DeepSeek V4.1 Flash 1017 vs Gemini 3.7 Flash

DeepSeek V4.1 Flash 1017 leads Gemini 3.7 Flash on coding, 70.3 to 58.0 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; 37 benchmarks have a result for both.

Replace DeepSeek V4.1 Flash 1017 Replace Gemini 3.7 Flash

DeepSeek V4.1 Flash 1017DeepSeek

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

Gemini 3.7 FlashGoogle

Coding: 58.0
Overall: 65.7
Output $/M: $3.75

vs boards won of 37 shared

DeepSeek V4.1 Flash 1017 or Gemini 3.7 Flash: which to pick

Pick DeepSeek V4.1 Flash 1017 for coding: it scores 70.3 on the Vibe Coding Index against 58.0 for Gemini 3.7 Flash, though on 4 benchmarks against 6.

Pick Gemini 3.7 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 3.7 FlashGoogle58.0/1006 independent benchmarks$1.78 per solved task

37277

Shared results: 37
DeepSeek V4.1 Flash 1017 only: 2
Gemini 3.7 Flash only: 77
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 58.0.
4 and 6 benchmarks behind the scores
Intelligence
AA Intelligence Index
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 39.6 against 39.5.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Gemini 3.7 Flash has a GPQA Diamond result, 93.9%.
Directional only
Knowledge
MMLU Pro
No call
Only Gemini 3.7 Flash has a MMLU Pro result, 90.1%.
Directional only
Web design
Design Arena: websites
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 1311 against 1303.
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
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 3.7 Flash $0.0026 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Gemini 3.7 Flash has the number, $1.78.
Directional only

At a glance

Coding index: DeepSeek V4.1 Flash 1017 70.3 from 4 benchmarks, Gemini 3.7 Flash 58.0 from 6 benchmarks.
Widest category: Knowledge, 26.7 points apart on average over 1 shared benchmark.
Widest single gap: SimpleQA Verified: DeepSeek V4.1 Flash 1017 42.5% against Gemini 3.7 Flash 69.2%, 26.7 points apart on a 0 to 100 scale.
Output price: DeepSeek V4.1 Flash 1017 $0.60 against Gemini 3.7 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 3.7 Flash
Coding	8	1
Intelligence	0	1
Agentic work	6	2
Reasoning	2	3
Math	2	1
Knowledge	0	1
Web design	7	1
General	1	1
All shared boards	26	11

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

Coding benchmarks for DeepSeek V4.1 Flash 1017 and Gemini 3.7 Flash
Benchmark	DeepSeek V4.1 Flash 1017	Gemini 3.7 Flash	Leader
LiveBench coding	96.0%	80.5%	DeepSeek V4.1 Flash 1017
LMArena WebDev	1664	1591	DeepSeek V4.1 Flash 1017
AA Coding Index	Not measured	71.5	Only Gemini 3.7 Flash measured
LiveCodeBench (Vals AI)	Not measured	88.7%	Only Gemini 3.7 Flash measured
SWE-bench Verified (Vals AI)	Not measured	80.8%	Only Gemini 3.7 Flash measured
Terminal-Bench 4 (Vals AI)	31.8%	12.1%	DeepSeek V4.1 Flash 1017
Vibe Code Bench (Vals AI)	87.7%	70.4%	DeepSeek V4.1 Flash 1017
Code migration (Vals AI)	44.2%	34.8%	DeepSeek V4.1 Flash 1017
ProgramBench (Vals AI)	Not measured	0.0%	Only Gemini 3.7 Flash measured
IOI (Vals AI)	Not measured	67.8%	Only Gemini 3.7 Flash measured
Terminal-Bench 2.1 (Vals AI)	Not measured	77.5%	Only Gemini 3.7 Flash measured
SkillsBench (Vals AI)	Not measured	65.9%	Only Gemini 3.7 Flash measured
CyberBench (Vals AI)	Not measured	43.8%	Only Gemini 3.7 Flash measured
SciCode	55.9%	59.8%	Gemini 3.7 Flash
FrontierCode	Not measured	43.6%	Only Gemini 3.7 Flash measured
FrontierSWE	Not measured	20.3%	Only Gemini 3.7 Flash measured
ALE-Bench	1622.0	904.3	DeepSeek V4.1 Flash 1017
LMArena Text: coding	Not measured	1499	Only Gemini 3.7 Flash measured
LMArena Text: software and IT	Not measured	1498	Only Gemini 3.7 Flash measured
LMArena WebDev: React	1667	1587	DeepSeek V4.1 Flash 1017
LMArena WebDev: HTML	1687	1593	DeepSeek V4.1 Flash 1017
LMArena WebDev: from an image	Not measured	1599	Only Gemini 3.7 Flash measured
SciCode	Not measured	13.8%	Only Gemini 3.7 Flash measured
Neither model has a result on the other 16 boards here

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

Intelligence benchmarks for DeepSeek V4.1 Flash 1017 and Gemini 3.7 Flash
Benchmark	DeepSeek V4.1 Flash 1017	Gemini 3.7 Flash	Leader
AA Intelligence Index	39.5	39.6	Gemini 3.7 Flash
Epoch Capabilities Index	Not measured	158.0	Only Gemini 3.7 Flash measured

Agentic workLike-for-likePublic boards, shown as published, kept out of the index62

Agentic work benchmarks for DeepSeek V4.1 Flash 1017 and Gemini 3.7 Flash
Benchmark	DeepSeek V4.1 Flash 1017	Gemini 3.7 Flash	Leader
Design Arena: agents building web apps	Not measured	1223	Only Gemini 3.7 Flash measured
Design Arena: agents building full-stack apps	Not measured	1208	Only Gemini 3.7 Flash measured
Design Arena: agents building mobile apps	Not measured	1229	Only Gemini 3.7 Flash measured
Design Arena: agents building Android apps	Not measured	1243	Only Gemini 3.7 Flash measured
Design Arena: agents building Godot games	Not measured	1264	Only Gemini 3.7 Flash measured
Design Arena: agents building games	Not measured	1235	Only Gemini 3.7 Flash measured
Design Arena: spreadsheets	1245	1058	DeepSeek V4.1 Flash 1017
LiveBench research	79.5%	76.5%	DeepSeek V4.1 Flash 1017
LiveBench browser	76.0%	56.0%	DeepSeek V4.1 Flash 1017
LiveBench long agentic	50.9%	28.3%	DeepSeek V4.1 Flash 1017
SREBench (Vals AI)	Not measured	4.6%	Only Gemini 3.7 Flash measured
Finance Agent (Vals AI)	57.0%	59.0%	Gemini 3.7 Flash
Tax Agent Bench (Vals AI)	44.4%	22.8%	DeepSeek V4.1 Flash 1017
Legal Research Bench (Vals AI)	45.2%	34.6%	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%	71.3%	Gemini 3.7 Flash
Terminal-Bench-Science (Vals AI)	Not measured	7.1%	Only Gemini 3.7 Flash measured
APEX-Agents	Not measured	67.8%	Only Gemini 3.7 Flash measured
Remote Labor Index	Not measured	5.0%	Only Gemini 3.7 Flash measured
Neither model has a result on the other 22 boards here

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

Reasoning benchmarks for DeepSeek V4.1 Flash 1017 and Gemini 3.7 Flash
Benchmark	DeepSeek V4.1 Flash 1017	Gemini 3.7 Flash	Leader
GPQA Diamond	Not measured	93.9%	Only Gemini 3.7 Flash measured
LiveBench reasoning	89.1%	87.8%	DeepSeek V4.1 Flash 1017
MMMU Pro (Vals AI)	Not measured	89.0%	Only Gemini 3.7 Flash measured
GPQA Diamond (Artificial Analysis run)	Not measured	94.5%	Only Gemini 3.7 Flash measured
Humanity's Last Exam (Artificial Analysis run)	43.3%	47.9%	Gemini 3.7 Flash
GPQA Diamond (Epoch AI run)	93.2%	94.8%	Gemini 3.7 Flash
ARC-AGI	Not measured	95.5%	Only Gemini 3.7 Flash measured
ARC-AGI-2	Not measured	84.6%	Only Gemini 3.7 Flash measured
Chess puzzles	46.0%	66.0%	Gemini 3.7 Flash
Mystery game puzzles	49.0%	37.0%	DeepSeek V4.1 Flash 1017
LMArena Text: hard prompts	Not measured	1495	Only Gemini 3.7 Flash measured
LMArena Text: expert	Not measured	1512	Only Gemini 3.7 Flash measured
LMArena Text: hard prompts in English	Not measured	1487	Only Gemini 3.7 Flash measured
LMArena Vision: diagrams	Not measured	1329	Only Gemini 3.7 Flash measured
LMArena Vision: homework	Not measured	1320	Only Gemini 3.7 Flash measured
Neither model has a result on the other 7 boards here

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

Math benchmarks for DeepSeek V4.1 Flash 1017 and Gemini 3.7 Flash
Benchmark	DeepSeek V4.1 Flash 1017	Gemini 3.7 Flash	Leader
OTIS Mock AIME	99.4%	97.2%	DeepSeek V4.1 Flash 1017
FrontierMath, tiers 1 to 3	71.2%	71.6%	Gemini 3.7 Flash
FrontierMath, tier 4	46.3%	36.6%	DeepSeek V4.1 Flash 1017
ProofBench	Not measured	58.0%	Only Gemini 3.7 Flash measured
LMArena Text: math	Not measured	1511	Only Gemini 3.7 Flash measured
LMArena Text: mathematical work	Not measured	1502	Only Gemini 3.7 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 3.7 Flash
Benchmark	DeepSeek V4.1 Flash 1017	Gemini 3.7 Flash	Leader
MMLU Pro	Not measured	90.1%	Only Gemini 3.7 Flash measured
LegalBench (Vals AI)	Not measured	87.3%	Only Gemini 3.7 Flash measured
TaxEval (Vals AI)	Not measured	74.7%	Only Gemini 3.7 Flash measured
MortgageTax (Vals AI)	Not measured	66.7%	Only Gemini 3.7 Flash measured
MedCode (Vals AI)	Not measured	53.4%	Only Gemini 3.7 Flash measured
SimpleQA Verified	42.5%	69.2%	Gemini 3.7 Flash
LMArena Text: science	Not measured	1511	Only Gemini 3.7 Flash measured
LMArena Text: medicine and healthcare	Not measured	1494	Only Gemini 3.7 Flash measured
LMArena Text: legal and government	Not measured	1496	Only Gemini 3.7 Flash measured
LMArena Text: business and finance	Not measured	1479	Only Gemini 3.7 Flash measured
FACTS Benchmark Suite	Not measured	71.2%	Only Gemini 3.7 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 index71

Web design benchmarks for DeepSeek V4.1 Flash 1017 and Gemini 3.7 Flash
Benchmark	DeepSeek V4.1 Flash 1017	Gemini 3.7 Flash	Leader
Design Arena: websites	1311	1303	DeepSeek V4.1 Flash 1017
Design Arena: UI components	1303	1296	DeepSeek V4.1 Flash 1017
Design Arena: game dev	1357	1317	DeepSeek V4.1 Flash 1017
Design Arena: data viz	1330	1315	DeepSeek V4.1 Flash 1017
Design Arena: 3D	1358	1307	DeepSeek V4.1 Flash 1017
Design Arena: all code categories	1332	1308	DeepSeek V4.1 Flash 1017
Design Arena: SVG	Not measured	1283	Only Gemini 3.7 Flash measured
Design Arena: ASCII art	1242	1276	Gemini 3.7 Flash
LiveBench design	83.4%	73.2%	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 Gemini 3.7 Flash
Benchmark	DeepSeek V4.1 Flash 1017	Gemini 3.7 Flash	Leader
Vals Index	61.3%	Not measured	Only DeepSeek V4.1 Flash 1017 measured
LiveBench data analysis	79.5%	68.0%	DeepSeek V4.1 Flash 1017
SAGE (Vals AI)	Not measured	49.2%	Only Gemini 3.7 Flash measured
MedScribe (Vals AI)	Not measured	83.9%	Only Gemini 3.7 Flash measured
Long context reasoning (AA-LCR)	82.7%	83.0%	Gemini 3.7 Flash
DTBench	Not measured	96.8%	Only Gemini 3.7 Flash measured
LMCA	Not measured	50.4%	Only Gemini 3.7 Flash measured
Furniture assembly	Not measured	26.7%	Only Gemini 3.7 Flash measured
LMArena Text	Not measured	1489	Only Gemini 3.7 Flash measured
LMArena Text: instruction following	Not measured	1484	Only Gemini 3.7 Flash measured
LMArena Text: creative writing	Not measured	1488	Only Gemini 3.7 Flash measured
LMArena Vision	Not measured	1314	Only Gemini 3.7 Flash measured
LMArena Text: English	Not measured	1483	Only Gemini 3.7 Flash measured
LMArena Text: non-English	Not measured	1487	Only Gemini 3.7 Flash measured
LMArena Text: Chinese	Not measured	1552	Only Gemini 3.7 Flash measured
LMArena Text: German	Not measured	1506	Only Gemini 3.7 Flash measured
LMArena Text: Japanese	Not measured	1511	Only Gemini 3.7 Flash measured
LMArena Text: Korean	Not measured	1481	Only Gemini 3.7 Flash measured
LMArena Text: French	Not measured	1500	Only Gemini 3.7 Flash measured
LMArena Text: Spanish	Not measured	1495	Only Gemini 3.7 Flash measured
LMArena Text: Russian	Not measured	1515	Only Gemini 3.7 Flash measured
LMArena Text: Polish	Not measured	1503	Only Gemini 3.7 Flash measured
LMArena Text: multi-turn	Not measured	1485	Only Gemini 3.7 Flash measured
LMArena Text: longer queries	Not measured	1492	Only Gemini 3.7 Flash measured
LMArena Text: writing and language	Not measured	1491	Only Gemini 3.7 Flash measured
LMArena Text: entertainment and media	Not measured	1477	Only Gemini 3.7 Flash measured
LMArena Vision: English	Not measured	1309	Only Gemini 3.7 Flash measured
LMArena Vision: Chinese	Not measured	1394	Only Gemini 3.7 Flash measured
LMArena Vision: reading text	Not measured	1322	Only Gemini 3.7 Flash measured
LMArena Vision: humor	Not measured	1299	Only Gemini 3.7 Flash measured
LMArena Vision: creative writing	Not measured	1312	Only Gemini 3.7 Flash measured
Neither model has a result on the other 15 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 3.7 Flash
Benchmark	DeepSeek V4.1 Flash 1017	Gemini 3.7 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 3.7 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 3.7 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 3.7 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 3.7 Flash
Specification	DeepSeek V4.1 Flash 1017	Google Gemini 3.7 Flash
Maker	DeepSeek	Google
Weights	Open weights	No public weights listed
Released	October 17, 2026	August 13, 2026
Context window	1.05M	1.05M
Input $/Mtok	$0.15	$0.75
Output $/Mtok	$0.60	$3.75
Coding index	70.3	58.0
Independent measurements behind it	4	6
Cost per solved task	Not computable	$1.78
Source	OpenRouter listing	OpenRouter listing

Questions

Is DeepSeek V4.1 Flash 1017 better than Gemini 3.7 Flash for coding?

Pick DeepSeek V4.1 Flash 1017 for coding: it scores 70.3 on the Vibe Coding Index against 58.0 for Gemini 3.7 Flash, though on 4 benchmarks against 6. Pick Gemini 3.7 Flash for work measured by SciCode or AA Intelligence Index, where it leads.

Which is cheaper, DeepSeek V4.1 Flash 1017 or Gemini 3.7 Flash?

DeepSeek V4.1 Flash 1017 costs $0.15 per million input tokens and $0.60 per million output tokens; Gemini 3.7 Flash costs $0.75 and $3.75. Cost per solved task is known for Gemini 3.7 Flash only.

Which has the bigger context window, DeepSeek V4.1 Flash 1017 or Gemini 3.7 Flash?

Neither: both take 1.05M tokens.

How many benchmarks is the DeepSeek V4.1 Flash 1017 vs Gemini 3.7 Flash comparison based on?

37 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 3.7 Flash's on 6. Where only one model has a result, the page says so instead of naming a winner.

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<iframe src="https://llmbenchmark.ai/embed/compare/deepseek-v4-1-flash-1017-vs-gemini-3-7-flash" title="DeepSeek V4.1 Flash 1017 vs Gemini 3.7 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-3-7-flash">LLMBenchmark.ai, DeepSeek V4.1 Flash 1017 vs Gemini 3.7 Flash</a></p>

DeepSeek V4.1 Flash 1017 or Gemini 3.7 Flash: which to pick

Which one for your work

Coding

Intelligence

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