DeepSeek V4.1 Flash 1017 vs GPT-5.5

GPT-5.5 leads DeepSeek V4.1 Flash 1017 on coding, 71.3 to 70.3 on the Vibe Coding Index, and DeepSeek V4.1 Flash 1017 costs 50.0x less per output token ($0.60 vs $30.00 per million). Updated October 22, 2026; 35 benchmarks have a result for both.

Replace DeepSeek V4.1 Flash 1017 Replace GPT-5.5

DeepSeek V4.1 Flash 1017DeepSeek

Coding: 70.3
Overall: 67.6
Output $/M: $0.60

GPT-5.5OpenAI

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

vs boards won of 35 shared

DeepSeek V4.1 Flash 1017 or GPT-5.5: which to pick

DeepSeek V4.1 Flash 1017 and GPT-5.5 are level for coding, 70.3 against 71.3 on the Vibe Coding Index, so price and context decide.

Pick DeepSeek V4.1 Flash 1017 for output tokens 50.0x cheaper ($0.60 vs $30.00 per million) and work measured by LiveBench coding or LMArena WebDev, where it leads. Pick GPT-5.5 for a larger context window (1.05M against 1.05M tokens) and work measured by Code migration (Vals AI) or SciCode, where it leads.

Model ADeepSeek V4.1 Flash 1017DeepSeek70.3/1004 independent benchmarksNo cost per solved task Model BGPT-5.5OpenAI71.3/1005 independent benchmarks$1.65 per solved task

35499

Shared results: 35
DeepSeek V4.1 Flash 1017 only: 4
GPT-5.5 only: 99
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
No call
Level: 70.3 against 71.3, inside 2 points.
4 and 5 benchmarks behind the scores
Intelligence
AA Intelligence Index
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 39.5 against 38.4.
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 1017
DeepSeek V4.1 Flash 1017 leads, 1311 against 1256.
Same board, both measured
General
Vals Index
No call
Only DeepSeek V4.1 Flash 1017 has a Vals Index result, 61.3%.
Directional only
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 1017
DeepSeek V4.1 Flash 1017 $0.0004, 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 1017 70.3 from 4 benchmarks, GPT-5.5 71.3 from 5 benchmarks.
Widest category: Knowledge, 20.5 points apart on average over 1 shared benchmark.
Widest single gap: LiveBench browser: DeepSeek V4.1 Flash 1017 76.0% against GPT-5.5 48.0%, 28.0 points apart on a 0 to 100 scale.
Output price: DeepSeek V4.1 Flash 1017 $0.60 against GPT-5.5 $30.00 per million output tokens, 50.0x apart.
Context window: DeepSeek V4.1 Flash 1017 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 1017	GPT-5.5
Coding	5	3
Intelligence	1	0
Agentic work	6	1
Reasoning	1	4
Math	1	2
Knowledge	0	1
Web design	7	1
General	0	2
All shared boards	21	14

CodingLike-for-likeSix of these boards feed the Vibe Coding Index53

Coding benchmarks for DeepSeek V4.1 Flash 1017 and GPT-5.5
Benchmark	DeepSeek V4.1 Flash 1017	GPT-5.5	Leader
LiveBench coding	96.0%	85.1%	DeepSeek V4.1 Flash 1017
LMArena WebDev	1664	1513	DeepSeek V4.1 Flash 1017
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)	31.8%	Not measured	Only DeepSeek V4.1 Flash 1017 measured
Vibe Code Bench (Vals AI)	87.7%	69.8%	DeepSeek V4.1 Flash 1017
Code migration (Vals AI)	44.2%	45.2%	GPT-5.5
ProgramBench (Vals AI)	Not measured	0.5%	Only GPT-5.5 measured
Terminal-Bench 2.1 (Vals AI)	Not measured	76.4%	Only GPT-5.5 measured
SkillsBench (Vals AI)	Not measured	62.5%	Only GPT-5.5 measured
SciCode	55.9%	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	Not measured	43.0%	Only GPT-5.5 measured
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	1622.0	1943.0	GPT-5.5
LMArena Text: coding	Not measured	1494	Only GPT-5.5 measured
LMArena Text: software and IT	Not measured	1493	Only GPT-5.5 measured
LMArena WebDev: React	1667	1506	DeepSeek V4.1 Flash 1017
LMArena WebDev: HTML	1687	1543	DeepSeek V4.1 Flash 1017
LMArena WebDev: from an image	Not measured	1532	Only GPT-5.5 measured
Neither model has a result on the other 15 boards here

IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10

Intelligence benchmarks for DeepSeek V4.1 Flash 1017 and GPT-5.5
Benchmark	DeepSeek V4.1 Flash 1017	GPT-5.5	Leader
AA Intelligence Index	39.5	38.4	DeepSeek V4.1 Flash 1017
Epoch Capabilities Index	Not measured	159.7	Only GPT-5.5 measured

Agentic workLike-for-likePublic boards, shown as published, kept out of the index61

Agentic work benchmarks for DeepSeek V4.1 Flash 1017 and GPT-5.5
Benchmark	DeepSeek V4.1 Flash 1017	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
Design Arena: spreadsheets	1245	Not measured	Only DeepSeek V4.1 Flash 1017 measured
LiveBench research	79.5%	61.6%	DeepSeek V4.1 Flash 1017
LiveBench browser	76.0%	48.0%	DeepSeek V4.1 Flash 1017
LiveBench long agentic	50.9%	44.6%	DeepSeek V4.1 Flash 1017
SREBench (Vals AI)	Not measured	3.8%	Only GPT-5.5 measured
Finance Agent (Vals AI)	57.0%	51.8%	DeepSeek V4.1 Flash 1017
Tax Agent Bench (Vals AI)	44.4%	21.8%	DeepSeek V4.1 Flash 1017
Legal Research Bench (Vals AI)	45.2%	40.4%	DeepSeek V4.1 Flash 1017
Harvey's Legal Agent Benchmark (Vals AI)	14.2%	Not measured	Only DeepSeek V4.1 Flash 1017 measured
Excel Modeling Benchmark (Vals AI)	61.8%	64.5%	GPT-5.5
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	Not measured	55.1%	Only GPT-5.5 measured
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 14 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index14

Reasoning benchmarks for DeepSeek V4.1 Flash 1017 and GPT-5.5
Benchmark	DeepSeek V4.1 Flash 1017	GPT-5.5	Leader
GPQA Diamond	Not measured	93.2%	Only GPT-5.5 measured
LiveBench reasoning	89.1%	89.7%	GPT-5.5
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)	43.3%	45.8%	GPT-5.5
GPQA Diamond (Epoch AI run)	93.2%	90.7%	DeepSeek V4.1 Flash 1017
ARC-AGI	Not measured	95.0%	Only GPT-5.5 measured
ARC-AGI-2	Not measured	85.0%	Only GPT-5.5 measured
SimpleBench	Not measured	69.0%	Only GPT-5.5 measured
Chess puzzles	46.0%	63.0%	GPT-5.5
Mystery game puzzles	49.0%	56.0%	GPT-5.5
EBR-bench	Not measured	34.3%	Only GPT-5.5 measured
LMArena Text: hard prompts	Not measured	1489	Only GPT-5.5 measured
LMArena Text: expert	Not measured	1507	Only GPT-5.5 measured
LMArena Text: hard prompts in English	Not measured	1483	Only GPT-5.5 measured
LMArena Vision: diagrams	Not measured	1325	Only GPT-5.5 measured
LMArena Vision: homework	Not measured	1332	Only GPT-5.5 measured
Kagi LLM Benchmark	Not measured	88.8%	Only GPT-5.5 measured
Neither model has a result on the other 4 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index12

Math benchmarks for DeepSeek V4.1 Flash 1017 and GPT-5.5
Benchmark	DeepSeek V4.1 Flash 1017	GPT-5.5	Leader
OTIS Mock AIME	99.4%	84.4%	DeepSeek V4.1 Flash 1017
FrontierMath, tiers 1 to 3	71.2%	85.3%	GPT-5.5
FrontierMath, tier 4	46.3%	72.5%	GPT-5.5
ProofBench	Not measured	50.0%	Only GPT-5.5 measured
LMArena Text: math	Not measured	1487	Only GPT-5.5 measured
LMArena Text: mathematical work	Not measured	1496	Only GPT-5.5 measured
Neither model has a result on the other 7 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01

Knowledge benchmarks for DeepSeek V4.1 Flash 1017 and GPT-5.5
Benchmark	DeepSeek V4.1 Flash 1017	GPT-5.5	Leader
MMLU Pro	Not measured	88.1%	Only GPT-5.5 measured
LegalBench (Vals AI)	Not measured	86.5%	Only GPT-5.5 measured
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)	Not measured	49.1%	Only GPT-5.5 measured
SimpleQA Verified	42.5%	63.0%	GPT-5.5
LMArena Text: science	Not measured	1485	Only GPT-5.5 measured
LMArena Text: medicine and healthcare	Not measured	1465	Only GPT-5.5 measured
LMArena Text: legal and government	Not measured	1491	Only GPT-5.5 measured
LMArena Text: business and finance	Not measured	1478	Only GPT-5.5 measured
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 index71

Web design benchmarks for DeepSeek V4.1 Flash 1017 and GPT-5.5
Benchmark	DeepSeek V4.1 Flash 1017	GPT-5.5	Leader
Design Arena: websites	1311	1256	DeepSeek V4.1 Flash 1017
Design Arena: UI components	1303	1257	DeepSeek V4.1 Flash 1017
Design Arena: game dev	1357	1293	DeepSeek V4.1 Flash 1017
Design Arena: data viz	1330	1264	DeepSeek V4.1 Flash 1017
Design Arena: 3D	1358	1197	DeepSeek V4.1 Flash 1017
Design Arena: all code categories	1332	1260	DeepSeek V4.1 Flash 1017
Design Arena: SVG	Not measured	1234	Only GPT-5.5 measured
Design Arena: ASCII art	1242	1255	GPT-5.5
LiveBench design	83.4%	70.3%	DeepSeek V4.1 Flash 1017

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

General benchmarks for DeepSeek V4.1 Flash 1017 and GPT-5.5
Benchmark	DeepSeek V4.1 Flash 1017	GPT-5.5	Leader
Vals Index	61.3%	Not measured	Only DeepSeek V4.1 Flash 1017 measured
LiveBench data analysis	79.5%	81.6%	GPT-5.5
Public Benefits Bench (Vals AI)	Not measured	60.9%	Only GPT-5.5 measured
SAGE (Vals AI)	Not measured	51.5%	Only GPT-5.5 measured
MedScribe (Vals AI)	Not measured	86.9%	Only GPT-5.5 measured
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)	82.7%	84.3%	GPT-5.5
DTBench	Not measured	96.0%	Only GPT-5.5 measured
LMCA	Not measured	54.3%	Only GPT-5.5 measured
Surface Evolver Bench	Not measured	88.1%	Only GPT-5.5 measured
Furniture assembly	Not measured	44.2%	Only GPT-5.5 measured
ExploitBench	Not measured	47.4%	Only GPT-5.5 measured
CL-bench Life	Not measured	22.2%	Only GPT-5.5 measured
LMArena Text	Not measured	1471	Only GPT-5.5 measured
LMArena Text: instruction following	Not measured	1478	Only GPT-5.5 measured
LMArena Text: creative writing	Not measured	1455	Only GPT-5.5 measured
LMArena Vision	Not measured	1297	Only GPT-5.5 measured
LMArena Text: English	Not measured	1470	Only GPT-5.5 measured
LMArena Text: non-English	Not measured	1467	Only GPT-5.5 measured
LMArena Text: Chinese	Not measured	1534	Only GPT-5.5 measured
LMArena Text: German	Not measured	1480	Only GPT-5.5 measured
LMArena Text: Japanese	Not measured	1499	Only GPT-5.5 measured
LMArena Text: Korean	Not measured	1460	Only GPT-5.5 measured
LMArena Text: French	Not measured	1485	Only GPT-5.5 measured
LMArena Text: Spanish	Not measured	1468	Only GPT-5.5 measured
LMArena Text: Russian	Not measured	1473	Only GPT-5.5 measured
LMArena Text: Polish	Not measured	1465	Only GPT-5.5 measured
LMArena Text: multi-turn	Not measured	1476	Only GPT-5.5 measured
LMArena Text: longer queries	Not measured	1484	Only GPT-5.5 measured
LMArena Text: writing and language	Not measured	1466	Only GPT-5.5 measured
LMArena Text: entertainment and media	Not measured	1449	Only GPT-5.5 measured
LMArena Vision: English	Not measured	1292	Only GPT-5.5 measured
LMArena Vision: Chinese	Not measured	1360	Only GPT-5.5 measured
LMArena Vision: reading text	Not measured	1306	Only GPT-5.5 measured
LMArena Vision: humor	Not measured	1293	Only GPT-5.5 measured
LMArena Vision: recognizing things	Not measured	1231	Only GPT-5.5 measured
LMArena Vision: creative writing	Not measured	1301	Only GPT-5.5 measured
LMArena Document	Not measured	1486	Only GPT-5.5 measured
Neither model has a result on the other 7 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 1017 and GPT-5.5
Benchmark	DeepSeek V4.1 Flash 1017	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 1017$0.0004Fits in one request

GPT-5.5$0.020Fits in one request

DeepSeek V4.1 Flash 1017 costs less

Repository review

50K input + 3K output tokens

DeepSeek V4.1 Flash 1017$0.0093Fits in one request

GPT-5.5$0.340Fits in one request

DeepSeek V4.1 Flash 1017 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4.1 Flash 1017$0.036Fits in one request

GPT-5.5$1.30Fits in one request

DeepSeek V4.1 Flash 1017 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 1017 and GPT-5.5
Specification	DeepSeek V4.1 Flash 1017	OpenAI GPT-5.5
Maker	DeepSeek	OpenAI
Weights	Open weights	No public weights listed
Released	October 17, 2026	April 24, 2026
Context window	1.05M	1.05M
Input $/Mtok	$0.15	$5.00
Output $/Mtok	$0.60	$30.00
Coding index	70.3	71.3
Independent measurements behind it	4	5
Cost per solved task	Not computable	$1.65
Source	OpenRouter listing	OpenRouter listing

Questions

Is DeepSeek V4.1 Flash 1017 better than GPT-5.5 for coding?

DeepSeek V4.1 Flash 1017 and GPT-5.5 are level for coding, 70.3 against 71.3 on the Vibe Coding Index, so price and context decide. Pick DeepSeek V4.1 Flash 1017 for output tokens 50.0x cheaper ($0.60 vs $30.00 per million) and work measured by LiveBench coding or LMArena WebDev, where it leads. Pick GPT-5.5 for a larger context window (1.05M against 1.05M tokens) and work measured by Code migration (Vals AI) or SciCode, where it leads.

Which is cheaper, DeepSeek V4.1 Flash 1017 or GPT-5.5?

DeepSeek V4.1 Flash 1017 costs $0.15 per million input tokens and $0.60 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 1017 or GPT-5.5?

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

How many benchmarks is the DeepSeek V4.1 Flash 1017 vs GPT-5.5 comparison based on?

35 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4.1 Flash 1017's coding index rests on 4 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 1017 or GPT-5.5: which to pick

Which one for your work

Coding

Intelligence

Agentic work

Reasoning

Knowledge

Web design

General

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