DeepSeek V4.1 Flash 1017 vs Gemini 3.8 Flash

DeepSeek V4.1 Flash 1017 leads Gemini 3.8 Flash on coding, 70.3 to 61.6 on the Vibe Coding Index, and DeepSeek V4.1 Flash 1017 costs 6.3x less per output token ($0.60 vs $3.75 per million). Updated October 22, 2026; 39 benchmarks have a result for both.

Replace DeepSeek V4.1 Flash 1017 Replace Gemini 3.8 Flash

DeepSeek V4.1 Flash 1017DeepSeek

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

Gemini 3.8 FlashGoogle

Coding: 61.6
Overall: 69.0
Output $/M: $3.75

vs boards won of 39 shared

DeepSeek V4.1 Flash 1017 or Gemini 3.8 Flash: which to pick

Pick DeepSeek V4.1 Flash 1017 for coding: it scores 70.3 on the Vibe Coding Index against 61.6 for Gemini 3.8 Flash, though on 4 benchmarks against 6.

Pick Gemini 3.8 Flash for work measured by SciCode or AA Intelligence Index, where it leads.

Model ADeepSeek V4.1 Flash 1017DeepSeek70.3/1004 independent benchmarksNo cost per solved task Model BGemini 3.8 FlashGoogle61.6/1006 independent benchmarks$2.74 per solved task

3989

Shared results: 39
DeepSeek V4.1 Flash 1017 only: 0
Gemini 3.8 Flash only: 89
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 61.6.
4 and 6 benchmarks behind the scores
Intelligence
AA Intelligence Index
Gemini 3.8 Flash
Gemini 3.8 Flash leads, 40.9 against 39.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Gemini 3.8 Flash has a AA Agentic Index result, 40.2.
Directional only
Reasoning
GPQA Diamond
No call
Only Gemini 3.8 Flash has a GPQA Diamond result, 94.4%.
Directional only
Knowledge
MMLU Pro
No call
Only Gemini 3.8 Flash has a MMLU Pro result, 90.2%.
Directional only
Web design
Design Arena: websites
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 1311 against 1300.
Same board, both measured
General
Vals Index
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 61.3% against 56.0%.
Same board, both measured
Long documents
Listed context window
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 $0.0004, Gemini 3.8 Flash $0.0026 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Gemini 3.8 Flash has the number, $2.74.
Directional only

At a glance

Coding index: DeepSeek V4.1 Flash 1017 70.3 from 4 benchmarks, Gemini 3.8 Flash 61.6 from 6 benchmarks.
Widest category: Knowledge, 27.2 points apart on average over 1 shared benchmark.
Widest single gap: Design Arena: spreadsheets: DeepSeek V4.1 Flash 1017 1245 against Gemini 3.8 Flash 1022, 27.9 points apart on a 0 to 100 scale.
Output price: DeepSeek V4.1 Flash 1017 $0.60 against Gemini 3.8 Flash $3.75 per million output tokens, 6.3x apart.
Context window: Both take 1.05M tokens.

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	Gemini 3.8 Flash
Coding	8	1
Intelligence	0	1
Agentic work	7	2
Reasoning	1	4
Math	3	0
Knowledge	0	1
Web design	6	2
General	2	1
All shared boards	27	12

CodingLike-for-likeSix of these boards feed the Vibe Coding Index81

Coding benchmarks for DeepSeek V4.1 Flash 1017 and Gemini 3.8 Flash
Benchmark	DeepSeek V4.1 Flash 1017	Gemini 3.8 Flash	Leader
LiveBench coding	96.0%	82.2%	DeepSeek V4.1 Flash 1017
LMArena WebDev	1664	1585	DeepSeek V4.1 Flash 1017
AA Coding Index	Not measured	76.3	Only Gemini 3.8 Flash measured
LiveCodeBench (Vals AI)	Not measured	89.5%	Only Gemini 3.8 Flash measured
SWE-bench Verified (Vals AI)	Not measured	80.0%	Only Gemini 3.8 Flash measured
Terminal-Bench 4 (Vals AI)	31.8%	19.7%	DeepSeek V4.1 Flash 1017
Vibe Code Bench (Vals AI)	87.7%	78.7%	DeepSeek V4.1 Flash 1017
Code migration (Vals AI)	44.2%	36.5%	DeepSeek V4.1 Flash 1017
ProgramBench (Vals AI)	Not measured	1.0%	Only Gemini 3.8 Flash measured
IOI (Vals AI)	Not measured	56.9%	Only Gemini 3.8 Flash measured
Terminal-Bench 2.1 (Vals AI)	Not measured	81.3%	Only Gemini 3.8 Flash measured
SWE-bench Multimodal (Vals AI)	Not measured	40.8%	Only Gemini 3.8 Flash measured
Vibe Code Bench 1-100 (Vals AI)	Not measured	18.8%	Only Gemini 3.8 Flash measured
SkillsBench (Vals AI)	Not measured	58.0%	Only Gemini 3.8 Flash measured
CyberBench (Vals AI)	Not measured	43.8%	Only Gemini 3.8 Flash measured
SciCode	55.9%	56.6%	Gemini 3.8 Flash
FrontierCode	Not measured	41.2%	Only Gemini 3.8 Flash measured
FrontierSWE	Not measured	19.6%	Only Gemini 3.8 Flash measured
WeirdML	Not measured	84.8%	Only Gemini 3.8 Flash measured
ALE-Bench	1622.0	1269.7	DeepSeek V4.1 Flash 1017
LMArena Text: coding	Not measured	1508	Only Gemini 3.8 Flash measured
LMArena Text: software and IT	Not measured	1507	Only Gemini 3.8 Flash measured
LMArena WebDev: React	1667	1577	DeepSeek V4.1 Flash 1017
LMArena WebDev: HTML	1687	1616	DeepSeek V4.1 Flash 1017
LMArena WebDev: from an image	Not measured	1593	Only Gemini 3.8 Flash measured
SciCode	Not measured	12.3%	Only Gemini 3.8 Flash measured
Neither model has a result on the other 13 boards here

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

Intelligence benchmarks for DeepSeek V4.1 Flash 1017 and Gemini 3.8 Flash
Benchmark	DeepSeek V4.1 Flash 1017	Gemini 3.8 Flash	Leader
AA Intelligence Index	39.5	40.9	Gemini 3.8 Flash
Epoch Capabilities Index	Not measured	157.4	Only Gemini 3.8 Flash measured

Agentic workLike-for-likePublic boards, shown as published, kept out of the index72

Agentic work benchmarks for DeepSeek V4.1 Flash 1017 and Gemini 3.8 Flash
Benchmark	DeepSeek V4.1 Flash 1017	Gemini 3.8 Flash	Leader
AA Agentic Index	Not measured	40.2	Only Gemini 3.8 Flash measured
Design Arena: agents building web apps	Not measured	1238	Only Gemini 3.8 Flash measured
Design Arena: agents building full-stack apps	Not measured	1244	Only Gemini 3.8 Flash measured
Design Arena: agents building mobile apps	Not measured	1236	Only Gemini 3.8 Flash measured
Design Arena: agents building Android apps	Not measured	1078	Only Gemini 3.8 Flash measured
Design Arena: agents building Godot games	Not measured	1273	Only Gemini 3.8 Flash measured
Design Arena: PowerPoint slides	Not measured	1153	Only Gemini 3.8 Flash measured
Design Arena: spreadsheets	1245	1022	DeepSeek V4.1 Flash 1017
LiveBench research	79.5%	79.1%	DeepSeek V4.1 Flash 1017
LiveBench browser	76.0%	64.0%	DeepSeek V4.1 Flash 1017
LiveBench long agentic	50.9%	30.1%	DeepSeek V4.1 Flash 1017
SREBench (Vals AI)	Not measured	11.1%	Only Gemini 3.8 Flash measured
Finance Agent (Vals AI)	57.0%	61.4%	Gemini 3.8 Flash
Tax Agent Bench (Vals AI)	44.4%	32.1%	DeepSeek V4.1 Flash 1017
Legal Research Bench (Vals AI)	45.2%	38.9%	DeepSeek V4.1 Flash 1017
Harvey's Legal Agent Benchmark (Vals AI)	14.2%	12.1%	DeepSeek V4.1 Flash 1017
Excel Modeling Benchmark (Vals AI)	61.8%	72.2%	Gemini 3.8 Flash
BioMysteryBench (Vals AI)	Not measured	62.2%	Only Gemini 3.8 Flash measured
Terminal-Bench-Science (Vals AI)	Not measured	8.6%	Only Gemini 3.8 Flash measured
Vals Science Index	Not measured	35.8%	Only Gemini 3.8 Flash measured
Time Horizon Index (Vals AI)	Not measured	18.8%	Only Gemini 3.8 Flash measured
CUA-bench (Vals AI)	Not measured	4.2%	Only Gemini 3.8 Flash measured
APEX-Agents	Not measured	64.3%	Only Gemini 3.8 Flash measured
Remote Labor Index	Not measured	5.8%	Only Gemini 3.8 Flash measured
Vending-Bench 2	Not measured	$5,094	Only Gemini 3.8 Flash measured
Neither model has a result on the other 16 boards here

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

Reasoning benchmarks for DeepSeek V4.1 Flash 1017 and Gemini 3.8 Flash
Benchmark	DeepSeek V4.1 Flash 1017	Gemini 3.8 Flash	Leader
GPQA Diamond	Not measured	94.4%	Only Gemini 3.8 Flash measured
LiveBench reasoning	89.1%	89.3%	Gemini 3.8 Flash
MysteryMechanism (Vals AI)	Not measured	36.5%	Only Gemini 3.8 Flash measured
MMMU Pro (Vals AI)	Not measured	89.1%	Only Gemini 3.8 Flash measured
GPQA Diamond (Artificial Analysis run)	Not measured	95.3%	Only Gemini 3.8 Flash measured
Humanity's Last Exam (Artificial Analysis run)	43.3%	47.8%	Gemini 3.8 Flash
GPQA Diamond (Epoch AI run)	93.2%	95.4%	Gemini 3.8 Flash
ARC-AGI	Not measured	98.5%	Only Gemini 3.8 Flash measured
ARC-AGI-2	Not measured	89.2%	Only Gemini 3.8 Flash measured
Chess puzzles	46.0%	58.0%	Gemini 3.8 Flash
Mystery game puzzles	49.0%	47.0%	DeepSeek V4.1 Flash 1017
LMArena Text: hard prompts	Not measured	1509	Only Gemini 3.8 Flash measured
LMArena Text: expert	Not measured	1526	Only Gemini 3.8 Flash measured
LMArena Text: hard prompts in English	Not measured	1504	Only Gemini 3.8 Flash measured
LMArena Vision: diagrams	Not measured	1345	Only Gemini 3.8 Flash measured
LMArena Vision: homework	Not measured	1351	Only Gemini 3.8 Flash measured
Neither model has a result on the other 6 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index30

Math benchmarks for DeepSeek V4.1 Flash 1017 and Gemini 3.8 Flash
Benchmark	DeepSeek V4.1 Flash 1017	Gemini 3.8 Flash	Leader
OTIS Mock AIME	99.4%	98.9%	DeepSeek V4.1 Flash 1017
FrontierMath, tiers 1 to 3	71.2%	68.4%	DeepSeek V4.1 Flash 1017
FrontierMath, tier 4	46.3%	22.0%	DeepSeek V4.1 Flash 1017
ProofBench	Not measured	48.0%	Only Gemini 3.8 Flash measured
LMArena Text: math	Not measured	1532	Only Gemini 3.8 Flash measured
LMArena Text: mathematical work	Not measured	1519	Only Gemini 3.8 Flash 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 Gemini 3.8 Flash
Benchmark	DeepSeek V4.1 Flash 1017	Gemini 3.8 Flash	Leader
MMLU Pro	Not measured	90.2%	Only Gemini 3.8 Flash measured
LegalBench (Vals AI)	Not measured	87.0%	Only Gemini 3.8 Flash measured
TaxEval (Vals AI)	Not measured	74.5%	Only Gemini 3.8 Flash measured
MortgageTax (Vals AI)	Not measured	65.3%	Only Gemini 3.8 Flash measured
MedCode (Vals AI)	Not measured	48.1%	Only Gemini 3.8 Flash measured
SimpleQA Verified	42.5%	69.7%	Gemini 3.8 Flash
LMArena Text: science	Not measured	1523	Only Gemini 3.8 Flash measured
LMArena Text: medicine and healthcare	Not measured	1508	Only Gemini 3.8 Flash measured
LMArena Text: legal and government	Not measured	1497	Only Gemini 3.8 Flash measured
LMArena Text: business and finance	Not measured	1479	Only Gemini 3.8 Flash measured
FACTS Benchmark Suite	Not measured	69.4%	Only Gemini 3.8 Flash measured
Neither model has a result on the other 7 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index62

Web design benchmarks for DeepSeek V4.1 Flash 1017 and Gemini 3.8 Flash
Benchmark	DeepSeek V4.1 Flash 1017	Gemini 3.8 Flash	Leader
Design Arena: websites	1311	1300	DeepSeek V4.1 Flash 1017
Design Arena: UI components	1303	1316	Gemini 3.8 Flash
Design Arena: game dev	1357	1304	DeepSeek V4.1 Flash 1017
Design Arena: data viz	1330	1270	DeepSeek V4.1 Flash 1017
Design Arena: 3D	1358	1294	DeepSeek V4.1 Flash 1017
Design Arena: all code categories	1332	1303	DeepSeek V4.1 Flash 1017
Design Arena: SVG	Not measured	1296	Only Gemini 3.8 Flash measured
Design Arena: ASCII art	1242	1262	Gemini 3.8 Flash
LiveBench design	83.4%	72.7%	DeepSeek V4.1 Flash 1017

GeneralLike-for-likePublic boards, shown as published, kept out of the index21

General benchmarks for DeepSeek V4.1 Flash 1017 and Gemini 3.8 Flash
Benchmark	DeepSeek V4.1 Flash 1017	Gemini 3.8 Flash	Leader
Vals Index	61.3%	56.0%	DeepSeek V4.1 Flash 1017
LiveBench data analysis	79.5%	54.0%	DeepSeek V4.1 Flash 1017
Public Benefits Bench (Vals AI)	Not measured	65.3%	Only Gemini 3.8 Flash measured
SAGE (Vals AI)	Not measured	35.1%	Only Gemini 3.8 Flash measured
MedScribe (Vals AI)	Not measured	84.5%	Only Gemini 3.8 Flash measured
Long context reasoning (AA-LCR)	82.7%	84.0%	Gemini 3.8 Flash
DTBench	Not measured	95.7%	Only Gemini 3.8 Flash measured
LMCA	Not measured	52.9%	Only Gemini 3.8 Flash measured
Surface Evolver Bench	Not measured	76.9%	Only Gemini 3.8 Flash measured
Furniture assembly	Not measured	31.7%	Only Gemini 3.8 Flash measured
LMArena Text	Not measured	1499	Only Gemini 3.8 Flash measured
LMArena Text: instruction following	Not measured	1492	Only Gemini 3.8 Flash measured
LMArena Text: creative writing	Not measured	1488	Only Gemini 3.8 Flash measured
LMArena Vision	Not measured	1315	Only Gemini 3.8 Flash measured
LMArena Text: English	Not measured	1498	Only Gemini 3.8 Flash measured
LMArena Text: non-English	Not measured	1491	Only Gemini 3.8 Flash measured
LMArena Text: Chinese	Not measured	1553	Only Gemini 3.8 Flash measured
LMArena Text: German	Not measured	1501	Only Gemini 3.8 Flash measured
LMArena Text: Japanese	Not measured	1507	Only Gemini 3.8 Flash measured
LMArena Text: Korean	Not measured	1464	Only Gemini 3.8 Flash measured
LMArena Text: French	Not measured	1496	Only Gemini 3.8 Flash measured
LMArena Text: Spanish	Not measured	1487	Only Gemini 3.8 Flash measured
LMArena Text: Russian	Not measured	1514	Only Gemini 3.8 Flash measured
LMArena Text: Polish	Not measured	1497	Only Gemini 3.8 Flash measured
LMArena Text: multi-turn	Not measured	1503	Only Gemini 3.8 Flash measured
LMArena Text: longer queries	Not measured	1507	Only Gemini 3.8 Flash measured
LMArena Text: writing and language	Not measured	1494	Only Gemini 3.8 Flash measured
LMArena Text: entertainment and media	Not measured	1480	Only Gemini 3.8 Flash measured
LMArena Vision: English	Not measured	1318	Only Gemini 3.8 Flash measured
LMArena Vision: Chinese	Not measured	1416	Only Gemini 3.8 Flash measured
LMArena Vision: reading text	Not measured	1333	Only Gemini 3.8 Flash measured
LMArena Vision: humor	Not measured	1321	Only Gemini 3.8 Flash measured
LMArena Vision: creative writing	Not measured	1299	Only Gemini 3.8 Flash measured
Neither model has a result on the other 13 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 Gemini 3.8 Flash
Benchmark	DeepSeek V4.1 Flash 1017	Gemini 3.8 Flash	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

Gemini 3.8 Flash$0.0026Fits 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

Gemini 3.8 Flash$0.049Fits 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

Gemini 3.8 Flash$0.188Fits 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 Gemini 3.8 Flash
Specification	DeepSeek V4.1 Flash 1017	Google Gemini 3.8 Flash
Maker	DeepSeek	Google
Weights	Open weights	No public weights listed
Released	October 17, 2026	September 2, 2026
Context window	1.05M	1.05M
Input $/Mtok	$0.15	$0.75
Output $/Mtok	$0.60	$3.75
Coding index	70.3	61.6
Independent measurements behind it	4	6
Cost per solved task	Not computable	$2.74
Source	OpenRouter listing	OpenRouter listing

Questions

Is DeepSeek V4.1 Flash 1017 better than Gemini 3.8 Flash for coding?

Pick DeepSeek V4.1 Flash 1017 for coding: it scores 70.3 on the Vibe Coding Index against 61.6 for Gemini 3.8 Flash, though on 4 benchmarks against 6. Pick Gemini 3.8 Flash for work measured by SciCode or AA Intelligence Index, where it leads.

Which is cheaper, DeepSeek V4.1 Flash 1017 or Gemini 3.8 Flash?

DeepSeek V4.1 Flash 1017 costs $0.15 per million input tokens and $0.60 per million output tokens; Gemini 3.8 Flash costs $0.75 and $3.75. Cost per solved task is known for Gemini 3.8 Flash only.

Which has the bigger context window, DeepSeek V4.1 Flash 1017 or Gemini 3.8 Flash?

Neither: both take 1.05M tokens.

How many benchmarks is the DeepSeek V4.1 Flash 1017 vs Gemini 3.8 Flash comparison based on?

39 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 Gemini 3.8 Flash'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-1017-vs-gemini-3-8-flash" title="DeepSeek V4.1 Flash 1017 vs Gemini 3.8 Flash, 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-1017-vs-gemini-3-8-flash">LLMBenchmark.ai, DeepSeek V4.1 Flash 1017 vs Gemini 3.8 Flash</a></p>

DeepSeek V4.1 Flash 1017 or Gemini 3.8 Flash: 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