DeepSeek V4 Pro 0813 vs Gemini 3.8 Flash

Gemini 3.8 Flash leads DeepSeek V4 Pro 0813 on coding, 61.6 to 56.1 on the Vibe Coding Index, and DeepSeek V4 Pro 0813 costs 1.9x less per output token ($1.98 vs $3.75 per million). Updated October 22, 2026; 77 benchmarks have a result for both.

Replace DeepSeek V4 Pro 0813 Replace Gemini 3.8 Flash

DeepSeek V4 Pro 0813DeepSeek

Coding: 56.1
Overall: 60.8
Output $/M: $1.98

Gemini 3.8 FlashGoogle

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

vs boards won of 77 shared

DeepSeek V4 Pro 0813 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 56.1 for DeepSeek V4 Pro 0813.

Pick DeepSeek V4 Pro 0813 for output tokens 1.9x cheaper ($1.98 vs $3.75 per million) and a lower cost per solved task ($0.10 against $2.74).

Model ADeepSeek V4 Pro 0813DeepSeek56.1/1005 independent benchmarks$0.10 per solved task Model BGemini 3.8 FlashGoogle61.6/1006 independent benchmarks$2.74 per solved task

77151

Shared results: 77
DeepSeek V4 Pro 0813 only: 1
Gemini 3.8 Flash only: 51
Like-for-like categories: 7 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 56.1.
5 and 6 benchmarks behind the scores
Intelligence
AA Intelligence Index
Gemini 3.8 Flash
Gemini 3.8 Flash leads, 40.9 against 36.0.
Same board, both measured
Agentic work
AA Agentic Index
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 leads, 41.3 against 40.2.
Same board, both measured
Reasoning
GPQA Diamond
Gemini 3.8 Flash
Gemini 3.8 Flash leads, 94.4% against 92.4%.
Same board, both measured
Knowledge
MMLU Pro
Gemini 3.8 Flash
Gemini 3.8 Flash leads, 90.2% against 87.0%.
Same board, both measured
Web design
Design Arena: websites
No call
Only Gemini 3.8 Flash has a Design Arena: websites result, 1300.
Directional only
General
Vals Index
Gemini 3.8 Flash
Gemini 3.8 Flash leads, 56.0% against 53.0%.
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 Pro 0813
DeepSeek V4 Pro 0813 $0.0016, Gemini 3.8 Flash $0.0026 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 $0.10, Gemini 3.8 Flash $2.74.
Published attempt cost and pass rate

At a glance

Coding index: DeepSeek V4 Pro 0813 56.1 from 5 benchmarks, Gemini 3.8 Flash 61.6 from 6 benchmarks.
Widest category: Coding, 8.5 points apart on average over 21 shared benchmarks.
Widest single gap: CyberBench (Vals AI): DeepSeek V4 Pro 0813 72.9% against Gemini 3.8 Flash 43.8%, 29.1 points apart on a 0 to 100 scale.
Output price: DeepSeek V4 Pro 0813 $1.98 against Gemini 3.8 Flash $3.75 per million output tokens, 1.9x 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 Pro 0813	Gemini 3.8 Flash
Coding	6	15
Intelligence	0	2
Agentic work	2	7
Reasoning	0	11
Math	2	4
Knowledge	0	9
General	0	19
All shared boards	10	67

CodingLike-for-likeSix of these boards feed the Vibe Coding Index615

Coding benchmarks for DeepSeek V4 Pro 0813 and Gemini 3.8 Flash
Benchmark	DeepSeek V4 Pro 0813	Gemini 3.8 Flash	Leader
LiveBench coding	Not measured	82.2%	Only Gemini 3.8 Flash measured
LMArena WebDev	1582	1585	Gemini 3.8 Flash
AA Coding Index	68.8	76.3	Gemini 3.8 Flash
LiveCodeBench (Vals AI)	87.5%	89.5%	Gemini 3.8 Flash
SWE-bench Verified (Vals AI)	96.4%	80.0%	DeepSeek V4 Pro 0813
Terminal-Bench 4 (Vals AI)	14.1%	19.7%	Gemini 3.8 Flash
Vibe Code Bench (Vals AI)	82.3%	78.7%	DeepSeek V4 Pro 0813
Code migration (Vals AI)	41.5%	36.5%	DeepSeek V4 Pro 0813
ProgramBench (Vals AI)	0.0%	1.0%	Gemini 3.8 Flash
IOI (Vals AI)	51.6%	56.9%	Gemini 3.8 Flash
Terminal-Bench 2.1 (Vals AI)	54.7%	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)	17.5%	18.8%	Gemini 3.8 Flash
SkillsBench (Vals AI)	53.8%	58.0%	Gemini 3.8 Flash
CyberBench (Vals AI)	72.9%	43.8%	DeepSeek V4 Pro 0813
SciCode	51.0%	56.6%	Gemini 3.8 Flash
FrontierCode	28.5%	41.2%	Gemini 3.8 Flash
FrontierSWE	Not measured	19.6%	Only Gemini 3.8 Flash measured
WeirdML	66.2%	84.8%	Gemini 3.8 Flash
ALE-Bench	1403.2	1269.7	DeepSeek V4 Pro 0813
LMArena Text: coding	1469	1508	Gemini 3.8 Flash
LMArena Text: software and IT	1462	1507	Gemini 3.8 Flash
LMArena WebDev: React	1588	1577	DeepSeek V4 Pro 0813
LMArena WebDev: HTML	1566	1616	Gemini 3.8 Flash
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 index02

Intelligence benchmarks for DeepSeek V4 Pro 0813 and Gemini 3.8 Flash
Benchmark	DeepSeek V4 Pro 0813	Gemini 3.8 Flash	Leader
AA Intelligence Index	36.0	40.9	Gemini 3.8 Flash
Epoch Capabilities Index	155.6	157.4	Gemini 3.8 Flash

Agentic workLike-for-likePublic boards, shown as published, kept out of the index27

Agentic work benchmarks for DeepSeek V4 Pro 0813 and Gemini 3.8 Flash
Benchmark	DeepSeek V4 Pro 0813	Gemini 3.8 Flash	Leader
AA Agentic Index	41.3	40.2	DeepSeek V4 Pro 0813
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	Not measured	1022	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)	1.9%	11.1%	Gemini 3.8 Flash
Finance Agent (Vals AI)	50.4%	61.4%	Gemini 3.8 Flash
Tax Agent Bench (Vals AI)	26.8%	32.1%	Gemini 3.8 Flash
Legal Research Bench (Vals AI)	40.9%	38.9%	DeepSeek V4 Pro 0813
Harvey's Legal Agent Benchmark (Vals AI)	10.8%	12.1%	Gemini 3.8 Flash
Excel Modeling Benchmark (Vals AI)	52.8%	72.2%	Gemini 3.8 Flash
BioMysteryBench (Vals AI)	Not measured	62.2%	Only Gemini 3.8 Flash measured
Terminal-Bench-Science (Vals AI)	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	47.3%	64.3%	Gemini 3.8 Flash
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 index011

Reasoning benchmarks for DeepSeek V4 Pro 0813 and Gemini 3.8 Flash
Benchmark	DeepSeek V4 Pro 0813	Gemini 3.8 Flash	Leader
GPQA Diamond	92.4%	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)	92.8%	95.3%	Gemini 3.8 Flash
Humanity's Last Exam (Artificial Analysis run)	41.0%	47.8%	Gemini 3.8 Flash
GPQA Diamond (Epoch AI run)	91.7%	95.4%	Gemini 3.8 Flash
ARC-AGI	90.5%	98.5%	Gemini 3.8 Flash
ARC-AGI-2	61.3%	89.2%	Gemini 3.8 Flash
Chess puzzles	46.0%	58.0%	Gemini 3.8 Flash
Mystery game puzzles	43.0%	47.0%	Gemini 3.8 Flash
LMArena Text: hard prompts	1458	1509	Gemini 3.8 Flash
LMArena Text: expert	1459	1526	Gemini 3.8 Flash
LMArena Text: hard prompts in English	1463	1504	Gemini 3.8 Flash
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 index24

Math benchmarks for DeepSeek V4 Pro 0813 and Gemini 3.8 Flash
Benchmark	DeepSeek V4 Pro 0813	Gemini 3.8 Flash	Leader
OTIS Mock AIME	98.6%	98.9%	Gemini 3.8 Flash
FrontierMath, tiers 1 to 3	64.6%	68.4%	Gemini 3.8 Flash
FrontierMath, tier 4	26.8%	22.0%	DeepSeek V4 Pro 0813
ProofBench	50.0%	48.0%	DeepSeek V4 Pro 0813
LMArena Text: math	1441	1532	Gemini 3.8 Flash
LMArena Text: mathematical work	1443	1519	Gemini 3.8 Flash
Neither model has a result on the other 7 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index09

Knowledge benchmarks for DeepSeek V4 Pro 0813 and Gemini 3.8 Flash
Benchmark	DeepSeek V4 Pro 0813	Gemini 3.8 Flash	Leader
MMLU Pro	87.0%	90.2%	Gemini 3.8 Flash
LegalBench (Vals AI)	82.4%	87.0%	Gemini 3.8 Flash
CorpFin (Vals AI)	65.4%	Not measured	Only DeepSeek V4 Pro 0813 measured
TaxEval (Vals AI)	73.1%	74.5%	Gemini 3.8 Flash
MortgageTax (Vals AI)	Not measured	65.3%	Only Gemini 3.8 Flash measured
MedCode (Vals AI)	42.5%	48.1%	Gemini 3.8 Flash
SimpleQA Verified	52.9%	69.7%	Gemini 3.8 Flash
LMArena Text: science	1457	1523	Gemini 3.8 Flash
LMArena Text: medicine and healthcare	1446	1508	Gemini 3.8 Flash
LMArena Text: legal and government	1459	1497	Gemini 3.8 Flash
LMArena Text: business and finance	1442	1479	Gemini 3.8 Flash
FACTS Benchmark Suite	Not measured	69.4%	Only Gemini 3.8 Flash measured
Neither model has a result on the other 6 boards here

Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared

Web design benchmarks for DeepSeek V4 Pro 0813 and Gemini 3.8 Flash
Benchmark	DeepSeek V4 Pro 0813	Gemini 3.8 Flash	Leader
Design Arena: websites	Not measured	1300	Only Gemini 3.8 Flash measured
Design Arena: UI components	Not measured	1316	Only Gemini 3.8 Flash measured
Design Arena: game dev	Not measured	1304	Only Gemini 3.8 Flash measured
Design Arena: data viz	Not measured	1270	Only Gemini 3.8 Flash measured
Design Arena: 3D	Not measured	1294	Only Gemini 3.8 Flash measured
Design Arena: all code categories	Not measured	1303	Only Gemini 3.8 Flash measured
Design Arena: SVG	Not measured	1296	Only Gemini 3.8 Flash measured
Design Arena: ASCII art	Not measured	1262	Only Gemini 3.8 Flash measured
LiveBench design	Not measured	72.7%	Only Gemini 3.8 Flash measured

GeneralLike-for-likePublic boards, shown as published, kept out of the index019

General benchmarks for DeepSeek V4 Pro 0813 and Gemini 3.8 Flash
Benchmark	DeepSeek V4 Pro 0813	Gemini 3.8 Flash	Leader
Vals Index	53.0%	56.0%	Gemini 3.8 Flash
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)	80.2%	84.5%	Gemini 3.8 Flash
Long context reasoning (AA-LCR)	80.3%	84.0%	Gemini 3.8 Flash
DTBench	93.9%	95.7%	Gemini 3.8 Flash
LMCA	45.5%	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	1446	1499	Gemini 3.8 Flash
LMArena Text: instruction following	1448	1492	Gemini 3.8 Flash
LMArena Text: creative writing	1439	1488	Gemini 3.8 Flash
LMArena Vision	Not measured	1315	Only Gemini 3.8 Flash measured
LMArena Text: English	1457	1498	Gemini 3.8 Flash
LMArena Text: non-English	1431	1491	Gemini 3.8 Flash
LMArena Text: Chinese	1481	1553	Gemini 3.8 Flash
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	1449	1464	Gemini 3.8 Flash
LMArena Text: French	1446	1496	Gemini 3.8 Flash
LMArena Text: Spanish	1435	1487	Gemini 3.8 Flash
LMArena Text: Russian	1448	1514	Gemini 3.8 Flash
LMArena Text: Polish	Not measured	1497	Only Gemini 3.8 Flash measured
LMArena Text: multi-turn	1454	1503	Gemini 3.8 Flash
LMArena Text: longer queries	1458	1507	Gemini 3.8 Flash
LMArena Text: writing and language	1443	1494	Gemini 3.8 Flash
LMArena Text: entertainment and media	1424	1480	Gemini 3.8 Flash
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 Pro 0813 and Gemini 3.8 Flash
Benchmark	DeepSeek V4 Pro 0813	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 Pro 0813$0.0016Fits in one request

Gemini 3.8 Flash$0.0026Fits in one request

DeepSeek V4 Pro 0813 costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Pro 0813$0.039Fits in one request

Gemini 3.8 Flash$0.049Fits in one request

DeepSeek V4 Pro 0813 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Pro 0813$0.152Fits in one request

Gemini 3.8 Flash$0.188Fits in one request

DeepSeek V4 Pro 0813 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 Pro 0813 and Gemini 3.8 Flash
Specification	DeepSeek V4 Pro 0813	Google Gemini 3.8 Flash
Maker	DeepSeek	Google
Weights	Open weights	No public weights listed
Released	August 12, 2026	September 2, 2026
Context window	1.05M	1.05M
Input $/Mtok	$0.66	$0.75
Output $/Mtok	$1.98	$3.75
Coding index	56.1	61.6
Independent measurements behind it	5	6
Cost per solved task	$0.10	$2.74
Source	OpenRouter listing	OpenRouter listing

Questions

Is DeepSeek V4 Pro 0813 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 56.1 for DeepSeek V4 Pro 0813. Pick DeepSeek V4 Pro 0813 for output tokens 1.9x cheaper ($1.98 vs $3.75 per million) and a lower cost per solved task ($0.10 against $2.74).

Which is cheaper, DeepSeek V4 Pro 0813 or Gemini 3.8 Flash?

DeepSeek V4 Pro 0813 costs $0.66 per million input tokens and $1.98 per million output tokens; Gemini 3.8 Flash costs $0.75 and $3.75. Per solved SWE-bench Verified task, DeepSeek V4 Pro 0813 costs $0.10 and Gemini 3.8 Flash $2.74.

Which has the bigger context window, DeepSeek V4 Pro 0813 or Gemini 3.8 Flash?

Neither: both take 1.05M tokens.

How many benchmarks is the DeepSeek V4 Pro 0813 vs Gemini 3.8 Flash comparison based on?

77 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Pro 0813's coding index rests on 5 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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DeepSeek V4 Pro 0813 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