DeepSeek V4.1 Flash vs GPT-5.6 Sol

GPT-5.6 Sol leads DeepSeek V4.1 Flash on coding, 73.5 to 62.5 on the Vibe Coding Index, and DeepSeek V4.1 Flash costs 25.6x less per output token ($0.78 vs $20.00 per million). Updated October 21, 2026; 99 benchmarks have a result for both.

Replace DeepSeek V4.1 Flash Replace GPT-5.6 Sol

DeepSeek V4.1 FlashDeepSeek

Coding: 62.5
Overall: 62.4
Output $/M: $0.78

GPT-5.6 SolOpenAI

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

vs boards won of 99 shared, 1 tied

DeepSeek V4.1 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 DeepSeek V4.1 Flash.

Pick DeepSeek V4.1 Flash for output tokens 25.6x cheaper ($0.78 vs $20.00 per million) and work measured by LiveBench coding or LMArena WebDev, where it leads.

Model ADeepSeek V4.1 FlashDeepSeek62.5/1005 independent benchmarksNo cost per solved task Model BGPT-5.6 SolOpenAI73.5/1006 independent benchmarks$1.20 per solved task

99240

Shared results: 99
DeepSeek V4.1 Flash only: 2
GPT-5.6 Sol only: 40
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.
5 and 6 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.6 Sol
GPT-5.6 Sol leads, 47.0 against 24.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GPT-5.6 Sol has a AA Agentic Index result, 50.2.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.6 Sol has a GPQA Diamond result, 95.2%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.6 Sol has a MMLU Pro result, 89.1%.
Directional only
Web design
Design Arena: websites
GPT-5.6 Sol
GPT-5.6 Sol leads, 1321 against 1309.
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
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash $0.0006, GPT-5.6 Sol $0.014 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.6 Sol has the number, $1.20.
Directional only

At a glance

Coding index: DeepSeek V4.1 Flash 62.5 from 5 benchmarks, GPT-5.6 Sol 73.5 from 6 benchmarks.
Widest category: Math, 18.5 points apart on average over 6 shared benchmarks.
Widest single gap: FrontierMath, tier 4: DeepSeek V4.1 Flash 26.8% against GPT-5.6 Sol 82.9%, 56.1 points apart on a 0 to 100 scale.
Output price: DeepSeek V4.1 Flash $0.78 against GPT-5.6 Sol $20.00 per million output tokens, 25.6x apart.
Context window: DeepSeek V4.1 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	DeepSeek V4.1 Flash	GPT-5.6 Sol	Ties
Coding	7	12
Intelligence	0	2
Agentic work	1	10
Reasoning	2	11
Math	1	5
Knowledge	3	4
Web design	0	9
General	7	24	1 tie
All shared boards	21	77	1 tie

CodingLike-for-likeSix of these boards feed the Vibe Coding Index712

Coding benchmarks for DeepSeek V4.1 Flash and GPT-5.6 Sol
Benchmark	DeepSeek V4.1 Flash	GPT-5.6 Sol	Leader
LiveBench coding	90.2%	89.7%	DeepSeek V4.1 Flash
LMArena WebDev	1618	1616	DeepSeek V4.1 Flash
AA Coding Index	Not measured	77.4	Only GPT-5.6 Sol measured
LiveCodeBench (Vals AI)	Not measured	82.6%	Only GPT-5.6 Sol measured
SWE-bench Verified (Vals AI)	Not measured	96.2%	Only GPT-5.6 Sol measured
Terminal-Bench 4 (Vals AI)	19.7%	37.9%	GPT-5.6 Sol
Vibe Code Bench (Vals AI)	84.7%	80.5%	DeepSeek V4.1 Flash
Code migration (Vals AI)	45.6%	52.9%	GPT-5.6 Sol
ProgramBench (Vals AI)	0.5%	1.5%	GPT-5.6 Sol
IOI (Vals AI)	40.3%	91.2%	GPT-5.6 Sol
Terminal-Bench 2.1 (Vals AI)	74.5%	85.8%	GPT-5.6 Sol
SWE-bench Multimodal (Vals AI)	46.5%	Not measured	Only DeepSeek V4.1 Flash measured
Vibe Code Bench 1-100 (Vals AI)	16.4%	20.0%	GPT-5.6 Sol
SkillsBench (Vals AI)	69.8%	54.1%	DeepSeek V4.1 Flash
CyberBench (Vals AI)	73.7%	76.3%	GPT-5.6 Sol
SciCode	51.9%	57.8%	GPT-5.6 Sol
Terminal-Bench Hard (Artificial Analysis run)	Not measured	65.9%	Only GPT-5.6 Sol measured
FrontierCode	43.9%	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	Not measured	88.8%	Only GPT-5.6 Sol measured
MirrorCode	Not measured	20.0%	Only GPT-5.6 Sol measured
ALE-Bench	1092.3	2176.9	GPT-5.6 Sol
LMArena Text: coding	1502	1496	DeepSeek V4.1 Flash
LMArena Text: software and IT	1496	1489	DeepSeek V4.1 Flash
LMArena WebDev: React	1628	1620	DeepSeek V4.1 Flash
LMArena WebDev: HTML	1612	1618	GPT-5.6 Sol
LMArena WebDev: from an image	1606	1609	GPT-5.6 Sol
SciCode	Not measured	10.8%	Only GPT-5.6 Sol measured
Neither model has a result on the other 10 boards here

IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02

Intelligence benchmarks for DeepSeek V4.1 Flash and GPT-5.6 Sol
Benchmark	DeepSeek V4.1 Flash	GPT-5.6 Sol	Leader
AA Intelligence Index	24.7	47.0	GPT-5.6 Sol
Epoch Capabilities Index	155.9	162.8	GPT-5.6 Sol

Agentic workLike-for-likePublic boards, shown as published, kept out of the index110

Agentic work benchmarks for DeepSeek V4.1 Flash and GPT-5.6 Sol
Benchmark	DeepSeek V4.1 Flash	GPT-5.6 Sol	Leader
AA Agentic Index	Not measured	50.2	Only GPT-5.6 Sol measured
Design Arena: agents building web apps	Not measured	1245	Only GPT-5.6 Sol measured
Design Arena: agents building full-stack apps	Not measured	1261	Only GPT-5.6 Sol measured
Design Arena: agents building mobile apps	Not measured	1248	Only GPT-5.6 Sol measured
Design Arena: agents building Android apps	Not measured	1239	Only GPT-5.6 Sol measured
Design Arena: agents building Godot games	Not measured	1260	Only GPT-5.6 Sol measured
Design Arena: agents building games	Not measured	1267	Only GPT-5.6 Sol measured
Design Arena: HTML slides	Not measured	1286	Only GPT-5.6 Sol measured
Design Arena: PowerPoint slides	Not measured	1241	Only GPT-5.6 Sol measured
LiveBench research	86.8%	54.5%	DeepSeek V4.1 Flash
LiveBench browser	84.0%	88.0%	GPT-5.6 Sol
LiveBench long agentic	40.1%	48.5%	GPT-5.6 Sol
SREBench (Vals AI)	0.8%	30.5%	GPT-5.6 Sol
Finance Agent (Vals AI)	53.5%	53.8%	GPT-5.6 Sol
Tax Agent Bench (Vals AI)	26.6%	31.1%	GPT-5.6 Sol
Legal Research Bench (Vals AI)	41.4%	48.1%	GPT-5.6 Sol
Excel Modeling Benchmark (Vals AI)	57.2%	72.3%	GPT-5.6 Sol
BioMysteryBench (Vals AI)	67.8%	71.1%	GPT-5.6 Sol
Terminal-Bench-Science (Vals AI)	0.0%	20.0%	GPT-5.6 Sol
Vals Science Index	29.6%	41.5%	GPT-5.6 Sol
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	39.5%	Not measured	Only DeepSeek V4.1 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 12 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index211

Reasoning benchmarks for DeepSeek V4.1 Flash and GPT-5.6 Sol
Benchmark	DeepSeek V4.1 Flash	GPT-5.6 Sol	Leader
GPQA Diamond	Not measured	95.2%	Only GPT-5.6 Sol measured
LiveBench reasoning	86.7%	91.7%	GPT-5.6 Sol
MysteryMechanism (Vals AI)	21.2%	33.3%	GPT-5.6 Sol
MMMU Pro (Vals AI)	Not measured	88.8%	Only GPT-5.6 Sol measured
GPQA Diamond (Artificial Analysis run)	Not measured	94.1%	Only GPT-5.6 Sol measured
Humanity's Last Exam (Artificial Analysis run)	39.2%	49.5%	GPT-5.6 Sol
GPQA Diamond (Epoch AI run)	89.8%	93.5%	GPT-5.6 Sol
ARC-AGI	94.5%	97.5%	GPT-5.6 Sol
ARC-AGI-2	72.9%	92.5%	GPT-5.6 Sol
SimpleBench	Not measured	64.8%	Only GPT-5.6 Sol measured
Chess puzzles	48.0%	59.0%	GPT-5.6 Sol
Mystery game puzzles	43.0%	58.0%	GPT-5.6 Sol
EBR-bench	Not measured	44.8%	Only GPT-5.6 Sol measured
LMArena Text: hard prompts	1483	1483	GPT-5.6 Sol
LMArena Text: expert	1501	1517	GPT-5.6 Sol
LMArena Text: hard prompts in English	1488	1477	DeepSeek V4.1 Flash
LMArena Vision: diagrams	1297	1305	GPT-5.6 Sol
LMArena Vision: homework	1307	1305	DeepSeek V4.1 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 DeepSeek V4.1 Flash and GPT-5.6 Sol
Benchmark	DeepSeek V4.1 Flash	GPT-5.6 Sol	Leader
OTIS Mock AIME	98.3%	100.0%	GPT-5.6 Sol
FrontierMath, tiers 1 to 3	67.4%	89.1%	GPT-5.6 Sol
FrontierMath, tier 4	26.8%	82.9%	GPT-5.6 Sol
ProofBench	54.0%	83.0%	GPT-5.6 Sol
LMArena Text: math	1478	1473	DeepSeek V4.1 Flash
LMArena Text: mathematical work	1485	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 index34

Knowledge benchmarks for DeepSeek V4.1 Flash and GPT-5.6 Sol
Benchmark	DeepSeek V4.1 Flash	GPT-5.6 Sol	Leader
MMLU Pro	Not measured	89.1%	Only GPT-5.6 Sol measured
LegalBench (Vals AI)	83.3%	87.0%	GPT-5.6 Sol
CorpFin (Vals AI)	Not measured	64.4%	Only GPT-5.6 Sol measured
TaxEval (Vals AI)	Not measured	74.8%	Only GPT-5.6 Sol measured
MortgageTax (Vals AI)	Not measured	67.3%	Only GPT-5.6 Sol measured
MedCode (Vals AI)	41.2%	44.0%	GPT-5.6 Sol
SimpleQA Verified	42.9%	69.7%	GPT-5.6 Sol
LMArena Text: science	1469	1455	DeepSeek V4.1 Flash
LMArena Text: medicine and healthcare	1484	1431	DeepSeek V4.1 Flash
LMArena Text: legal and government	1443	1476	GPT-5.6 Sol
LMArena Text: business and finance	1461	1457	DeepSeek V4.1 Flash
FACTS Benchmark Suite	Not measured	52.4%	Only GPT-5.6 Sol measured
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 DeepSeek V4.1 Flash and GPT-5.6 Sol
Benchmark	DeepSeek V4.1 Flash	GPT-5.6 Sol	Leader
Design Arena: websites	1309	1321	GPT-5.6 Sol
Design Arena: UI components	1315	1336	GPT-5.6 Sol
Design Arena: game dev	1330	1368	GPT-5.6 Sol
Design Arena: data viz	1290	1322	GPT-5.6 Sol
Design Arena: 3D	1356	1386	GPT-5.6 Sol
Design Arena: all code categories	1320	1341	GPT-5.6 Sol
Design Arena: SVG	1244	1316	GPT-5.6 Sol
Design Arena: ASCII art	1265	1304	GPT-5.6 Sol
LiveBench design	79.2%	81.6%	GPT-5.6 Sol

GeneralLike-for-likePublic boards, shown as published, kept out of the index724

General benchmarks for DeepSeek V4.1 Flash and GPT-5.6 Sol
Benchmark	DeepSeek V4.1 Flash	GPT-5.6 Sol	Leader
LiveBench data analysis	79.3%	79.8%	GPT-5.6 Sol
Public Benefits Bench (Vals AI)	64.3%	66.5%	GPT-5.6 Sol
SAGE (Vals AI)	47.9%	52.6%	GPT-5.6 Sol
MedScribe (Vals AI)	85.5%	85.2%	DeepSeek V4.1 Flash
Vals Multimodal Index	Not measured	72.6%	Only GPT-5.6 Sol measured
IFBench	Not measured	72.7%	Only GPT-5.6 Sol measured
Long context reasoning (AA-LCR)	84.0%	84.0%	Tie
DTBench	89.9%	95.7%	GPT-5.6 Sol
LMCA	47.0%	58.5%	GPT-5.6 Sol
Surface Evolver Bench	46.3%	93.1%	GPT-5.6 Sol
Furniture assembly	34.2%	56.7%	GPT-5.6 Sol
LMArena Text	1464	1457	DeepSeek V4.1 Flash
LMArena Text: instruction following	1473	1483	GPT-5.6 Sol
LMArena Text: creative writing	1437	1449	GPT-5.6 Sol
LMArena Vision	1278	1281	GPT-5.6 Sol
LMArena Text: English	1473	1457	DeepSeek V4.1 Flash
LMArena Text: non-English	1449	1451	GPT-5.6 Sol
LMArena Text: Chinese	1501	1525	GPT-5.6 Sol
LMArena Text: German	1470	1476	GPT-5.6 Sol
LMArena Text: Japanese	1432	1472	GPT-5.6 Sol
LMArena Text: Korean	1436	1442	GPT-5.6 Sol
LMArena Text: French	1465	1472	GPT-5.6 Sol
LMArena Text: Spanish	1459	1443	DeepSeek V4.1 Flash
LMArena Text: Russian	1467	1471	GPT-5.6 Sol
LMArena Text: Polish	1457	1459	GPT-5.6 Sol
LMArena Text: multi-turn	1461	1461	GPT-5.6 Sol
LMArena Text: longer queries	1473	1481	GPT-5.6 Sol
LMArena Text: writing and language	1450	1462	GPT-5.6 Sol
LMArena Text: entertainment and media	1445	1450	GPT-5.6 Sol
LMArena Vision: English	1288	1280	DeepSeek V4.1 Flash
LMArena Vision: Chinese	1327	1313	DeepSeek V4.1 Flash
LMArena Vision: reading text	1282	1290	GPT-5.6 Sol
LMArena Vision: humor	1260	1246	DeepSeek V4.1 Flash
LMArena Vision: creative writing	1275	1283	GPT-5.6 Sol
LMArena Document	Not measured	1483	Only 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 DeepSeek V4.1 Flash and GPT-5.6 Sol
Benchmark	DeepSeek V4.1 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

DeepSeek V4.1 Flash$0.0006Fits in one request

GPT-5.6 Sol$0.014Fits in one request

DeepSeek V4.1 Flash costs less

Repository review

50K input + 3K output tokens

DeepSeek V4.1 Flash$0.013Fits in one request

GPT-5.6 Sol$0.260Fits in one request

DeepSeek V4.1 Flash costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4.1 Flash$0.050Fits in one request

GPT-5.6 Sol$1.00Fits in one request

DeepSeek V4.1 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 DeepSeek V4.1 Flash and GPT-5.6 Sol
Specification	DeepSeek V4.1 Flash	OpenAI GPT-5.6 Sol
Maker	DeepSeek	OpenAI
Weights	Open weights	No public weights listed
Released	September 10, 2026	July 9, 2026
Context window	1.05M	1.05M
Input $/Mtok	$0.21	$4.00
Output $/Mtok	$0.78	$20.00
Coding index	62.5	73.5
Independent measurements behind it	5	6
Cost per solved task	Not computable	$1.20
Source	OpenRouter listing	OpenRouter listing

Questions

Is DeepSeek V4.1 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 DeepSeek V4.1 Flash. Pick DeepSeek V4.1 Flash for output tokens 25.6x cheaper ($0.78 vs $20.00 per million) and work measured by LiveBench coding or LMArena WebDev, where it leads.

Which is cheaper, DeepSeek V4.1 Flash or GPT-5.6 Sol?

DeepSeek V4.1 Flash costs $0.21 per million input tokens and $0.78 per million output tokens; GPT-5.6 Sol costs $4.00 and $20.00. Cost per solved task is known for GPT-5.6 Sol only.

Which has the bigger context window, DeepSeek V4.1 Flash or GPT-5.6 Sol?

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

How many benchmarks is the DeepSeek V4.1 Flash vs GPT-5.6 Sol comparison based on?

99 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4.1 Flash's coding index rests on 5 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.

Compare next

Embed this comparison on your site

Free under CC BY 4.0, while the source link stays visible. Preview the frame · Terms and other embeds

<iframe src="https://llmbenchmark.ai/embed/compare/deepseek-v4-1-flash-vs-gpt-5-6-sol" title="DeepSeek V4.1 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/deepseek-v4-1-flash-vs-gpt-5-6-sol">LLMBenchmark.ai, DeepSeek V4.1 Flash vs GPT-5.6 Sol</a></p>

DeepSeek V4.1 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