DeepSeek V4 Flash 0731 vs Gemini 3.5 Flash

DeepSeek V4 Flash 0731 leads Gemini 3.5 Flash on coding, 51.8 to 39.8 on the Vibe Coding Index, and DeepSeek V4 Flash 0731 costs 32.1x less per output token ($0.28 vs $9.00 per million). Updated October 21, 2026; 49 benchmarks have a result for both.

Replace DeepSeek V4 Flash 0731 Replace Gemini 3.5 Flash

DeepSeek V4 Flash 0731DeepSeek

Coding: 51.8
Overall: 55.2
Output $/M: $0.28

Gemini 3.5 FlashGoogle

Coding: 39.8
Overall: 63.8
Output $/M: $9.00

vs boards won of 49 shared, 1 tied

DeepSeek V4 Flash 0731 or Gemini 3.5 Flash: which to pick

Pick DeepSeek V4 Flash 0731 for coding: it scores 51.8 on the Vibe Coding Index against 39.8 for Gemini 3.5 Flash.

Pick Gemini 3.5 Flash for work measured by LiveCodeBench (Vals AI) or Terminal-Bench 2.1 (Vals AI), where it leads.

Model ADeepSeek V4 Flash 0731DeepSeek51.8/1004 independent benchmarks$0.01 per solved task Model BGemini 3.5 FlashGoogle39.8/1004 independent benchmarks$1.21 per solved task

49663

Shared results: 49
DeepSeek V4 Flash 0731 only: 6
Gemini 3.5 Flash only: 63
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 Flash 0731
DeepSeek V4 Flash 0731 scores 51.8 against 39.8.
4 benchmarks behind each score
Intelligence
AA Intelligence Index
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 leads, 34.3 against 33.6.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only DeepSeek V4 Flash 0731 has a AA Agentic Index result, 41.0.
Directional only
Reasoning
GPQA Diamond
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 92.7% against 89.9%.
Same board, both measured
Knowledge
MMLU Pro
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 89.5% against 86.2%.
Same board, both measured
Web design
Design Arena: websites
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 1268 against 1238.
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 Flash 0731
DeepSeek V4 Flash 0731 $0.0003, Gemini 3.5 Flash $0.0060 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 $0.01, Gemini 3.5 Flash $1.21.
Published attempt cost and pass rate

At a glance

Coding index: DeepSeek V4 Flash 0731 51.8 from 4 benchmarks, Gemini 3.5 Flash 39.8 from 4 benchmarks.
Widest category: Knowledge, 10.5 points apart on average over 6 shared benchmarks.
Widest single gap: SimpleQA Verified: DeepSeek V4 Flash 0731 33.6% against Gemini 3.5 Flash 66.2%, 32.6 points apart on a 0 to 100 scale.
Output price: DeepSeek V4 Flash 0731 $0.28 against Gemini 3.5 Flash $9.00 per million output tokens, 32.1x 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 Flash 0731	Gemini 3.5 Flash	Ties
Coding	6	4	1 tie
Intelligence	1	1
Agentic work	1	4
Reasoning	1	8
Math	1	3
Knowledge	0	6
Web design	0	8
General	2	2
All shared boards	12	36	1 tie

CodingLike-for-likeSix of these boards feed the Vibe Coding Index64

Coding benchmarks for DeepSeek V4 Flash 0731 and Gemini 3.5 Flash
Benchmark	DeepSeek V4 Flash 0731	Gemini 3.5 Flash	Leader
LMArena WebDev	Not measured	1499	Only Gemini 3.5 Flash measured
AA Coding Index	69.1	Not measured	Only DeepSeek V4 Flash 0731 measured
LiveCodeBench (Vals AI)	87.3%	87.6%	Gemini 3.5 Flash
SWE-bench Verified (Vals AI)	88.8%	78.8%	DeepSeek V4 Flash 0731
Terminal-Bench 4 (Vals AI)	18.7%	6.1%	DeepSeek V4 Flash 0731
Vibe Code Bench (Vals AI)	74.7%	48.7%	DeepSeek V4 Flash 0731
Code migration (Vals AI)	38.6%	26.8%	DeepSeek V4 Flash 0731
ProgramBench (Vals AI)	0.0%	0.0%	Tie
IOI (Vals AI)	32.7%	Not measured	Only DeepSeek V4 Flash 0731 measured
Terminal-Bench 2.1 (Vals AI)	67.0%	74.2%	Gemini 3.5 Flash
SkillsBench (Vals AI)	50.7%	52.7%	Gemini 3.5 Flash
CyberBench (Vals AI)	72.1%	Not measured	Only DeepSeek V4 Flash 0731 measured
SciCode	50.3%	53.9%	Gemini 3.5 Flash
Terminal-Bench Hard (Artificial Analysis run)	Not measured	46.2%	Only Gemini 3.5 Flash measured
FrontierCode	18.8%	Not measured	Only DeepSeek V4 Flash 0731 measured
WeirdML	63.0%	62.6%	DeepSeek V4 Flash 0731
ALE-Bench	1306.1	911.0	DeepSeek V4 Flash 0731
LMArena Text: coding	Not measured	1493	Only Gemini 3.5 Flash measured
LMArena Text: software and IT	Not measured	1494	Only Gemini 3.5 Flash measured
LMArena WebDev: React	Not measured	1495	Only Gemini 3.5 Flash measured
LMArena WebDev: HTML	Not measured	1522	Only Gemini 3.5 Flash measured
LMArena WebDev: from an image	Not measured	1504	Only Gemini 3.5 Flash measured
SciCode	Not measured	12.3%	Only Gemini 3.5 Flash measured
Neither model has a result on the other 16 boards here

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

Intelligence benchmarks for DeepSeek V4 Flash 0731 and Gemini 3.5 Flash
Benchmark	DeepSeek V4 Flash 0731	Gemini 3.5 Flash	Leader
AA Intelligence Index	34.3	33.6	DeepSeek V4 Flash 0731
Epoch Capabilities Index	154.8	154.9	Gemini 3.5 Flash

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

Agentic work benchmarks for DeepSeek V4 Flash 0731 and Gemini 3.5 Flash
Benchmark	DeepSeek V4 Flash 0731	Gemini 3.5 Flash	Leader
AA Agentic Index	41.0	Not measured	Only DeepSeek V4 Flash 0731 measured
Design Arena: agents building web apps	Not measured	1202	Only Gemini 3.5 Flash measured
Design Arena: agents building full-stack apps	Not measured	1197	Only Gemini 3.5 Flash measured
Design Arena: agents building mobile apps	Not measured	1174	Only Gemini 3.5 Flash measured
Design Arena: agents building Android apps	Not measured	1184	Only Gemini 3.5 Flash measured
Design Arena: agents building Godot games	Not measured	1098	Only Gemini 3.5 Flash measured
Design Arena: agents building games	Not measured	1180	Only Gemini 3.5 Flash measured
Design Arena: HTML slides	Not measured	1142	Only Gemini 3.5 Flash measured
Design Arena: PowerPoint slides	Not measured	1179	Only Gemini 3.5 Flash measured
Finance Agent (Vals AI)	49.5%	57.9%	Gemini 3.5 Flash
Tax Agent Bench (Vals AI)	28.1%	21.6%	DeepSeek V4 Flash 0731
Legal Research Bench (Vals AI)	30.3%	30.8%	Gemini 3.5 Flash
Excel Modeling Benchmark (Vals AI)	57.0%	63.5%	Gemini 3.5 Flash
BioMysteryBench (Vals AI)	64.4%	Not measured	Only DeepSeek V4 Flash 0731 measured
Terminal-Bench-Science (Vals AI)	4.3%	5.7%	Gemini 3.5 Flash
τ²-Bench Telecom	Not measured	95.6%	Only Gemini 3.5 Flash measured
APEX-Agents	Not measured	27.5%	Only Gemini 3.5 Flash measured
Vending-Bench 2	Not measured	$5,396	Only Gemini 3.5 Flash measured
Neither model has a result on the other 23 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index18

Reasoning benchmarks for DeepSeek V4 Flash 0731 and Gemini 3.5 Flash
Benchmark	DeepSeek V4 Flash 0731	Gemini 3.5 Flash	Leader
GPQA Diamond	89.9%	92.7%	Gemini 3.5 Flash
MMMU Pro (Vals AI)	Not measured	88.3%	Only Gemini 3.5 Flash measured
GPQA Diamond (Artificial Analysis run)	90.8%	92.2%	Gemini 3.5 Flash
Humanity's Last Exam (Artificial Analysis run)	38.6%	42.7%	Gemini 3.5 Flash
GPQA Diamond (Epoch AI run)	91.0%	92.8%	Gemini 3.5 Flash
ARC-AGI	89.0%	92.5%	Gemini 3.5 Flash
ARC-AGI-2	61.4%	72.1%	Gemini 3.5 Flash
SimpleBench	61.1%	76.7%	Gemini 3.5 Flash
Chess puzzles	32.0%	45.0%	Gemini 3.5 Flash
Mystery game puzzles	34.0%	32.0%	DeepSeek V4 Flash 0731
EBR-bench	Not measured	4.8%	Only Gemini 3.5 Flash measured
LMArena Text: hard prompts	Not measured	1488	Only Gemini 3.5 Flash measured
LMArena Text: expert	Not measured	1495	Only Gemini 3.5 Flash measured
LMArena Text: hard prompts in English	Not measured	1483	Only Gemini 3.5 Flash measured
LMArena Vision: diagrams	Not measured	1323	Only Gemini 3.5 Flash measured
LMArena Vision: homework	Not measured	1330	Only Gemini 3.5 Flash measured
Neither model has a result on the other 6 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index13

Math benchmarks for DeepSeek V4 Flash 0731 and Gemini 3.5 Flash
Benchmark	DeepSeek V4 Flash 0731	Gemini 3.5 Flash	Leader
OTIS Mock AIME	94.4%	95.6%	Gemini 3.5 Flash
FrontierMath, tiers 1 to 3	57.5%	62.8%	Gemini 3.5 Flash
FrontierMath, tier 4	24.4%	26.8%	Gemini 3.5 Flash
ProofBench	56.0%	31.0%	DeepSeek V4 Flash 0731
LMArena Text: math	Not measured	1504	Only Gemini 3.5 Flash measured
LMArena Text: mathematical work	Not measured	1495	Only Gemini 3.5 Flash measured
Neither model has a result on the other 7 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index06

Knowledge benchmarks for DeepSeek V4 Flash 0731 and Gemini 3.5 Flash
Benchmark	DeepSeek V4 Flash 0731	Gemini 3.5 Flash	Leader
MMLU Pro	86.2%	89.5%	Gemini 3.5 Flash
LegalBench (Vals AI)	77.7%	83.6%	Gemini 3.5 Flash
CorpFin (Vals AI)	61.9%	64.7%	Gemini 3.5 Flash
TaxEval (Vals AI)	70.7%	74.4%	Gemini 3.5 Flash
MortgageTax (Vals AI)	Not measured	68.1%	Only Gemini 3.5 Flash measured
MedCode (Vals AI)	41.4%	55.8%	Gemini 3.5 Flash
SimpleQA Verified	33.6%	66.2%	Gemini 3.5 Flash
LMArena Text: science	Not measured	1495	Only Gemini 3.5 Flash measured
LMArena Text: medicine and healthcare	Not measured	1483	Only Gemini 3.5 Flash measured
LMArena Text: legal and government	Not measured	1488	Only Gemini 3.5 Flash measured
LMArena Text: business and finance	Not measured	1467	Only Gemini 3.5 Flash measured
FACTS Benchmark Suite	Not measured	63.1%	Only Gemini 3.5 Flash measured
Neither model has a result on the other 6 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index08

Web design benchmarks for DeepSeek V4 Flash 0731 and Gemini 3.5 Flash
Benchmark	DeepSeek V4 Flash 0731	Gemini 3.5 Flash	Leader
Design Arena: websites	1238	1268	Gemini 3.5 Flash
Design Arena: UI components	1235	1273	Gemini 3.5 Flash
Design Arena: game dev	1208	1263	Gemini 3.5 Flash
Design Arena: data viz	1180	1236	Gemini 3.5 Flash
Design Arena: 3D	1209	1244	Gemini 3.5 Flash
Design Arena: all code categories	1230	1266	Gemini 3.5 Flash
Design Arena: SVG	1185	1254	Gemini 3.5 Flash
Design Arena: ASCII art	1090	1254	Gemini 3.5 Flash
Neither model has a result on the other 1 board here

GeneralLike-for-likePublic boards, shown as published, kept out of the index22

General benchmarks for DeepSeek V4 Flash 0731 and Gemini 3.5 Flash
Benchmark	DeepSeek V4 Flash 0731	Gemini 3.5 Flash	Leader
Public Benefits Bench (Vals AI)	Not measured	59.5%	Only Gemini 3.5 Flash measured
SAGE (Vals AI)	Not measured	49.9%	Only Gemini 3.5 Flash measured
MedScribe (Vals AI)	80.4%	76.6%	DeepSeek V4 Flash 0731
Vals Multimodal Index	Not measured	62.9%	Only Gemini 3.5 Flash measured
IFBench	Not measured	76.3%	Only Gemini 3.5 Flash measured
Long context reasoning (AA-LCR)	79.7%	74.3%	DeepSeek V4 Flash 0731
DTBench	90.9%	94.7%	Gemini 3.5 Flash
LMCA	41.7%	47.1%	Gemini 3.5 Flash
Surface Evolver Bench	Not measured	58.1%	Only Gemini 3.5 Flash measured
LMArena Text	Not measured	1482	Only Gemini 3.5 Flash measured
LMArena Text: instruction following	Not measured	1467	Only Gemini 3.5 Flash measured
LMArena Text: creative writing	Not measured	1470	Only Gemini 3.5 Flash measured
LMArena Vision	Not measured	1308	Only Gemini 3.5 Flash measured
LMArena Text: English	Not measured	1481	Only Gemini 3.5 Flash measured
LMArena Text: non-English	Not measured	1475	Only Gemini 3.5 Flash measured
LMArena Text: Chinese	Not measured	1527	Only Gemini 3.5 Flash measured
LMArena Text: German	Not measured	1494	Only Gemini 3.5 Flash measured
LMArena Text: Japanese	Not measured	1486	Only Gemini 3.5 Flash measured
LMArena Text: Korean	Not measured	1448	Only Gemini 3.5 Flash measured
LMArena Text: French	Not measured	1490	Only Gemini 3.5 Flash measured
LMArena Text: Spanish	Not measured	1480	Only Gemini 3.5 Flash measured
LMArena Text: Russian	Not measured	1493	Only Gemini 3.5 Flash measured
LMArena Text: Polish	Not measured	1516	Only Gemini 3.5 Flash measured
LMArena Text: multi-turn	Not measured	1481	Only Gemini 3.5 Flash measured
LMArena Text: longer queries	Not measured	1483	Only Gemini 3.5 Flash measured
LMArena Text: writing and language	Not measured	1478	Only Gemini 3.5 Flash measured
LMArena Text: entertainment and media	Not measured	1459	Only Gemini 3.5 Flash measured
LMArena Vision: English	Not measured	1305	Only Gemini 3.5 Flash measured
LMArena Vision: Chinese	Not measured	1377	Only Gemini 3.5 Flash measured
LMArena Vision: reading text	Not measured	1317	Only Gemini 3.5 Flash measured
LMArena Vision: humor	Not measured	1321	Only Gemini 3.5 Flash measured
LMArena Vision: creative writing	Not measured	1301	Only Gemini 3.5 Flash measured
LMArena Document	Not measured	1463	Only Gemini 3.5 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 Flash 0731 and Gemini 3.5 Flash
Benchmark	DeepSeek V4 Flash 0731	Gemini 3.5 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 Flash 0731$0.0003Fits in one request

Gemini 3.5 Flash$0.0060Fits in one request

DeepSeek V4 Flash 0731 costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Flash 0731$0.0078Fits in one request

Gemini 3.5 Flash$0.102Fits in one request

DeepSeek V4 Flash 0731 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Flash 0731$0.031Fits in one request

Gemini 3.5 Flash$0.390Fits in one request

DeepSeek V4 Flash 0731 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 Flash 0731 and Gemini 3.5 Flash
Specification	DeepSeek V4 Flash 0731	Google Gemini 3.5 Flash
Maker	DeepSeek	Google
Weights	Open weights	No public weights listed
Released	July 31, 2026	May 19, 2026
Context window	1.05M	1.05M
Input $/Mtok	$0.14	$1.50
Output $/Mtok	$0.28	$9.00
Coding index	51.8	39.8
Independent measurements behind it	4	4
Cost per solved task	$0.01	$1.21
Source	OpenRouter listing	OpenRouter listing

Questions

Is DeepSeek V4 Flash 0731 better than Gemini 3.5 Flash for coding?

Pick DeepSeek V4 Flash 0731 for coding: it scores 51.8 on the Vibe Coding Index against 39.8 for Gemini 3.5 Flash. Pick Gemini 3.5 Flash for work measured by LiveCodeBench (Vals AI) or Terminal-Bench 2.1 (Vals AI), where it leads.

Which is cheaper, DeepSeek V4 Flash 0731 or Gemini 3.5 Flash?

DeepSeek V4 Flash 0731 costs $0.14 per million input tokens and $0.28 per million output tokens; Gemini 3.5 Flash costs $1.50 and $9.00. Per solved SWE-bench Verified task, DeepSeek V4 Flash 0731 costs $0.01 and Gemini 3.5 Flash $1.21.

Which has the bigger context window, DeepSeek V4 Flash 0731 or Gemini 3.5 Flash?

Neither: both take 1.05M tokens.

How many benchmarks is the DeepSeek V4 Flash 0731 vs Gemini 3.5 Flash comparison based on?

49 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4 Flash 0731's coding index rests on 4 independent benchmarks and Gemini 3.5 Flash's on 4. 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-flash-0731-vs-gemini-3-5-flash" title="DeepSeek V4 Flash 0731 vs Gemini 3.5 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-flash-0731-vs-gemini-3-5-flash">LLMBenchmark.ai, DeepSeek V4 Flash 0731 vs Gemini 3.5 Flash</a></p>

DeepSeek V4 Flash 0731 or Gemini 3.5 Flash: 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