flash
Tax Agent Bench (Vals AI)22.8%31.1%GPT-5.6 Sol
Legal Research Bench (Vals AI)34.6%48.1%GPT-5.6 Sol
Excel Modeling Benchmark (Vals AI)71.3%72.3%GPT-5.6 Sol
BioMysteryBench (Vals AI)Not measured71.1%Only GPT-5.6 Sol measured
Terminal-Bench-Science (Vals AI)7.1%20.0%GPT-5.6 Sol
Vals Science IndexNot measured41.5%Only GPT-5.6 Sol measured
Time Horizon Index (Vals AI)Not measured23.8%Only GPT-5.6 Sol measured
CUA-bench (Vals AI)Not measured8.3%Only GPT-5.6 Sol measured
τ²-Bench TelecomNot measured85.1%Only GPT-5.6 Sol measured
APEX-Agents67.8%Not measuredOnly Gemini 3.7 Flash measured
Remote Labor Index5.0%Not measuredOnly Gemini 3.7 Flash measured
OSWorld 2.0Not measured27.3%Only GPT-5.6 Sol measured
BALROGNot measured60.0%Only GPT-5.6 Sol measured
PostTrainBenchNot measured36.2%Only GPT-5.6 Sol measured
Vending-Bench 2Not measured$9,619Only GPT-5.6 Sol measured
LMArena SearchNot measured1261Only GPT-5.6 Sol measured
Neither model has a result on the other 10 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index87
Reasoning benchmarks for Gemini 3.7 Flash and GPT-5.6 Sol
BenchmarkGemini 3.7 FlashGPT-5.6 SolLeader
GPQA Diamond93.9%95.2%GPT-5.6 Sol
LiveBench reasoning87.8%91.7%GPT-5.6 Sol
MysteryMechanism (Vals AI)Not measured33.3%Only GPT-5.6 Sol measured
MMMU Pro (Vals AI)89.0%88.8%Gemini 3.7 Flash
GPQA Diamond (Artificial Analysis run)94.5%94.1%Gemini 3.7 Flash
Humanity's Last Exam (Artificial Analysis run)47.9%49.5%GPT-5.6 Sol
GPQA Diamond (Epoch AI run)94.8%93.5%Gemini 3.7 Flash
ARC-AGI95.5%97.5%GPT-5.6 Sol
ARC-AGI-284.6%92.5%GPT-5.6 Sol
SimpleBenchNot measured64.8%Only GPT-5.6 Sol measured
Chess puzzles66.0%59.0%Gemini 3.7 Flash
Mystery game puzzles37.0%58.0%GPT-5.6 Sol
EBR-benchNot measured44.8%Only GPT-5.6 Sol measured
LMArena Text: hard prompts14951483Gemini 3.7 Flash
LMArena Text: expert15121517GPT-5.6 Sol
LMArena Text: hard prompts in English14871477Gemini 3.7 Flash
LMArena Vision: diagrams13291305Gemini 3.7 Flash
LMArena Vision: homework13201305Gemini 3.7 Flash
Kagi LLM BenchmarkNot measured67.0%Only GPT-5.6 Sol measured
Neither model has a result on the other 3 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index24
Math benchmarks for Gemini 3.7 Flash and GPT-5.6 Sol
BenchmarkGemini 3.7 FlashGPT-5.6 SolLeader
OTIS Mock AIME97.2%100.0%GPT-5.6 Sol
FrontierMath, tiers 1 to 371.6%89.1%GPT-5.6 Sol
FrontierMath, tier 436.6%82.9%GPT-5.6 Sol
ProofBench58.0%83.0%GPT-5.6 Sol
LMArena Text: math15111473Gemini 3.7 Flash
LMArena Text: mathematical work15021496Gemini 3.7 Flash
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index83
Knowledge benchmarks for Gemini 3.7 Flash and GPT-5.6 Sol
BenchmarkGemini 3.7 FlashGPT-5.6 SolLeader
MMLU Pro90.1%89.1%Gemini 3.7 Flash
LegalBench (Vals AI)87.3%87.0%Gemini 3.7 Flash
CorpFin (Vals AI)Not measured64.4%Only GPT-5.6 Sol measured
TaxEval (Vals AI)74.7%74.8%GPT-5.6 Sol
MortgageTax (Vals AI)66.7%67.3%GPT-5.6 Sol
MedCode (Vals AI)53.4%44.0%Gemini 3.7 Flash
SimpleQA Verified69.2%69.7%GPT-5.6 Sol
LMArena Text: science15111455Gemini 3.7 Flash
LMArena Text: medicine and healthcare14941431Gemini 3.7 Flash
LMArena Text: legal and government14961476Gemini 3.7 Flash
LMArena Text: business and finance14791457Gemini 3.7 Flash
FACTS Benchmark Suite71.2%52.4%Gemini 3.7 Flash
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index09
Web design benchmarks for Gemini 3.7 Flash and GPT-5.6 Sol
BenchmarkGemini 3.7 FlashGPT-5.6 SolLeader
Design Arena: websites13031321GPT-5.6 Sol
Design Arena: UI components12961335GPT-5.6 Sol
Design Arena: game dev13171367GPT-5.6 Sol
Design Arena: data viz13151322GPT-5.6 Sol
Design Arena: 3D13071384GPT-5.6 Sol
Design Arena: all code categories13081341GPT-5.6 Sol
Design Arena: SVG12831314GPT-5.6 Sol
Design Arena: ASCII art12761304GPT-5.6 Sol
LiveBench design73.2%81.6%GPT-5.6 Sol
GeneralLike-for-likePublic boards, shown as published, kept out of the index246
General benchmarks for Gemini 3.7 Flash and GPT-5.6 Sol
BenchmarkGemini 3.7 FlashGPT-5.6 SolLeader
LiveBench data analysis68.0%79.8%GPT-5.6 Sol
Public Benefits Bench (Vals AI)Not measured66.5%Only GPT-5.6 Sol measured
SAGE (Vals AI)49.2%52.6%GPT-5.6 Sol
MedScribe (Vals AI)83.9%85.2%GPT-5.6 Sol
Vals Multimodal IndexNot measured72.6%Only GPT-5.6 Sol measured
IFBenchNot measured72.7%Only GPT-5.6 Sol measured
Long context reasoning (AA-LCR)83.0%84.0%GPT-5.6 Sol
DTBench96.8%95.7%Gemini 3.7 Flash
LMCA50.4%58.5%GPT-5.6 Sol
Surface Evolver BenchNot measured93.1%Only GPT-5.6 Sol measured
Furniture assembly26.7%56.7%GPT-5.6 Sol
LMArena Text14891457Gemini 3.7 Flash
LMArena Text: instruction following14841483Gemini 3.7 Flash
LMArena Text: creative writing14881449Gemini 3.7 Flash
LMArena Vision13141281Gemini 3.7 Flash
LMArena Text: English14831457Gemini 3.7 Flash
LMArena Text: non-English14871451Gemini 3.7 Flash
LMArena Text: Chinese15521525Gemini 3.7 Flash
LMArena Text: German15061476Gemini 3.7 Flash
LMArena Text: Japanese15111472Gemini 3.7 Flash
LMArena Text: Korean14811442Gemini 3.7 Flash
LMArena Text: French15001472Gemini 3.7 Flash
LMArena Text: Spanish14951443Gemini 3.7 Flash
LMArena Text: Russian15151471Gemini 3.7 Flash
LMArena Text: Polish15031459Gemini 3.7 Flash
LMArena Text: multi-turn14851461Gemini 3.7 Flash
LMArena Text: longer queries14921481Gemini 3.7 Flash
LMArena Text: writing and language14911462Gemini 3.7 Flash
LMArena Text: entertainment and media14771450Gemini 3.7 Flash
LMArena Vision: English13091280Gemini 3.7 Flash
LMArena Vision: Chinese13941313Gemini 3.7 Flash
LMArena Vision: reading text13221290Gemini 3.7 Flash
LMArena Vision: humor12991246Gemini 3.7 Flash
LMArena Vision: creative writing13121283Gemini 3.7 Flash
LMArena DocumentNot measured1483Only GPT-5.6 Sol measured
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-5.6 Sol
BenchmarkGemini 3.7 FlashGPT-5.6 SolLeader
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-5.6 Sol$0.014Fits 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-5.6 Sol$0.260Fits 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-5.6 Sol$1.00Fits 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-5.6 Sol
SpecificationGoogle Gemini 3.7 FlashOpenAI GPT-5.6 Sol
MakerGoogleOpenAI
WeightsNo public weights listedNo public weights listed
ReleasedAugust 13, 2026July 9, 2026
Context window1.05M1.05M
Input $/Mtok$0.75$4.00
Output $/Mtok$3.75$20.00
Coding index58.073.5
Independent measurements behind it66
Cost per solved task$1.78$1.20
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemini 3.7 Flash better than GPT-5.6 Sol for coding?

Pick GPT-5.6 Sol for coding: it scores 73.5 on the Vibe Coding Index against 58.0 for Gemini 3.7 Flash. Pick Gemini 3.7 Flash for output tokens 5.3x cheaper ($3.75 vs $20.00 per million) and work measured by LiveCodeBench (Vals AI) or SkillsBench (Vals AI), where it leads.

Which is cheaper, Gemini 3.7 Flash or GPT-5.6 Sol?

Gemini 3.7 Flash costs $0.75 per million input tokens and $3.75 per million output tokens; GPT-5.6 Sol costs $4.00 and $20.00. Per solved SWE-bench Verified task, Gemini 3.7 Flash costs $1.78 and GPT-5.6 Sol $1.20.

Which has the bigger context window, Gemini 3.7 Flash or GPT-5.6 Sol?

GPT-5.6 Sol, with 1.05M tokens against 1.05M.

How many benchmarks is the Gemini 3.7 Flash vs GPT-5.6 Sol comparison based on?

111 benchmarks have a result for both models as of October 22, 2026. Gemini 3.7 Flash's coding index rests on 6 independent benchmarks and GPT-5.6 Sol's on 6. Where only one model has a result, the page says so instead of naming a winner.

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