DeepSeek V4.1 Flash 1017 vs GPT-5.6 Terra

DeepSeek V4.1 Flash 1017 leads GPT-5.6 Terra on coding, 70.3 to 63.9 on the Vibe Coding Index, and DeepSeek V4.1 Flash 1017 costs 20.0x less per output token ($0.60 vs $12.00 per million). Updated October 22, 2026; 36 benchmarks have a result for both.

Replace DeepSeek V4.1 Flash 1017 Replace GPT-5.6 Terra

DeepSeek V4.1 Flash 1017DeepSeek

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

GPT-5.6 TerraOpenAI

Coding: 63.9
Overall: 68.1
Output $/M: $12.00

vs boards won of 36 shared

DeepSeek V4.1 Flash 1017 or GPT-5.6 Terra: which to pick

Pick DeepSeek V4.1 Flash 1017 for coding: it scores 70.3 on the Vibe Coding Index against 63.9 for GPT-5.6 Terra, though on 4 benchmarks against 6.

Pick GPT-5.6 Terra for a larger context window (1.05M against 1.05M tokens) and work measured by Code migration (Vals AI) or ALE-Bench, where it leads.

Model ADeepSeek V4.1 Flash 1017DeepSeek70.3/1004 independent benchmarksNo cost per solved task Model BGPT-5.6 TerraOpenAI63.9/1006 independent benchmarks$0.42 per solved task

36391

Shared results: 36
DeepSeek V4.1 Flash 1017 only: 3
GPT-5.6 Terra only: 91
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
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 scores 70.3 against 63.9.
4 and 6 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.6 Terra
GPT-5.6 Terra leads, 42.1 against 39.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GPT-5.6 Terra has a AA Agentic Index result, 43.2.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.6 Terra has a GPQA Diamond result, 90.9%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.6 Terra has a MMLU Pro result, 86.7%.
Directional only
Web design
Design Arena: websites
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 1311 against 1268.
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-5.6 Terra
GPT-5.6 Terra 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-5.6 Terra $0.0080 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.6 Terra has the number, $0.42.
Directional only

At a glance

Coding index: DeepSeek V4.1 Flash 1017 70.3 from 4 benchmarks, GPT-5.6 Terra 63.9 from 6 benchmarks.
Widest category: Math, 13.1 points apart on average over 3 shared benchmarks.
Widest single gap: Design Arena: 3D: DeepSeek V4.1 Flash 1017 1358 against GPT-5.6 Terra 1134, 28.0 points apart on a 0 to 100 scale.
Output price: DeepSeek V4.1 Flash 1017 $0.60 against GPT-5.6 Terra $12.00 per million output tokens, 20.0x apart.
Context window: DeepSeek V4.1 Flash 1017 takes 1.05M tokens, GPT-5.6 Terra 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-5.6 Terra
Coding	7	2
Intelligence	0	1
Agentic work	5	2
Reasoning	2	3
Math	0	3
Knowledge	0	1
Web design	8	0
General	1	1
All shared boards	23	13

CodingLike-for-likeSix of these boards feed the Vibe Coding Index72

Coding benchmarks for DeepSeek V4.1 Flash 1017 and GPT-5.6 Terra
Benchmark	DeepSeek V4.1 Flash 1017	GPT-5.6 Terra	Leader
LiveBench coding	96.0%	86.2%	DeepSeek V4.1 Flash 1017
LMArena WebDev	1664	1522	DeepSeek V4.1 Flash 1017
AA Coding Index	Not measured	76.7	Only GPT-5.6 Terra measured
LiveCodeBench (Vals AI)	Not measured	85.9%	Only GPT-5.6 Terra measured
SWE-bench Verified (Vals AI)	Not measured	95.4%	Only GPT-5.6 Terra measured
Terminal-Bench 4 (Vals AI)	31.8%	22.7%	DeepSeek V4.1 Flash 1017
Vibe Code Bench (Vals AI)	87.7%	74.6%	DeepSeek V4.1 Flash 1017
Code migration (Vals AI)	44.2%	47.8%	GPT-5.6 Terra
ProgramBench (Vals AI)	Not measured	0.5%	Only GPT-5.6 Terra measured
IOI (Vals AI)	Not measured	87.6%	Only GPT-5.6 Terra measured
Terminal-Bench 2.1 (Vals AI)	Not measured	77.5%	Only GPT-5.6 Terra measured
Vibe Code Bench 1-100 (Vals AI)	Not measured	14.8%	Only GPT-5.6 Terra measured
SkillsBench (Vals AI)	Not measured	58.9%	Only GPT-5.6 Terra measured
CyberBench (Vals AI)	Not measured	72.1%	Only GPT-5.6 Terra measured
SciCode	55.9%	55.0%	DeepSeek V4.1 Flash 1017
Terminal-Bench Hard (Artificial Analysis run)	Not measured	62.9%	Only GPT-5.6 Terra measured
FrontierCode	Not measured	41.3%	Only GPT-5.6 Terra measured
WeirdML	Not measured	78.3%	Only GPT-5.6 Terra measured
ALE-Bench	1622.0	1951.4	GPT-5.6 Terra
LMArena Text: coding	Not measured	1485	Only GPT-5.6 Terra measured
LMArena Text: software and IT	Not measured	1482	Only GPT-5.6 Terra measured
LMArena WebDev: React	1667	1513	DeepSeek V4.1 Flash 1017
LMArena WebDev: HTML	1687	1526	DeepSeek V4.1 Flash 1017
LMArena WebDev: from an image	Not measured	1541	Only GPT-5.6 Terra measured
SciCode	Not measured	10.8%	Only GPT-5.6 Terra measured
Neither model has a result on the other 14 boards here

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

Intelligence benchmarks for DeepSeek V4.1 Flash 1017 and GPT-5.6 Terra
Benchmark	DeepSeek V4.1 Flash 1017	GPT-5.6 Terra	Leader
AA Intelligence Index	39.5	42.1	GPT-5.6 Terra
Epoch Capabilities Index	Not measured	160.1	Only GPT-5.6 Terra measured

Agentic workLike-for-likePublic boards, shown as published, kept out of the index52

Agentic work benchmarks for DeepSeek V4.1 Flash 1017 and GPT-5.6 Terra
Benchmark	DeepSeek V4.1 Flash 1017	GPT-5.6 Terra	Leader
AA Agentic Index	Not measured	43.2	Only GPT-5.6 Terra measured
Design Arena: agents building web apps	Not measured	1120	Only GPT-5.6 Terra measured
Design Arena: agents building full-stack apps	Not measured	1061	Only GPT-5.6 Terra measured
Design Arena: agents building mobile apps	Not measured	1111	Only GPT-5.6 Terra measured
Design Arena: agents building Android apps	Not measured	1062	Only GPT-5.6 Terra measured
Design Arena: agents building games	Not measured	1083	Only GPT-5.6 Terra measured
Design Arena: HTML slides	Not measured	1058	Only GPT-5.6 Terra measured
Design Arena: PowerPoint slides	Not measured	1105	Only GPT-5.6 Terra measured
Design Arena: spreadsheets	1245	Not measured	Only DeepSeek V4.1 Flash 1017 measured
LiveBench research	79.5%	60.4%	DeepSeek V4.1 Flash 1017
LiveBench browser	76.0%	84.0%	GPT-5.6 Terra
LiveBench long agentic	50.9%	48.4%	DeepSeek V4.1 Flash 1017
SREBench (Vals AI)	Not measured	9.5%	Only GPT-5.6 Terra measured
Finance Agent (Vals AI)	57.0%	54.4%	DeepSeek V4.1 Flash 1017
Tax Agent Bench (Vals AI)	44.4%	30.0%	DeepSeek V4.1 Flash 1017
Legal Research Bench (Vals AI)	45.2%	41.4%	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%	66.2%	GPT-5.6 Terra
Terminal-Bench-Science (Vals AI)	Not measured	10.0%	Only GPT-5.6 Terra measured
τ²-Bench Telecom	Not measured	86.3%	Only GPT-5.6 Terra measured
APEX-Agents	Not measured	58.2%	Only GPT-5.6 Terra measured
BALROG	Not measured	53.2%	Only GPT-5.6 Terra measured
Vending-Bench 2	Not measured	$7,343	Only GPT-5.6 Terra measured
Neither model has a result on the other 18 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index23

Reasoning benchmarks for DeepSeek V4.1 Flash 1017 and GPT-5.6 Terra
Benchmark	DeepSeek V4.1 Flash 1017	GPT-5.6 Terra	Leader
GPQA Diamond	Not measured	90.9%	Only GPT-5.6 Terra measured
LiveBench reasoning	89.1%	90.6%	GPT-5.6 Terra
MMMU Pro (Vals AI)	Not measured	86.5%	Only GPT-5.6 Terra measured
GPQA Diamond (Artificial Analysis run)	Not measured	92.5%	Only GPT-5.6 Terra measured
Humanity's Last Exam (Artificial Analysis run)	43.3%	42.9%	DeepSeek V4.1 Flash 1017
GPQA Diamond (Epoch AI run)	93.2%	93.3%	GPT-5.6 Terra
ARC-AGI	Not measured	96.5%	Only GPT-5.6 Terra measured
ARC-AGI-2	Not measured	83.9%	Only GPT-5.6 Terra measured
SimpleBench	Not measured	48.9%	Only GPT-5.6 Terra measured
Chess puzzles	46.0%	47.0%	GPT-5.6 Terra
Mystery game puzzles	49.0%	35.0%	DeepSeek V4.1 Flash 1017
LMArena Text: hard prompts	Not measured	1469	Only GPT-5.6 Terra measured
LMArena Text: expert	Not measured	1496	Only GPT-5.6 Terra measured
LMArena Text: hard prompts in English	Not measured	1465	Only GPT-5.6 Terra measured
LMArena Vision: diagrams	Not measured	1295	Only GPT-5.6 Terra measured
LMArena Vision: homework	Not measured	1312	Only GPT-5.6 Terra measured
Kagi LLM Benchmark	Not measured	51.3%	Only GPT-5.6 Terra measured
Neither model has a result on the other 5 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index03

Math benchmarks for DeepSeek V4.1 Flash 1017 and GPT-5.6 Terra
Benchmark	DeepSeek V4.1 Flash 1017	GPT-5.6 Terra	Leader
OTIS Mock AIME	99.4%	99.7%	GPT-5.6 Terra
FrontierMath, tiers 1 to 3	71.2%	86.0%	GPT-5.6 Terra
FrontierMath, tier 4	46.3%	70.7%	GPT-5.6 Terra
ProofBench	Not measured	74.0%	Only GPT-5.6 Terra measured
LMArena Text: math	Not measured	1465	Only GPT-5.6 Terra measured
LMArena Text: mathematical work	Not measured	1478	Only GPT-5.6 Terra 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-5.6 Terra
Benchmark	DeepSeek V4.1 Flash 1017	GPT-5.6 Terra	Leader
MMLU Pro	Not measured	86.7%	Only GPT-5.6 Terra measured
LegalBench (Vals AI)	Not measured	85.1%	Only GPT-5.6 Terra measured
CorpFin (Vals AI)	Not measured	65.3%	Only GPT-5.6 Terra measured
TaxEval (Vals AI)	Not measured	76.2%	Only GPT-5.6 Terra measured
MortgageTax (Vals AI)	Not measured	67.3%	Only GPT-5.6 Terra measured
MedCode (Vals AI)	Not measured	43.4%	Only GPT-5.6 Terra measured
SimpleQA Verified	42.5%	43.2%	GPT-5.6 Terra
LMArena Text: science	Not measured	1452	Only GPT-5.6 Terra measured
LMArena Text: medicine and healthcare	Not measured	1430	Only GPT-5.6 Terra measured
LMArena Text: legal and government	Not measured	1461	Only GPT-5.6 Terra measured
LMArena Text: business and finance	Not measured	1448	Only GPT-5.6 Terra measured
FACTS Benchmark Suite	Not measured	52.0%	Only GPT-5.6 Terra measured
Neither model has a result on the other 6 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index80

Web design benchmarks for DeepSeek V4.1 Flash 1017 and GPT-5.6 Terra
Benchmark	DeepSeek V4.1 Flash 1017	GPT-5.6 Terra	Leader
Design Arena: websites	1311	1268	DeepSeek V4.1 Flash 1017
Design Arena: UI components	1303	1256	DeepSeek V4.1 Flash 1017
Design Arena: game dev	1357	1229	DeepSeek V4.1 Flash 1017
Design Arena: data viz	1330	1240	DeepSeek V4.1 Flash 1017
Design Arena: 3D	1358	1134	DeepSeek V4.1 Flash 1017
Design Arena: all code categories	1332	1241	DeepSeek V4.1 Flash 1017
Design Arena: SVG	Not measured	1219	Only GPT-5.6 Terra measured
Design Arena: ASCII art	1242	1177	DeepSeek V4.1 Flash 1017
LiveBench design	83.4%	76.0%	DeepSeek V4.1 Flash 1017

GeneralLike-for-likePublic boards, shown as published, kept out of the index11

General benchmarks for DeepSeek V4.1 Flash 1017 and GPT-5.6 Terra
Benchmark	DeepSeek V4.1 Flash 1017	GPT-5.6 Terra	Leader
Vals Index	61.3%	Not measured	Only DeepSeek V4.1 Flash 1017 measured
LiveBench data analysis	79.5%	79.3%	DeepSeek V4.1 Flash 1017
Public Benefits Bench (Vals AI)	Not measured	62.4%	Only GPT-5.6 Terra measured
SAGE (Vals AI)	Not measured	47.0%	Only GPT-5.6 Terra measured
MedScribe (Vals AI)	Not measured	82.9%	Only GPT-5.6 Terra measured
Vals Multimodal Index	Not measured	65.1%	Only GPT-5.6 Terra measured
IFBench	Not measured	71.2%	Only GPT-5.6 Terra measured
Long context reasoning (AA-LCR)	82.7%	83.0%	GPT-5.6 Terra
DTBench	Not measured	93.3%	Only GPT-5.6 Terra measured
LMCA	Not measured	54.9%	Only GPT-5.6 Terra measured
Surface Evolver Bench	Not measured	83.8%	Only GPT-5.6 Terra measured
Furniture assembly	Not measured	54.2%	Only GPT-5.6 Terra measured
LMArena Text	Not measured	1448	Only GPT-5.6 Terra measured
LMArena Text: instruction following	Not measured	1456	Only GPT-5.6 Terra measured
LMArena Text: creative writing	Not measured	1413	Only GPT-5.6 Terra measured
LMArena Vision	Not measured	1271	Only GPT-5.6 Terra measured
LMArena Text: English	Not measured	1449	Only GPT-5.6 Terra measured
LMArena Text: non-English	Not measured	1441	Only GPT-5.6 Terra measured
LMArena Text: Chinese	Not measured	1514	Only GPT-5.6 Terra measured
LMArena Text: German	Not measured	1460	Only GPT-5.6 Terra measured
LMArena Text: Japanese	Not measured	1454	Only GPT-5.6 Terra measured
LMArena Text: Korean	Not measured	1427	Only GPT-5.6 Terra measured
LMArena Text: French	Not measured	1466	Only GPT-5.6 Terra measured
LMArena Text: Spanish	Not measured	1451	Only GPT-5.6 Terra measured
LMArena Text: Russian	Not measured	1452	Only GPT-5.6 Terra measured
LMArena Text: Polish	Not measured	1446	Only GPT-5.6 Terra measured
LMArena Text: multi-turn	Not measured	1453	Only GPT-5.6 Terra measured
LMArena Text: longer queries	Not measured	1454	Only GPT-5.6 Terra measured
LMArena Text: writing and language	Not measured	1431	Only GPT-5.6 Terra measured
LMArena Text: entertainment and media	Not measured	1418	Only GPT-5.6 Terra measured
LMArena Vision: English	Not measured	1272	Only GPT-5.6 Terra measured
LMArena Vision: Chinese	Not measured	1337	Only GPT-5.6 Terra measured
LMArena Vision: reading text	Not measured	1278	Only GPT-5.6 Terra measured
LMArena Vision: humor	Not measured	1236	Only GPT-5.6 Terra measured
LMArena Vision: creative writing	Not measured	1248	Only GPT-5.6 Terra measured
LMArena Document	Not measured	1472	Only GPT-5.6 Terra measured
Neither model has a result on the other 10 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-5.6 Terra
Benchmark	DeepSeek V4.1 Flash 1017	GPT-5.6 Terra	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-5.6 Terra$0.0080Fits 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-5.6 Terra$0.136Fits 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-5.6 Terra$0.520Fits 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-5.6 Terra
Specification	DeepSeek V4.1 Flash 1017	OpenAI GPT-5.6 Terra
Maker	DeepSeek	OpenAI
Weights	Open weights	No public weights listed
Released	October 17, 2026	July 9, 2026
Context window	1.05M	1.05M
Input $/Mtok	$0.15	$2.00
Output $/Mtok	$0.60	$12.00
Coding index	70.3	63.9
Independent measurements behind it	4	6
Cost per solved task	Not computable	$0.42
Source	OpenRouter listing	OpenRouter listing

Questions

Is DeepSeek V4.1 Flash 1017 better than GPT-5.6 Terra for coding?

Pick DeepSeek V4.1 Flash 1017 for coding: it scores 70.3 on the Vibe Coding Index against 63.9 for GPT-5.6 Terra, though on 4 benchmarks against 6. Pick GPT-5.6 Terra for a larger context window (1.05M against 1.05M tokens) and work measured by Code migration (Vals AI) or ALE-Bench, where it leads.

Which is cheaper, DeepSeek V4.1 Flash 1017 or GPT-5.6 Terra?

DeepSeek V4.1 Flash 1017 costs $0.15 per million input tokens and $0.60 per million output tokens; GPT-5.6 Terra costs $2.00 and $12.00. Cost per solved task is known for GPT-5.6 Terra only.

Which has the bigger context window, DeepSeek V4.1 Flash 1017 or GPT-5.6 Terra?

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

How many benchmarks is the DeepSeek V4.1 Flash 1017 vs GPT-5.6 Terra comparison based on?

36 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-5.6 Terra's on 6. 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-5.6 Terra: 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