DeepSeek V4 Flash 0731 vs Gemini 3.8 Flash

Gemini 3.8 Flash leads DeepSeek V4 Flash 0731 on coding, 61.6 to 51.8 on the Vibe Coding Index, and DeepSeek V4 Flash 0731 costs 13.4x less per output token ($0.28 vs $3.75 per million). Updated October 21, 2026; 53 benchmarks have a result for both.

Replace DeepSeek V4 Flash 0731 Replace Gemini 3.8 Flash

DeepSeek V4 Flash 0731DeepSeek

Coding: 51.8
Overall: 34.3
Output $/M: $0.28

Gemini 3.8 FlashGoogle

Coding: 61.6
Overall: 40.9
Output $/M: $3.75

vs boards won of 53 shared

DeepSeek V4 Flash 0731 or Gemini 3.8 Flash: which to pick

Pick Gemini 3.8 Flash for coding: it scores 61.6 on the Vibe Coding Index against 51.8 for DeepSeek V4 Flash 0731.

Pick DeepSeek V4 Flash 0731 for output tokens 13.4x cheaper ($0.28 vs $3.75 per million) and a lower cost per solved task ($0.01 against $2.74).

Model ADeepSeek V4 Flash 0731DeepSeek51.8/1004 independent benchmarks$0.01 per solved task Model BGemini 3.8 FlashGoogle61.6/1006 independent benchmarks$2.74 per solved task

53273

Shared results: 53
DeepSeek V4 Flash 0731 only: 2
Gemini 3.8 Flash only: 73
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
Gemini 3.8 Flash
Gemini 3.8 Flash scores 61.6 against 51.8.
4 and 6 benchmarks behind the scores
Intelligence
AA Intelligence Index
Gemini 3.8 Flash
Gemini 3.8 Flash leads, 40.9 against 34.3.
Same board, both measured
Agentic work
AA Agentic Index
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 leads, 41.0 against 40.2.
Same board, both measured
Reasoning
GPQA Diamond
Gemini 3.8 Flash
Gemini 3.8 Flash leads, 94.4% against 89.9%.
Same board, both measured
Knowledge
MMLU Pro
Gemini 3.8 Flash
Gemini 3.8 Flash leads, 90.2% against 86.2%.
Same board, both measured
Web design
Design Arena: websites
Gemini 3.8 Flash
Gemini 3.8 Flash leads, 1300 against 1238.
Same board, both measured
General
Vals Index
No call
Only Gemini 3.8 Flash has a Vals Index result, 56.0%.
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 Flash 0731
DeepSeek V4 Flash 0731 $0.0003, Gemini 3.8 Flash $0.0026 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 3.8 Flash $2.74.
Published attempt cost and pass rate

At a glance

Coding index: DeepSeek V4 Flash 0731 51.8 from 4 benchmarks, Gemini 3.8 Flash 61.6 from 6 benchmarks.
Widest category: Reasoning, 12.4 points apart on average over 8 shared benchmarks.
Widest single gap: SimpleQA Verified: DeepSeek V4 Flash 0731 33.6% against Gemini 3.8 Flash 69.7%, 36.1 points apart on a 0 to 100 scale.
Output price: DeepSeek V4 Flash 0731 $0.28 against Gemini 3.8 Flash $3.75 per million output tokens, 13.4x 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 3.8 Flash
Coding	4	11
Intelligence	0	2
Agentic work	2	5
Reasoning	0	8
Math	2	2
Knowledge	0	5
Web design	0	8
General	0	4
All shared boards	8	45

CodingLike-for-likeSix of these boards feed the Vibe Coding Index411

Coding benchmarks for DeepSeek V4 Flash 0731 and Gemini 3.8 Flash
Benchmark	DeepSeek V4 Flash 0731	Gemini 3.8 Flash	Leader
LiveBench coding	Not measured	82.2%	Only Gemini 3.8 Flash measured
LMArena WebDev	Not measured	1585	Only Gemini 3.8 Flash measured
AA Coding Index	69.1	76.3	Gemini 3.8 Flash
LiveCodeBench (Vals AI)	87.3%	89.5%	Gemini 3.8 Flash
SWE-bench Verified (Vals AI)	88.8%	80.0%	DeepSeek V4 Flash 0731
Terminal-Bench 4 (Vals AI)	18.7%	19.7%	Gemini 3.8 Flash
Vibe Code Bench (Vals AI)	74.7%	78.7%	Gemini 3.8 Flash
Code migration (Vals AI)	38.6%	36.5%	DeepSeek V4 Flash 0731
ProgramBench (Vals AI)	0.0%	1.0%	Gemini 3.8 Flash
IOI (Vals AI)	32.7%	56.9%	Gemini 3.8 Flash
Terminal-Bench 2.1 (Vals AI)	67.0%	81.3%	Gemini 3.8 Flash
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)	50.7%	58.0%	Gemini 3.8 Flash
CyberBench (Vals AI)	72.1%	43.8%	DeepSeek V4 Flash 0731
SciCode	50.3%	56.6%	Gemini 3.8 Flash
FrontierCode	18.8%	41.2%	Gemini 3.8 Flash
FrontierSWE	Not measured	19.6%	Only Gemini 3.8 Flash measured
WeirdML	63.0%	84.8%	Gemini 3.8 Flash
ALE-Bench	1306.1	1269.7	DeepSeek V4 Flash 0731
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	Not measured	1577	Only Gemini 3.8 Flash measured
LMArena WebDev: HTML	Not measured	1616	Only Gemini 3.8 Flash measured
LMArena WebDev: from an image	Not measured	1593	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 index02

Intelligence benchmarks for DeepSeek V4 Flash 0731 and Gemini 3.8 Flash
Benchmark	DeepSeek V4 Flash 0731	Gemini 3.8 Flash	Leader
AA Intelligence Index	34.3	40.9	Gemini 3.8 Flash
Epoch Capabilities Index	154.8	157.4	Gemini 3.8 Flash

Agentic workLike-for-likePublic boards, shown as published, kept out of the index25

Agentic work benchmarks for DeepSeek V4 Flash 0731 and Gemini 3.8 Flash
Benchmark	DeepSeek V4 Flash 0731	Gemini 3.8 Flash	Leader
AA Agentic Index	41.0	40.2	DeepSeek V4 Flash 0731
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	1074	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	1177	Only Gemini 3.8 Flash measured
Design Arena: spreadsheets	Not measured	1056	Only Gemini 3.8 Flash measured
LiveBench research	Not measured	79.1%	Only Gemini 3.8 Flash measured
LiveBench browser	Not measured	64.0%	Only Gemini 3.8 Flash measured
LiveBench long agentic	Not measured	30.1%	Only Gemini 3.8 Flash measured
SREBench (Vals AI)	Not measured	11.1%	Only Gemini 3.8 Flash measured
Finance Agent (Vals AI)	49.5%	61.4%	Gemini 3.8 Flash
Tax Agent Bench (Vals AI)	28.1%	32.1%	Gemini 3.8 Flash
Legal Research Bench (Vals AI)	30.3%	38.9%	Gemini 3.8 Flash
Harvey's Legal Agent Benchmark (Vals AI)	Not measured	12.1%	Only Gemini 3.8 Flash measured
Excel Modeling Benchmark (Vals AI)	57.0%	72.2%	Gemini 3.8 Flash
BioMysteryBench (Vals AI)	64.4%	62.2%	DeepSeek V4 Flash 0731
Terminal-Bench-Science (Vals AI)	4.3%	8.6%	Gemini 3.8 Flash
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 14 boards here

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

Reasoning benchmarks for DeepSeek V4 Flash 0731 and Gemini 3.8 Flash
Benchmark	DeepSeek V4 Flash 0731	Gemini 3.8 Flash	Leader
GPQA Diamond	89.9%	94.4%	Gemini 3.8 Flash
LiveBench reasoning	Not measured	89.3%	Only Gemini 3.8 Flash measured
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)	90.8%	95.3%	Gemini 3.8 Flash
Humanity's Last Exam (Artificial Analysis run)	38.6%	47.8%	Gemini 3.8 Flash
GPQA Diamond (Epoch AI run)	91.0%	95.4%	Gemini 3.8 Flash
ARC-AGI	89.0%	98.5%	Gemini 3.8 Flash
ARC-AGI-2	61.4%	89.2%	Gemini 3.8 Flash
SimpleBench	61.1%	Not measured	Only DeepSeek V4 Flash 0731 measured
Chess puzzles	32.0%	58.0%	Gemini 3.8 Flash
Mystery game puzzles	34.0%	47.0%	Gemini 3.8 Flash
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 4 boards here

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

Math benchmarks for DeepSeek V4 Flash 0731 and Gemini 3.8 Flash
Benchmark	DeepSeek V4 Flash 0731	Gemini 3.8 Flash	Leader
OTIS Mock AIME	94.4%	98.9%	Gemini 3.8 Flash
FrontierMath, tiers 1 to 3	57.5%	68.4%	Gemini 3.8 Flash
FrontierMath, tier 4	24.4%	22.0%	DeepSeek V4 Flash 0731
ProofBench	56.0%	48.0%	DeepSeek V4 Flash 0731
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 index05

Knowledge benchmarks for DeepSeek V4 Flash 0731 and Gemini 3.8 Flash
Benchmark	DeepSeek V4 Flash 0731	Gemini 3.8 Flash	Leader
MMLU Pro	86.2%	90.2%	Gemini 3.8 Flash
LegalBench (Vals AI)	77.7%	87.0%	Gemini 3.8 Flash
CorpFin (Vals AI)	61.9%	Not measured	Only DeepSeek V4 Flash 0731 measured
TaxEval (Vals AI)	70.7%	74.5%	Gemini 3.8 Flash
MortgageTax (Vals AI)	Not measured	65.3%	Only Gemini 3.8 Flash measured
MedCode (Vals AI)	41.4%	48.1%	Gemini 3.8 Flash
SimpleQA Verified	33.6%	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
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 3.8 Flash
Benchmark	DeepSeek V4 Flash 0731	Gemini 3.8 Flash	Leader
Design Arena: websites	1238	1300	Gemini 3.8 Flash
Design Arena: UI components	1235	1319	Gemini 3.8 Flash
Design Arena: game dev	1208	1305	Gemini 3.8 Flash
Design Arena: data viz	1180	1269	Gemini 3.8 Flash
Design Arena: 3D	1209	1295	Gemini 3.8 Flash
Design Arena: all code categories	1230	1304	Gemini 3.8 Flash
Design Arena: SVG	1185	1295	Gemini 3.8 Flash
Design Arena: ASCII art	1090	1265	Gemini 3.8 Flash
LiveBench design	Not measured	72.7%	Only Gemini 3.8 Flash measured

GeneralLike-for-likePublic boards, shown as published, kept out of the index04

General benchmarks for DeepSeek V4 Flash 0731 and Gemini 3.8 Flash
Benchmark	DeepSeek V4 Flash 0731	Gemini 3.8 Flash	Leader
Vals Index	Not measured	56.0%	Only Gemini 3.8 Flash measured
LiveBench data analysis	Not measured	54.0%	Only Gemini 3.8 Flash measured
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)	80.4%	84.5%	Gemini 3.8 Flash
Long context reasoning (AA-LCR)	79.7%	84.0%	Gemini 3.8 Flash
DTBench	90.9%	95.7%	Gemini 3.8 Flash
LMCA	41.7%	52.9%	Gemini 3.8 Flash
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 Flash 0731 and Gemini 3.8 Flash
Benchmark	DeepSeek V4 Flash 0731	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 Flash 0731$0.0003Fits in one request

Gemini 3.8 Flash$0.0026Fits 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 3.8 Flash$0.049Fits 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 3.8 Flash$0.188Fits 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 3.8 Flash
Specification	DeepSeek V4 Flash 0731	Google Gemini 3.8 Flash
Maker	DeepSeek	Google
Weights	Open weights	No public weights listed
Released	July 31, 2026	September 2, 2026
Context window	1.05M	1.05M
Input $/Mtok	$0.14	$0.75
Output $/Mtok	$0.28	$3.75
Coding index	51.8	61.6
Independent measurements behind it	4	6
Cost per solved task	$0.01	$2.74
Source	OpenRouter listing	OpenRouter listing

Questions

Is DeepSeek V4 Flash 0731 better than Gemini 3.8 Flash for coding?

Pick Gemini 3.8 Flash for coding: it scores 61.6 on the Vibe Coding Index against 51.8 for DeepSeek V4 Flash 0731. Pick DeepSeek V4 Flash 0731 for output tokens 13.4x cheaper ($0.28 vs $3.75 per million) and a lower cost per solved task ($0.01 against $2.74).

Which is cheaper, DeepSeek V4 Flash 0731 or Gemini 3.8 Flash?

DeepSeek V4 Flash 0731 costs $0.14 per million input tokens and $0.28 per million output tokens; Gemini 3.8 Flash costs $0.75 and $3.75. Per solved SWE-bench Verified task, DeepSeek V4 Flash 0731 costs $0.01 and Gemini 3.8 Flash $2.74.

Which has the bigger context window, DeepSeek V4 Flash 0731 or Gemini 3.8 Flash?

Neither: both take 1.05M tokens.

How many benchmarks is the DeepSeek V4 Flash 0731 vs Gemini 3.8 Flash comparison based on?

53 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 3.8 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-flash-0731-vs-gemini-3-8-flash" title="DeepSeek V4 Flash 0731 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-flash-0731-vs-gemini-3-8-flash">LLMBenchmark.ai, DeepSeek V4 Flash 0731 vs Gemini 3.8 Flash</a></p>

DeepSeek V4 Flash 0731 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