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Gemini 3.5 Flash vs GPT-5.6 Sol

GPT-5.6 Sol leads Gemini 3.5 Flash on coding, 73.5 to 39.8 on the Vibe Coding Index, and Gemini 3.5 Flash costs 2.2x less per output token ($9.00 vs $20.00 per million). Updated October 22, 2026; 111 benchmarks have a result for both.

Replace Gemini 3.5 Flash Replace GPT-5.6 Sol
Gemini 3.5 FlashGoogle
Coding
39.8
Overall
63.8
Output $/M
$9.00
43
GPT-5.6 SolOpenAI
Coding
73.5
Overall
74.7
Output $/M
$20.00
68
vs boards won of 111 shared

Gemini 3.5 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 39.8 for Gemini 3.5 Flash.

Pick Gemini 3.5 Flash for output tokens 2.2x cheaper ($9.00 vs $20.00 per million) and work measured by LiveCodeBench (Vals AI) or LMArena Text: software and IT, where it leads.

Model AGemini 3.5 FlashGoogle39.8/1004 independent benchmarks$1.21 per solved taskModel BGPT-5.6 SolOpenAI73.5/1006 independent benchmarks$1.20 per solved task
Shared results
111
Gemini 3.5 Flash only
1
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 3.5 Flash 39.8 from 4 benchmarks, GPT-5.6 Sol 73.5 from 6 benchmarks.
Widest category
Math, 24.0 points apart on average over 6 shared benchmarks.
Widest single gap
FrontierMath, tier 4: Gemini 3.5 Flash 26.8% against GPT-5.6 Sol 82.9%, 56.1 points apart on a 0 to 100 scale.
Output price
Gemini 3.5 Flash $9.00 against GPT-5.6 Sol $20.00 per million output tokens, 2.2x apart.
Context window
Gemini 3.5 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 3.5 FlashBarsGPT-5.6 SolTies
Coding316
Intelligence02
Agentic work213
Reasoning511
Math15
Knowledge93
Web design08
General2310
All shared boards4368
CodingLike-for-likeSix of these boards feed the Vibe Coding Index316
Coding benchmarks for Gemini 3.5 Flash and GPT-5.6 Sol
BenchmarkGemini 3.5 FlashGPT-5.6 SolLeader
LiveBench codingNot measured89.7%Only GPT-5.6 Sol measured
LMArena WebDev14991616GPT-5.6 Sol
AA Coding IndexNot measured77.4Only GPT-5.6 Sol measured
LiveCodeBench (Vals AI)87.6%82.6%Gemini 3.5 Flash
SWE-bench Verified (Vals AI)78.8%96.2%GPT-5.6 Sol
Terminal-Bench 4 (Vals AI)6.1%37.9%GPT-5.6 Sol
Vibe Code Bench (Vals AI)48.7%80.5%GPT-5.6 Sol
Code migration (Vals AI)26.8%52.9%GPT-5.6 Sol
ProgramBench (Vals AI)0.0%1.5%GPT-5.6 Sol
IOI (Vals AI)Not measured91.2%Only GPT-5.6 Sol measured
Terminal-Bench 2.1 (Vals AI)74.2%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)52.7%54.1%GPT-5.6 Sol
CyberBench (Vals AI)Not measured76.3%Only GPT-5.6 Sol measured
SciCode53.9%57.8%GPT-5.6 Sol
Terminal-Bench Hard (Artificial Analysis run)46.2%65.9%GPT-5.6 Sol
FrontierCodeNot measured47.5%Only GPT-5.6 Sol measured
FrontierSWENot measured32.2%Only GPT-5.6 Sol measured
GSO-BenchNot measured76.5%Only GPT-5.6 Sol measured
WeirdML62.6%88.8%GPT-5.6 Sol
MirrorCodeNot measured20.0%Only GPT-5.6 Sol measured
ALE-Bench911.02176.9GPT-5.6 Sol
LMArena Text: coding14931496GPT-5.6 Sol
LMArena Text: software and IT14941489Gemini 3.5 Flash
LMArena WebDev: React14951620GPT-5.6 Sol
LMArena WebDev: HTML15221618GPT-5.6 Sol
LMArena WebDev: from an image15041609GPT-5.6 Sol
SciCode12.3%10.8%Gemini 3.5 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 3.5 Flash and GPT-5.6 Sol
BenchmarkGemini 3.5 FlashGPT-5.6 SolLeader
AA Intelligence Index33.647.0GPT-5.6 Sol
Epoch Capabilities Index154.9162.8GPT-5.6 Sol
Agentic workLike-for-likePublic boards, shown as published, kept out of the index213
Agentic work benchmarks for Gemini 3.5 Flash and GPT-5.6 Sol
BenchmarkGemini 3.5 FlashGPT-5.6 SolLeader
AA Agentic IndexNot measured50.2Only GPT-5.6 Sol measured
Design Arena: agents building web apps12021245GPT-5.6 Sol
Design Arena: agents building full-stack apps11971261GPT-5.6 Sol
Design Arena: agents building mobile apps11741248GPT-5.6 Sol
Design Arena: agents building Android apps11851240GPT-5.6 Sol
Design Arena: agents building Godot games10981260GPT-5.6 Sol
Design Arena: agents building games11801266GPT-5.6 Sol
Design Arena: HTML slides10931236GPT-5.6 Sol
Design Arena: PowerPoint slides11541215GPT-5.6 Sol
LiveBench researchNot measured54.5%Only GPT-5.6 Sol measured
LiveBench browserNot measured88.0%Only GPT-5.6 Sol measured
LiveBench long agenticNot measured48.5%Only GPT-5.6 Sol measured
SREBench (Vals AI)Not measured30.5%Only GPT-5.6 Sol measured
Finance Agent (Vals AI)57.9%53.8%Gemini 3.5 Flash
Tax Agent Bench (Vals AI)21.6%31.1%GPT-5.6 Sol
Legal Research Bench (Vals AI)30.8%48.1%GPT-5.6 Sol
Excel Modeling Benchmark (Vals AI)63.5%72.3%GPT-5.6 Sol
BioMysteryBench (Vals AI)Not measured71.1%Only GPT-5.6 Sol measured
Terminal-Bench-Science (Vals AI)5.7%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 Telecom95.6%85.1%Gemini 3.5 Flash
APEX-Agents27.5%Not measuredOnly Gemini 3.5 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 2$5,396$9,619GPT-5.6 Sol
LMArena SearchNot measured1261Only GPT-5.6 Sol measured
Neither model has a result on the other 12 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index511
Reasoning benchmarks for Gemini 3.5 Flash and GPT-5.6 Sol
BenchmarkGemini 3.5 FlashGPT-5.6 SolLeader
GPQA Diamond92.7%95.2%GPT-5.6 Sol
LiveBench reasoningNot measured91.7%Only GPT-5.6 Sol measured
MysteryMechanism (Vals AI)Not measured33.3%Only GPT-5.6 Sol measured
MMMU Pro (Vals AI)88.3%88.8%GPT-5.6 Sol
GPQA Diamond (Artificial Analysis run)92.2%94.1%GPT-5.6 Sol
Humanity's Last Exam (Artificial Analysis run)42.7%49.5%GPT-5.6 Sol
GPQA Diamond (Epoch AI run)92.8%93.5%GPT-5.6 Sol
ARC-AGI92.5%97.5%GPT-5.6 Sol
ARC-AGI-272.1%92.5%GPT-5.6 Sol
SimpleBench76.7%64.8%Gemini 3.5 Flash
Chess puzzles45.0%59.0%GPT-5.6 Sol
Mystery game puzzles32.0%58.0%GPT-5.6 Sol
EBR-bench4.8%44.8%GPT-5.6 Sol
LMArena Text: hard prompts14881483Gemini 3.5 Flash
LMArena Text: expert14951517GPT-5.6 Sol
LMArena Text: hard prompts in English14831477Gemini 3.5 Flash
LMArena Vision: diagrams13231305Gemini 3.5 Flash
LMArena Vision: homework13301305Gemini 3.5 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 index15
Math benchmarks for Gemini 3.5 Flash and GPT-5.6 Sol
BenchmarkGemini 3.5 FlashGPT-5.6 SolLeader
OTIS Mock AIME95.6%100.0%GPT-5.6 Sol
FrontierMath, tiers 1 to 362.8%89.1%GPT-5.6 Sol
FrontierMath, tier 426.8%82.9%GPT-5.6 Sol
ProofBench31.0%83.0%GPT-5.6 Sol
LMArena Text: math15041473Gemini 3.5 Flash
LMArena Text: mathematical work14951496GPT-5.6 Sol
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index93
Knowledge benchmarks for Gemini 3.5 Flash and GPT-5.6 Sol
BenchmarkGemini 3.5 FlashGPT-5.6 SolLeader
MMLU Pro89.5%89.1%Gemini 3.5 Flash
LegalBench (Vals AI)83.6%87.0%GPT-5.6 Sol
CorpFin (Vals AI)64.7%64.4%Gemini 3.5 Flash
TaxEval (Vals AI)74.4%74.8%GPT-5.6 Sol
MortgageTax (Vals AI)68.1%67.3%Gemini 3.5 Flash
MedCode (Vals AI)55.8%44.0%Gemini 3.5 Flash
SimpleQA Verified66.2%69.7%GPT-5.6 Sol
LMArena Text: science14951455Gemini 3.5 Flash
LMArena Text: medicine and healthcare14831431Gemini 3.5 Flash
LMArena Text: legal and government14881476Gemini 3.5 Flash
LMArena Text: business and finance14671457Gemini 3.5 Flash
FACTS Benchmark Suite63.1%52.4%Gemini 3.5 Flash
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index08
Web design benchmarks for Gemini 3.5 Flash and GPT-5.6 Sol
BenchmarkGemini 3.5 FlashGPT-5.6 SolLeader
Design Arena: websites12671321GPT-5.6 Sol
Design Arena: UI components12691335GPT-5.6 Sol
Design Arena: game dev12631367GPT-5.6 Sol
Design Arena: data viz12361322GPT-5.6 Sol
Design Arena: 3D12431384GPT-5.6 Sol
Design Arena: all code categories12661341GPT-5.6 Sol
Design Arena: SVG12531314GPT-5.6 Sol
Design Arena: ASCII art12541304GPT-5.6 Sol
LiveBench designNot measured81.6%Only GPT-5.6 Sol measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index2310
General benchmarks for Gemini 3.5 Flash and GPT-5.6 Sol
BenchmarkGemini 3.5 FlashGPT-5.6 SolLeader
LiveBench data analysisNot measured79.8%Only GPT-5.6 Sol measured
Public Benefits Bench (Vals AI)59.5%66.5%GPT-5.6 Sol
SAGE (Vals AI)49.9%52.6%GPT-5.6 Sol
MedScribe (Vals AI)76.6%85.2%GPT-5.6 Sol
Vals Multimodal Index62.9%72.6%GPT-5.6 Sol
IFBench76.3%72.7%Gemini 3.5 Flash
Long context reasoning (AA-LCR)74.3%84.0%GPT-5.6 Sol
DTBench94.7%95.7%GPT-5.6 Sol
LMCA47.1%58.5%GPT-5.6 Sol
Surface Evolver Bench58.1%93.1%GPT-5.6 Sol
Furniture assemblyNot measured56.7%Only GPT-5.6 Sol measured
LMArena Text14821457Gemini 3.5 Flash
LMArena Text: instruction following14671483GPT-5.6 Sol
LMArena Text: creative writing14701449Gemini 3.5 Flash
LMArena Vision13081281Gemini 3.5 Flash
LMArena Text: English14811457Gemini 3.5 Flash
LMArena Text: non-English14751451Gemini 3.5 Flash
LMArena Text: Chinese15271525Gemini 3.5 Flash
LMArena Text: German14941476Gemini 3.5 Flash
LMArena Text: Japanese14861472Gemini 3.5 Flash
LMArena Text: Korean14481442Gemini 3.5 Flash
LMArena Text: French14901472Gemini 3.5 Flash
LMArena Text: Spanish14801443Gemini 3.5 Flash
LMArena Text: Russian14931471Gemini 3.5 Flash
LMArena Text: Polish15161459Gemini 3.5 Flash
LMArena Text: multi-turn14811461Gemini 3.5 Flash
LMArena Text: longer queries14831481Gemini 3.5 Flash
LMArena Text: writing and language14781462Gemini 3.5 Flash
LMArena Text: entertainment and media14591450Gemini 3.5 Flash
LMArena Vision: English13051280Gemini 3.5 Flash
LMArena Vision: Chinese13771313Gemini 3.5 Flash
LMArena Vision: reading text13171290Gemini 3.5 Flash
LMArena Vision: humor13211246Gemini 3.5 Flash
LMArena Vision: creative writing13011283Gemini 3.5 Flash
LMArena Document14631483GPT-5.6 Sol
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 3.5 Flash and GPT-5.6 Sol
BenchmarkGemini 3.5 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 3.5 Flash$0.0060Fits in one request
GPT-5.6 Sol$0.014Fits in one request

Gemini 3.5 Flash costs less

Repository review

50K input + 3K output tokens

Gemini 3.5 Flash$0.102Fits in one request
GPT-5.6 Sol$0.260Fits in one request

Gemini 3.5 Flash costs less

Agent loop

200K input + 10K output tokens

Gemini 3.5 Flash$0.390Fits in one request
GPT-5.6 Sol$1.00Fits in one request

Gemini 3.5 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 3.5 Flash and GPT-5.6 Sol
SpecificationGoogle Gemini 3.5 FlashOpenAI GPT-5.6 Sol
MakerGoogleOpenAI
WeightsNo public weights listedNo public weights listed
ReleasedMay 19, 2026July 9, 2026
Context window1.05M1.05M
Input $/Mtok$1.50$4.00
Output $/Mtok$9.00$20.00
Coding index39.873.5
Independent measurements behind it46
Cost per solved task$1.21$1.20
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemini 3.5 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 39.8 for Gemini 3.5 Flash. Pick Gemini 3.5 Flash for output tokens 2.2x cheaper ($9.00 vs $20.00 per million) and work measured by LiveCodeBench (Vals AI) or LMArena Text: software and IT, where it leads.

Which is cheaper, Gemini 3.5 Flash or GPT-5.6 Sol?

Gemini 3.5 Flash costs $1.50 per million input tokens and $9.00 per million output tokens; GPT-5.6 Sol costs $4.00 and $20.00. Per solved SWE-bench Verified task, Gemini 3.5 Flash costs $1.21 and GPT-5.6 Sol $1.20.

Which has the bigger context window, Gemini 3.5 Flash or GPT-5.6 Sol?

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

How many benchmarks is the Gemini 3.5 Flash vs GPT-5.6 Sol comparison based on?

111 benchmarks have a result for both models as of October 22, 2026. Gemini 3.5 Flash's coding index rests on 4 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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