Gemini 3.6 Flash vs GPT-5.6 Sol

GPT-5.6 Sol leads Gemini 3.6 Flash on coding, 73.5 to 62.5 on the Vibe Coding Index, and Gemini 3.6 Flash costs 5.3x less per output token ($3.75 vs $20.00 per million). Updated October 21, 2026; 107 benchmarks have a result for both.

Replace Gemini 3.6 Flash Replace GPT-5.6 Sol

Gemini 3.6 FlashGoogle

Coding: 62.5
Overall: 68.3
Output $/M: $3.75

GPT-5.6 SolOpenAI

Coding: 73.5
Overall: 74.7
Output $/M: $20.00

vs boards won of 107 shared, 1 tied

Gemini 3.6 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 62.5 for Gemini 3.6 Flash.

Pick Gemini 3.6 Flash for output tokens 5.3x cheaper ($3.75 vs $20.00 per million) and work measured by LiveCodeBench (Vals AI) or LMArena Text: software and IT, where it leads.

Model AGemini 3.6 FlashGoogle62.5/1004 independent benchmarks$1.49 per solved task Model BGPT-5.6 SolOpenAI73.5/1006 independent benchmarks$1.20 per solved task

107232

Shared results: 107
Gemini 3.6 Flash only: 2
GPT-5.6 Sol only: 32
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.

Coding
Vibe Coding Index, six public coding benchmarks
GPT-5.6 Sol
GPT-5.6 Sol scores 73.5 against 62.5.
4 and 6 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.6 Sol
GPT-5.6 Sol leads, 47.0 against 34.0.
Same board, both measured
Agentic work
AA Agentic Index
GPT-5.6 Sol
GPT-5.6 Sol leads, 50.2 against 29.0.
Same board, both measured
Reasoning
GPQA Diamond
GPT-5.6 Sol
GPT-5.6 Sol leads, 95.2% against 93.4%.
Same board, both measured
Knowledge
MMLU Pro
Gemini 3.6 Flash
Gemini 3.6 Flash leads, 89.3% against 89.1%.
Same board, both measured
Web design
Design Arena: websites
GPT-5.6 Sol
GPT-5.6 Sol leads, 1321 against 1300.
Same board, both measured
Long documents
Listed context window
GPT-5.6 Sol
GPT-5.6 Sol takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemini 3.6 Flash
Gemini 3.6 Flash $0.0026, GPT-5.6 Sol $0.014 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.6 Sol
Gemini 3.6 Flash $1.49, GPT-5.6 Sol $1.20.
Published attempt cost and pass rate

At a glance

Coding index: Gemini 3.6 Flash 62.5 from 4 benchmarks, GPT-5.6 Sol 73.5 from 6 benchmarks.
Widest category: Math, 25.1 points apart on average over 6 shared benchmarks.
Widest single gap: FrontierMath, tier 4: Gemini 3.6 Flash 22.0% against GPT-5.6 Sol 82.9%, 61.0 points apart on a 0 to 100 scale.
Output price: Gemini 3.6 Flash $3.75 against GPT-5.6 Sol $20.00 per million output tokens, 5.3x apart.
Context window: Gemini 3.6 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
Category	Gemini 3.6 Flash	GPT-5.6 Sol	Ties
Coding	2	17	1 tie
Intelligence	0	2
Agentic work	1	13
Reasoning	5	9
Math	1	5
Knowledge	9	3
Web design	0	7
General	19	13
All shared boards	37	69	1 tie

CodingLike-for-likeSix of these boards feed the Vibe Coding Index217

Coding benchmarks for Gemini 3.6 Flash and GPT-5.6 Sol
Benchmark	Gemini 3.6 Flash	GPT-5.6 Sol	Leader
LiveBench coding	Not measured	89.7%	Only GPT-5.6 Sol measured
LMArena WebDev	1538	1616	GPT-5.6 Sol
AA Coding Index	69.2	77.4	GPT-5.6 Sol
LiveCodeBench (Vals AI)	88.1%	82.6%	Gemini 3.6 Flash
SWE-bench Verified (Vals AI)	79.6%	96.2%	GPT-5.6 Sol
Terminal-Bench 4 (Vals AI)	Not measured	37.9%	Only GPT-5.6 Sol measured
Vibe Code Bench (Vals AI)	64.0%	80.5%	GPT-5.6 Sol
Code migration (Vals AI)	30.9%	52.9%	GPT-5.6 Sol
ProgramBench (Vals AI)	0.0%	1.5%	GPT-5.6 Sol
IOI (Vals AI)	35.1%	91.2%	GPT-5.6 Sol
Terminal-Bench 2.1 (Vals AI)	73.8%	85.8%	GPT-5.6 Sol
Vibe Code Bench 1-100 (Vals AI)	Not measured	20.0%	Only GPT-5.6 Sol measured
SkillsBench (Vals AI)	Not measured	54.1%	Only GPT-5.6 Sol measured
CyberBench (Vals AI)	45.4%	76.3%	GPT-5.6 Sol
SciCode	53.4%	57.8%	GPT-5.6 Sol
Terminal-Bench Hard (Artificial Analysis run)	Not measured	65.9%	Only GPT-5.6 Sol measured
FrontierCode	34.4%	47.5%	GPT-5.6 Sol
FrontierSWE	Not measured	32.2%	Only GPT-5.6 Sol measured
GSO-Bench	Not measured	76.5%	Only GPT-5.6 Sol measured
WeirdML	56.1%	88.8%	GPT-5.6 Sol
MirrorCode	Not measured	20.0%	Only GPT-5.6 Sol measured
ALE-Bench	715.5	2176.9	GPT-5.6 Sol
LMArena Text: coding	1491	1496	GPT-5.6 Sol
LMArena Text: software and IT	1492	1489	Gemini 3.6 Flash
LMArena WebDev: React	1528	1620	GPT-5.6 Sol
LMArena WebDev: HTML	1547	1618	GPT-5.6 Sol
LMArena WebDev: from an image	1545	1609	GPT-5.6 Sol
SciCode	10.8%	10.8%	Tie
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 3.6 Flash and GPT-5.6 Sol
Benchmark	Gemini 3.6 Flash	GPT-5.6 Sol	Leader
AA Intelligence Index	34.0	47.0	GPT-5.6 Sol
Epoch Capabilities Index	154.5	162.8	GPT-5.6 Sol

Agentic workLike-for-likePublic boards, shown as published, kept out of the index113

Agentic work benchmarks for Gemini 3.6 Flash and GPT-5.6 Sol
Benchmark	Gemini 3.6 Flash	GPT-5.6 Sol	Leader
AA Agentic Index	29.0	50.2	GPT-5.6 Sol
Design Arena: agents building web apps	1197	1245	GPT-5.6 Sol
Design Arena: agents building full-stack apps	1181	1261	GPT-5.6 Sol
Design Arena: agents building mobile apps	1199	1248	GPT-5.6 Sol
Design Arena: agents building Android apps	1203	1239	GPT-5.6 Sol
Design Arena: agents building Godot games	Not measured	1260	Only GPT-5.6 Sol measured
Design Arena: agents building games	1181	1267	GPT-5.6 Sol
Design Arena: HTML slides	1148	1286	GPT-5.6 Sol
Design Arena: PowerPoint slides	1146	1241	GPT-5.6 Sol
Design Arena: spreadsheets	1034	Not measured	Only Gemini 3.6 Flash measured
LiveBench research	Not measured	54.5%	Only GPT-5.6 Sol measured
LiveBench browser	Not measured	88.0%	Only GPT-5.6 Sol measured
LiveBench long agentic	Not measured	48.5%	Only GPT-5.6 Sol measured
SREBench (Vals AI)	Not measured	30.5%	Only GPT-5.6 Sol measured
Finance Agent (Vals AI)	56.3%	53.8%	Gemini 3.6 Flash
Tax Agent Bench (Vals AI)	18.0%	31.1%	GPT-5.6 Sol
Legal Research Bench (Vals AI)	25.0%	48.1%	GPT-5.6 Sol
Excel Modeling Benchmark (Vals AI)	65.4%	72.3%	GPT-5.6 Sol
BioMysteryBench (Vals AI)	58.5%	71.1%	GPT-5.6 Sol
Terminal-Bench-Science (Vals AI)	4.3%	20.0%	GPT-5.6 Sol
Vals Science Index	Not measured	41.5%	Only GPT-5.6 Sol measured
Time Horizon Index (Vals AI)	Not measured	23.8%	Only GPT-5.6 Sol measured
CUA-bench (Vals AI)	Not measured	8.3%	Only GPT-5.6 Sol measured
τ²-Bench Telecom	Not measured	85.1%	Only GPT-5.6 Sol measured
APEX-Agents	46.9%	Not measured	Only Gemini 3.6 Flash measured
OSWorld 2.0	Not measured	27.3%	Only GPT-5.6 Sol measured
BALROG	Not measured	60.0%	Only GPT-5.6 Sol measured
PostTrainBench	Not measured	36.2%	Only GPT-5.6 Sol measured
Vending-Bench 2	Not measured	$9,619	Only GPT-5.6 Sol measured
LMArena Search	Not measured	1261	Only GPT-5.6 Sol measured
Neither model has a result on the other 11 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index59

Reasoning benchmarks for Gemini 3.6 Flash and GPT-5.6 Sol
Benchmark	Gemini 3.6 Flash	GPT-5.6 Sol	Leader
GPQA Diamond	93.4%	95.2%	GPT-5.6 Sol
LiveBench reasoning	Not measured	91.7%	Only GPT-5.6 Sol measured
MysteryMechanism (Vals AI)	Not measured	33.3%	Only GPT-5.6 Sol measured
MMMU Pro (Vals AI)	88.4%	88.8%	GPT-5.6 Sol
GPQA Diamond (Artificial Analysis run)	92.8%	94.1%	GPT-5.6 Sol
Humanity's Last Exam (Artificial Analysis run)	40.8%	49.5%	GPT-5.6 Sol
GPQA Diamond (Epoch AI run)	94.1%	93.5%	Gemini 3.6 Flash
ARC-AGI	91.2%	97.5%	GPT-5.6 Sol
ARC-AGI-2	60.4%	92.5%	GPT-5.6 Sol
SimpleBench	Not measured	64.8%	Only GPT-5.6 Sol measured
Chess puzzles	42.0%	59.0%	GPT-5.6 Sol
Mystery game puzzles	30.0%	58.0%	GPT-5.6 Sol
EBR-bench	Not measured	44.8%	Only GPT-5.6 Sol measured
LMArena Text: hard prompts	1486	1483	Gemini 3.6 Flash
LMArena Text: expert	1490	1517	GPT-5.6 Sol
LMArena Text: hard prompts in English	1484	1477	Gemini 3.6 Flash
LMArena Vision: diagrams	1314	1305	Gemini 3.6 Flash
LMArena Vision: homework	1314	1305	Gemini 3.6 Flash
Kagi LLM Benchmark	Not measured	67.0%	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 3.6 Flash and GPT-5.6 Sol
Benchmark	Gemini 3.6 Flash	GPT-5.6 Sol	Leader
OTIS Mock AIME	94.2%	100.0%	GPT-5.6 Sol
FrontierMath, tiers 1 to 3	58.9%	89.1%	GPT-5.6 Sol
FrontierMath, tier 4	22.0%	82.9%	GPT-5.6 Sol
ProofBench	36.0%	83.0%	GPT-5.6 Sol
LMArena Text: math	1508	1473	Gemini 3.6 Flash
LMArena Text: mathematical work	1492	1496	GPT-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 3.6 Flash and GPT-5.6 Sol
Benchmark	Gemini 3.6 Flash	GPT-5.6 Sol	Leader
MMLU Pro	89.3%	89.1%	Gemini 3.6 Flash
LegalBench (Vals AI)	86.7%	87.0%	GPT-5.6 Sol
CorpFin (Vals AI)	63.3%	64.4%	GPT-5.6 Sol
TaxEval (Vals AI)	74.9%	74.8%	Gemini 3.6 Flash
MortgageTax (Vals AI)	67.6%	67.3%	Gemini 3.6 Flash
MedCode (Vals AI)	53.1%	44.0%	Gemini 3.6 Flash
SimpleQA Verified	66.2%	69.7%	GPT-5.6 Sol
LMArena Text: science	1489	1455	Gemini 3.6 Flash
LMArena Text: medicine and healthcare	1469	1431	Gemini 3.6 Flash
LMArena Text: legal and government	1479	1476	Gemini 3.6 Flash
LMArena Text: business and finance	1464	1457	Gemini 3.6 Flash
FACTS Benchmark Suite	65.3%	52.4%	Gemini 3.6 Flash
Neither model has a result on the other 6 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index07

Web design benchmarks for Gemini 3.6 Flash and GPT-5.6 Sol
Benchmark	Gemini 3.6 Flash	GPT-5.6 Sol	Leader
Design Arena: websites	1300	1321	GPT-5.6 Sol
Design Arena: UI components	1297	1336	GPT-5.6 Sol
Design Arena: game dev	1263	1368	GPT-5.6 Sol
Design Arena: data viz	1289	1322	GPT-5.6 Sol
Design Arena: 3D	1272	1386	GPT-5.6 Sol
Design Arena: all code categories	1290	1341	GPT-5.6 Sol
Design Arena: SVG	Not measured	1316	Only GPT-5.6 Sol measured
Design Arena: ASCII art	1281	1304	GPT-5.6 Sol
LiveBench design	Not measured	81.6%	Only GPT-5.6 Sol measured

GeneralLike-for-likePublic boards, shown as published, kept out of the index1913

General benchmarks for Gemini 3.6 Flash and GPT-5.6 Sol
Benchmark	Gemini 3.6 Flash	GPT-5.6 Sol	Leader
LiveBench data analysis	Not measured	79.8%	Only GPT-5.6 Sol measured
Public Benefits Bench (Vals AI)	56.6%	66.5%	GPT-5.6 Sol
SAGE (Vals AI)	48.7%	52.6%	GPT-5.6 Sol
MedScribe (Vals AI)	79.7%	85.2%	GPT-5.6 Sol
Vals Multimodal Index	65.1%	72.6%	GPT-5.6 Sol
IFBench	Not measured	72.7%	Only GPT-5.6 Sol measured
Long context reasoning (AA-LCR)	80.0%	84.0%	GPT-5.6 Sol
DTBench	95.5%	95.7%	GPT-5.6 Sol
LMCA	44.9%	58.5%	GPT-5.6 Sol
Surface Evolver Bench	Not measured	93.1%	Only GPT-5.6 Sol measured
Furniture assembly	23.3%	56.7%	GPT-5.6 Sol
LMArena Text	1480	1457	Gemini 3.6 Flash
LMArena Text: instruction following	1467	1483	GPT-5.6 Sol
LMArena Text: creative writing	1466	1449	Gemini 3.6 Flash
LMArena Vision	1299	1281	Gemini 3.6 Flash
LMArena Text: English	1482	1457	Gemini 3.6 Flash
LMArena Text: non-English	1470	1451	Gemini 3.6 Flash
LMArena Text: Chinese	1531	1525	Gemini 3.6 Flash
LMArena Text: German	1480	1476	Gemini 3.6 Flash
LMArena Text: Japanese	1475	1472	Gemini 3.6 Flash
LMArena Text: Korean	1429	1442	GPT-5.6 Sol
LMArena Text: French	1503	1472	Gemini 3.6 Flash
LMArena Text: Spanish	1476	1443	Gemini 3.6 Flash
LMArena Text: Russian	1488	1471	Gemini 3.6 Flash
LMArena Text: Polish	1473	1459	Gemini 3.6 Flash
LMArena Text: multi-turn	1481	1461	Gemini 3.6 Flash
LMArena Text: longer queries	1475	1481	GPT-5.6 Sol
LMArena Text: writing and language	1470	1462	Gemini 3.6 Flash
LMArena Text: entertainment and media	1456	1450	Gemini 3.6 Flash
LMArena Vision: English	1295	1280	Gemini 3.6 Flash
LMArena Vision: Chinese	1357	1313	Gemini 3.6 Flash
LMArena Vision: reading text	1312	1290	Gemini 3.6 Flash
LMArena Vision: humor	1289	1246	Gemini 3.6 Flash
LMArena Vision: creative writing	1278	1283	GPT-5.6 Sol
LMArena Document	1456	1483	GPT-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 3.6 Flash and GPT-5.6 Sol
Benchmark	Gemini 3.6 Flash	GPT-5.6 Sol	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 3.6 Flash$0.0026Fits in one request

GPT-5.6 Sol$0.014Fits in one request

Gemini 3.6 Flash costs less

Repository review

50K input + 3K output tokens

Gemini 3.6 Flash$0.049Fits in one request

GPT-5.6 Sol$0.260Fits in one request

Gemini 3.6 Flash costs less

Agent loop

200K input + 10K output tokens

Gemini 3.6 Flash$0.188Fits in one request

GPT-5.6 Sol$1.00Fits in one request

Gemini 3.6 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 3.6 Flash and GPT-5.6 Sol
Specification	Google Gemini 3.6 Flash	OpenAI GPT-5.6 Sol
Maker	Google	OpenAI
Weights	No public weights listed	No public weights listed
Released	July 21, 2026	July 9, 2026
Context window	1.05M	1.05M
Input $/Mtok	$0.75	$4.00
Output $/Mtok	$3.75	$20.00
Coding index	62.5	73.5
Independent measurements behind it	4	6
Cost per solved task	$1.49	$1.20
Source	OpenRouter listing	OpenRouter listing

Questions

Is Gemini 3.6 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 62.5 for Gemini 3.6 Flash. Pick Gemini 3.6 Flash for output tokens 5.3x cheaper ($3.75 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 3.6 Flash or GPT-5.6 Sol?

Gemini 3.6 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 3.6 Flash costs $1.49 and GPT-5.6 Sol $1.20.

Which has the bigger context window, Gemini 3.6 Flash or GPT-5.6 Sol?

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

How many benchmarks is the Gemini 3.6 Flash vs GPT-5.6 Sol comparison based on?

107 benchmarks have a result for both models as of October 21, 2026. Gemini 3.6 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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<iframe src="https://llmbenchmark.ai/embed/compare/gemini-3-6-flash-vs-gpt-5-6-sol" title="Gemini 3.6 Flash vs GPT-5.6 Sol, LLMBenchmark.ai" width="100%" height="360" style="border:0;max-width:100%" loading="lazy"></iframe>
<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/gemini-3-6-flash-vs-gpt-5-6-sol">LLMBenchmark.ai, Gemini 3.6 Flash vs GPT-5.6 Sol</a></p>

Gemini 3.6 Flash or GPT-5.6 Sol: which to pick

Which one for your work

Coding

Intelligence

Agentic work

Reasoning

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