DeepSeek V4.1 Flash vs GPT-5.5

GPT-5.5 leads DeepSeek V4.1 Flash on coding, 71.3 to 62.5 on the Vibe Coding Index, and DeepSeek V4.1 Flash costs 35.7x less per output token ($0.84 vs $30.00 per million). Updated October 22, 2026; 92 benchmarks have a result for both.

Replace DeepSeek V4.1 Flash Replace GPT-5.5

DeepSeek V4.1 FlashDeepSeek

Coding: 62.5
Overall: 62.4
Output $/M: $0.84

GPT-5.5OpenAI

Coding: 71.3
Overall: 69.2
Output $/M: $30.00

vs boards won of 92 shared, 1 tied

DeepSeek V4.1 Flash or GPT-5.5: which to pick

Pick GPT-5.5 for coding: it scores 71.3 on the Vibe Coding Index against 62.5 for DeepSeek V4.1 Flash.

Pick DeepSeek V4.1 Flash for output tokens 35.7x cheaper ($0.84 vs $30.00 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-5.5OpenAI71.3/1005 independent benchmarks$1.65 per solved task

92942

Shared results: 92
DeepSeek V4.1 Flash only: 9
GPT-5.5 only: 42
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-5.5
GPT-5.5 scores 71.3 against 62.5.
5 benchmarks behind each score
Intelligence
AA Intelligence Index
GPT-5.5
GPT-5.5 leads, 38.4 against 24.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GPT-5.5 has a AA Agentic Index result, 36.4.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.5 has a GPQA Diamond result, 93.2%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.5 has a MMLU Pro result, 88.1%.
Directional only
Web design
Design Arena: websites
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash leads, 1308 against 1256.
Same board, both measured
Long documents
Listed context window
GPT-5.5
GPT-5.5 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-5.5 $0.020 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.5 has the number, $1.65.
Directional only

At a glance

Coding index: DeepSeek V4.1 Flash 62.5 from 5 benchmarks, GPT-5.5 71.3 from 5 benchmarks.
Widest category: Math, 14.1 points apart on average over 6 shared benchmarks.
Widest single gap: FrontierMath, tier 4: DeepSeek V4.1 Flash 26.8% against GPT-5.5 72.5%, 45.7 points apart on a 0 to 100 scale.
Output price: DeepSeek V4.1 Flash $0.84 against GPT-5.5 $30.00 per million output tokens, 35.7x apart.
Context window: DeepSeek V4.1 Flash takes 1.05M tokens, GPT-5.5 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-5.5	Ties
Coding	11	3	1 tie
Intelligence	0	2
Agentic work	5	4
Reasoning	1	11
Math	2	4
Knowledge	1	6
Web design	9	0
General	2	30
All shared boards	31	60	1 tie

CodingLike-for-likeSix of these boards feed the Vibe Coding Index113

Coding benchmarks for DeepSeek V4.1 Flash and GPT-5.5
Benchmark	DeepSeek V4.1 Flash	GPT-5.5	Leader
LiveBench coding	90.2%	85.1%	DeepSeek V4.1 Flash
LMArena WebDev	1618	1513	DeepSeek V4.1 Flash
AA Coding Index	Not measured	74.9	Only GPT-5.5 measured
LiveCodeBench (Vals AI)	Not measured	85.3%	Only GPT-5.5 measured
SWE-bench Verified (Vals AI)	Not measured	82.6%	Only GPT-5.5 measured
Terminal-Bench 4 (Vals AI)	19.7%	Not measured	Only DeepSeek V4.1 Flash measured
Vibe Code Bench (Vals AI)	84.7%	69.8%	DeepSeek V4.1 Flash
Code migration (Vals AI)	45.6%	45.2%	DeepSeek V4.1 Flash
ProgramBench (Vals AI)	0.5%	0.5%	Tie
IOI (Vals AI)	40.3%	Not measured	Only DeepSeek V4.1 Flash measured
Terminal-Bench 2.1 (Vals AI)	74.5%	76.4%	GPT-5.5
SWE-bench Multimodal (Vals AI)	46.5%	Not measured	Only DeepSeek V4.1 Flash measured
Vibe Code Bench 1-100 (Vals AI)	16.4%	Not measured	Only DeepSeek V4.1 Flash measured
SkillsBench (Vals AI)	69.8%	62.5%	DeepSeek V4.1 Flash
CyberBench (Vals AI)	73.7%	Not measured	Only DeepSeek V4.1 Flash measured
SciCode	51.9%	56.1%	GPT-5.5
Terminal-Bench Hard (Artificial Analysis run)	Not measured	60.6%	Only GPT-5.5 measured
Terminal-Bench	Not measured	84.7%	Only GPT-5.5 measured
FrontierCode	43.9%	43.0%	DeepSeek V4.1 Flash
GSO-Bench	Not measured	40.2%	Only GPT-5.5 measured
WeirdML	Not measured	84.9%	Only GPT-5.5 measured
MirrorCode	Not measured	10.0%	Only GPT-5.5 measured
ALE-Bench	1092.3	1943.0	GPT-5.5
LMArena Text: coding	1502	1494	DeepSeek V4.1 Flash
LMArena Text: software and IT	1496	1493	DeepSeek V4.1 Flash
LMArena WebDev: React	1628	1506	DeepSeek V4.1 Flash
LMArena WebDev: HTML	1612	1543	DeepSeek V4.1 Flash
LMArena WebDev: from an image	1606	1532	DeepSeek V4.1 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 DeepSeek V4.1 Flash and GPT-5.5
Benchmark	DeepSeek V4.1 Flash	GPT-5.5	Leader
AA Intelligence Index	24.7	38.4	GPT-5.5
Epoch Capabilities Index	155.9	159.7	GPT-5.5

Agentic workLike-for-likePublic boards, shown as published, kept out of the index54

Agentic work benchmarks for DeepSeek V4.1 Flash and GPT-5.5
Benchmark	DeepSeek V4.1 Flash	GPT-5.5	Leader
AA Agentic Index	Not measured	36.4	Only GPT-5.5 measured
Design Arena: agents building web apps	Not measured	1117	Only GPT-5.5 measured
Design Arena: agents building full-stack apps	Not measured	1089	Only GPT-5.5 measured
Design Arena: agents building mobile apps	Not measured	1142	Only GPT-5.5 measured
Design Arena: agents building Android apps	Not measured	1164	Only GPT-5.5 measured
Design Arena: agents building Godot games	Not measured	1173	Only GPT-5.5 measured
Design Arena: agents building games	Not measured	1179	Only GPT-5.5 measured
Design Arena: HTML slides	Not measured	1017	Only GPT-5.5 measured
Design Arena: PowerPoint slides	Not measured	1084	Only GPT-5.5 measured
LiveBench research	86.8%	61.6%	DeepSeek V4.1 Flash
LiveBench browser	84.0%	48.0%	DeepSeek V4.1 Flash
LiveBench long agentic	40.1%	44.6%	GPT-5.5
SREBench (Vals AI)	0.8%	3.8%	GPT-5.5
Finance Agent (Vals AI)	53.5%	51.8%	DeepSeek V4.1 Flash
Tax Agent Bench (Vals AI)	26.6%	21.8%	DeepSeek V4.1 Flash
Legal Research Bench (Vals AI)	41.4%	40.4%	DeepSeek V4.1 Flash
Excel Modeling Benchmark (Vals AI)	57.2%	64.5%	GPT-5.5
BioMysteryBench (Vals AI)	67.8%	Not measured	Only DeepSeek V4.1 Flash measured
Terminal-Bench-Science (Vals AI)	0.0%	Not measured	Only DeepSeek V4.1 Flash measured
Vals Science Index	29.6%	Not measured	Only DeepSeek V4.1 Flash measured
Time Horizon Index (Vals AI)	Not measured	8.3%	Only GPT-5.5 measured
τ²-Bench Telecom	Not measured	93.9%	Only GPT-5.5 measured
APEX-Agents	39.5%	55.1%	GPT-5.5
Remote Labor Index	Not measured	6.3%	Only GPT-5.5 measured
OSWorld 2.0	Not measured	13.0%	Only GPT-5.5 measured
DeepResearch Bench	Not measured	54.0%	Only GPT-5.5 measured
PostTrainBench	Not measured	27.2%	Only GPT-5.5 measured
Vending-Bench 2	Not measured	$7,524	Only GPT-5.5 measured
Neither model has a result on the other 13 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index111

Reasoning benchmarks for DeepSeek V4.1 Flash and GPT-5.5
Benchmark	DeepSeek V4.1 Flash	GPT-5.5	Leader
GPQA Diamond	Not measured	93.2%	Only GPT-5.5 measured
LiveBench reasoning	86.7%	89.7%	GPT-5.5
MysteryMechanism (Vals AI)	21.2%	Not measured	Only DeepSeek V4.1 Flash measured
MMMU Pro (Vals AI)	Not measured	88.3%	Only GPT-5.5 measured
GPQA Diamond (Artificial Analysis run)	Not measured	93.5%	Only GPT-5.5 measured
Humanity's Last Exam (Artificial Analysis run)	39.2%	45.8%	GPT-5.5
GPQA Diamond (Epoch AI run)	89.8%	90.7%	GPT-5.5
ARC-AGI	94.5%	95.0%	GPT-5.5
ARC-AGI-2	72.9%	85.0%	GPT-5.5
SimpleBench	Not measured	69.0%	Only GPT-5.5 measured
Chess puzzles	48.0%	63.0%	GPT-5.5
Mystery game puzzles	43.0%	56.0%	GPT-5.5
EBR-bench	Not measured	34.3%	Only GPT-5.5 measured
LMArena Text: hard prompts	1483	1489	GPT-5.5
LMArena Text: expert	1501	1507	GPT-5.5
LMArena Text: hard prompts in English	1488	1483	DeepSeek V4.1 Flash
LMArena Vision: diagrams	1297	1325	GPT-5.5
LMArena Vision: homework	1307	1332	GPT-5.5
Kagi LLM Benchmark	Not measured	88.8%	Only GPT-5.5 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 DeepSeek V4.1 Flash and GPT-5.5
Benchmark	DeepSeek V4.1 Flash	GPT-5.5	Leader
OTIS Mock AIME	98.3%	84.4%	DeepSeek V4.1 Flash
FrontierMath, tiers 1 to 3	67.4%	85.3%	GPT-5.5
FrontierMath, tier 4	26.8%	72.5%	GPT-5.5
ProofBench	54.0%	50.0%	DeepSeek V4.1 Flash
LMArena Text: math	1478	1487	GPT-5.5
LMArena Text: mathematical work	1485	1496	GPT-5.5
Neither model has a result on the other 7 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index16

Knowledge benchmarks for DeepSeek V4.1 Flash and GPT-5.5
Benchmark	DeepSeek V4.1 Flash	GPT-5.5	Leader
MMLU Pro	Not measured	88.1%	Only GPT-5.5 measured
LegalBench (Vals AI)	83.3%	86.5%	GPT-5.5
CaseLaw (Vals AI)	Not measured	66.2%	Only GPT-5.5 measured
CorpFin (Vals AI)	Not measured	68.4%	Only GPT-5.5 measured
TaxEval (Vals AI)	Not measured	75.0%	Only GPT-5.5 measured
MortgageTax (Vals AI)	Not measured	68.8%	Only GPT-5.5 measured
MedCode (Vals AI)	41.2%	49.1%	GPT-5.5
SimpleQA Verified	42.9%	63.0%	GPT-5.5
LMArena Text: science	1469	1485	GPT-5.5
LMArena Text: medicine and healthcare	1484	1465	DeepSeek V4.1 Flash
LMArena Text: legal and government	1443	1491	GPT-5.5
LMArena Text: business and finance	1461	1478	GPT-5.5
FACTS Benchmark Suite	Not measured	66.7%	Only GPT-5.5 measured
Neither model has a result on the other 5 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index90

Web design benchmarks for DeepSeek V4.1 Flash and GPT-5.5
Benchmark	DeepSeek V4.1 Flash	GPT-5.5	Leader
Design Arena: websites	1308	1256	DeepSeek V4.1 Flash
Design Arena: UI components	1312	1257	DeepSeek V4.1 Flash
Design Arena: game dev	1330	1293	DeepSeek V4.1 Flash
Design Arena: data viz	1291	1264	DeepSeek V4.1 Flash
Design Arena: 3D	1355	1197	DeepSeek V4.1 Flash
Design Arena: all code categories	1320	1260	DeepSeek V4.1 Flash
Design Arena: SVG	1245	1234	DeepSeek V4.1 Flash
Design Arena: ASCII art	1265	1255	DeepSeek V4.1 Flash
LiveBench design	79.2%	70.3%	DeepSeek V4.1 Flash

GeneralLike-for-likePublic boards, shown as published, kept out of the index230

General benchmarks for DeepSeek V4.1 Flash and GPT-5.5
Benchmark	DeepSeek V4.1 Flash	GPT-5.5	Leader
LiveBench data analysis	79.3%	81.6%	GPT-5.5
Public Benefits Bench (Vals AI)	64.3%	60.9%	DeepSeek V4.1 Flash
SAGE (Vals AI)	47.9%	51.5%	GPT-5.5
MedScribe (Vals AI)	85.5%	86.9%	GPT-5.5
Vals Multimodal Index	Not measured	68.1%	Only GPT-5.5 measured
IFBench	Not measured	75.9%	Only GPT-5.5 measured
Long context reasoning (AA-LCR)	84.0%	84.3%	GPT-5.5
DTBench	89.9%	96.0%	GPT-5.5
LMCA	47.0%	54.3%	GPT-5.5
Surface Evolver Bench	46.3%	88.1%	GPT-5.5
Furniture assembly	34.2%	44.2%	GPT-5.5
ExploitBench	Not measured	47.4%	Only GPT-5.5 measured
CL-bench Life	Not measured	22.2%	Only GPT-5.5 measured
LMArena Text	1464	1471	GPT-5.5
LMArena Text: instruction following	1473	1478	GPT-5.5
LMArena Text: creative writing	1437	1455	GPT-5.5
LMArena Vision	1278	1297	GPT-5.5
LMArena Text: English	1473	1470	DeepSeek V4.1 Flash
LMArena Text: non-English	1449	1467	GPT-5.5
LMArena Text: Chinese	1501	1534	GPT-5.5
LMArena Text: German	1470	1480	GPT-5.5
LMArena Text: Japanese	1432	1499	GPT-5.5
LMArena Text: Korean	1436	1460	GPT-5.5
LMArena Text: French	1465	1485	GPT-5.5
LMArena Text: Spanish	1459	1468	GPT-5.5
LMArena Text: Russian	1467	1473	GPT-5.5
LMArena Text: Polish	1457	1465	GPT-5.5
LMArena Text: multi-turn	1461	1476	GPT-5.5
LMArena Text: longer queries	1473	1484	GPT-5.5
LMArena Text: writing and language	1450	1466	GPT-5.5
LMArena Text: entertainment and media	1445	1449	GPT-5.5
LMArena Vision: English	1288	1292	GPT-5.5
LMArena Vision: Chinese	1327	1360	GPT-5.5
LMArena Vision: reading text	1282	1306	GPT-5.5
LMArena Vision: humor	1260	1293	GPT-5.5
LMArena Vision: recognizing things	Not measured	1231	Only GPT-5.5 measured
LMArena Vision: creative writing	1275	1301	GPT-5.5
LMArena Document	Not measured	1486	Only GPT-5.5 measured
Neither model has a result on the other 8 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-5.5
Benchmark	DeepSeek V4.1 Flash	GPT-5.5	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-5.5$0.020Fits in one request

DeepSeek V4.1 Flash costs less

Repository review

50K input + 3K output tokens

DeepSeek V4.1 Flash$0.014Fits in one request

GPT-5.5$0.340Fits in one request

DeepSeek V4.1 Flash costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4.1 Flash$0.053Fits in one request

GPT-5.5$1.30Fits 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-5.5
Specification	DeepSeek V4.1 Flash	OpenAI GPT-5.5
Maker	DeepSeek	OpenAI
Weights	Open weights	No public weights listed
Released	September 10, 2026	April 24, 2026
Context window	1.05M	1.05M
Input $/Mtok	$0.2225	$5.00
Output $/Mtok	$0.84	$30.00
Coding index	62.5	71.3
Independent measurements behind it	5	5
Cost per solved task	Not computable	$1.65
Source	OpenRouter listing	OpenRouter listing

Questions

Is DeepSeek V4.1 Flash better than GPT-5.5 for coding?

Pick GPT-5.5 for coding: it scores 71.3 on the Vibe Coding Index against 62.5 for DeepSeek V4.1 Flash. Pick DeepSeek V4.1 Flash for output tokens 35.7x cheaper ($0.84 vs $30.00 per million) and work measured by LiveBench coding or LMArena WebDev, where it leads.

Which is cheaper, DeepSeek V4.1 Flash or GPT-5.5?

DeepSeek V4.1 Flash costs $0.2225 per million input tokens and $0.84 per million output tokens; GPT-5.5 costs $5.00 and $30.00. Cost per solved task is known for GPT-5.5 only.

Which has the bigger context window, DeepSeek V4.1 Flash or GPT-5.5?

GPT-5.5, with 1.05M tokens against 1.05M.

How many benchmarks is the DeepSeek V4.1 Flash vs GPT-5.5 comparison based on?

92 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4.1 Flash's coding index rests on 5 independent benchmarks and GPT-5.5's on 5. Where only one model has a result, the page says so instead of naming a winner.

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DeepSeek V4.1 Flash or GPT-5.5: 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