DeepSeek V4 Pro 0423 vs Gemini 3.5 Flash Lite

Gemini 3.5 Flash Lite leads DeepSeek V4 Pro 0423 on coding, 49.0 to 38.7 on the Vibe Coding Index, at about the same output price ($2.50 vs $2.976 per million). Updated October 22, 2026; 63 benchmarks have a result for both.

Replace DeepSeek V4 Pro 0423 Replace Gemini 3.5 Flash Lite

DeepSeek V4 Pro 0423DeepSeek

Coding: 38.7
Overall: 50.0
Output $/M: $2.976

Gemini 3.5 Flash LiteGoogle

Coding: 49.0
Overall: 49.8
Output $/M: $2.50

vs boards won of 63 shared, 2 tied

DeepSeek V4 Pro 0423 or Gemini 3.5 Flash Lite: which to pick

Pick Gemini 3.5 Flash Lite for coding: it scores 49.0 on the Vibe Coding Index against 38.7 for DeepSeek V4 Pro 0423, though on 3 benchmarks against 5.

Pick DeepSeek V4 Pro 0423 for work measured by LMArena WebDev or AA Coding Index, where it leads.

Model ADeepSeek V4 Pro 0423DeepSeek38.7/1005 independent benchmarks$0.57 per solved task Model BGemini 3.5 Flash LiteGoogle49.0/1003 independent benchmarks$0.41 per solved task

632122

Shared results: 63
DeepSeek V4 Pro 0423 only: 21
Gemini 3.5 Flash Lite only: 22
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 3.5 Flash Lite
Gemini 3.5 Flash Lite scores 49.0 against 38.7.
5 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 30.4 against 22.2.
Same board, both measured
Agentic work
AA Agentic Index
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 26.3 against 14.3.
Same board, both measured
Reasoning
GPQA Diamond
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 89.4% against 83.8%.
Same board, both measured
Knowledge
MMLU Pro
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 87.3% against 85.8%.
Same board, both measured
Web design
Design Arena: websites
No call
Only DeepSeek V4 Pro 0423 has a Design Arena: websites result, 1234.
Directional only
Long documents
Listed context window
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemini 3.5 Flash Lite
DeepSeek V4 Pro 0423 $0.0028, Gemini 3.5 Flash Lite $0.0015 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Gemini 3.5 Flash Lite
DeepSeek V4 Pro 0423 $0.57, Gemini 3.5 Flash Lite $0.41.
Published attempt cost and pass rate

At a glance

Coding index: DeepSeek V4 Pro 0423 38.7 from 5 benchmarks, Gemini 3.5 Flash Lite 49.0 from 3 benchmarks.
Widest category: Agentic work, 10.2 points apart on average over 5 shared benchmarks.
Widest single gap: OTIS Mock AIME: DeepSeek V4 Pro 0423 96.7% against Gemini 3.5 Flash Lite 71.1%, 25.6 points apart on a 0 to 100 scale.
Output price: DeepSeek V4 Pro 0423 $2.976 against Gemini 3.5 Flash Lite $2.50 per million output tokens, 1.2x 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 0423	Gemini 3.5 Flash Lite	Ties
Coding	11	0	2 ties
Intelligence	2	0
Agentic work	4	1
Reasoning	5	2
Math	6	0
Knowledge	6	3
General	21	0
All shared boards	55	6	2 ties

CodingLike-for-likeSix of these boards feed the Vibe Coding Index110

Coding benchmarks for DeepSeek V4 Pro 0423 and Gemini 3.5 Flash Lite
Benchmark	DeepSeek V4 Pro 0423	Gemini 3.5 Flash Lite	Leader
LMArena WebDev	1447	1435	DeepSeek V4 Pro 0423
AA Coding Index	59.4	49.3	DeepSeek V4 Pro 0423
LiveCodeBench (Vals AI)	87.5%	79.0%	DeepSeek V4 Pro 0423
SWE-bench Verified (Vals AI)	77.4%	75.0%	DeepSeek V4 Pro 0423
Terminal-Bench 4 (Vals AI)	11.1%	Not measured	Only DeepSeek V4 Pro 0423 measured
Vibe Code Bench (Vals AI)	49.9%	37.2%	DeepSeek V4 Pro 0423
Code migration (Vals AI)	26.2%	6.2%	DeepSeek V4 Pro 0423
ProgramBench (Vals AI)	0.0%	0.0%	Tie
Terminal-Bench 2.1 (Vals AI)	50.2%	50.2%	Tie
SkillsBench (Vals AI)	51.3%	Not measured	Only DeepSeek V4 Pro 0423 measured
CyberBench (Vals AI)	Not measured	59.1%	Only Gemini 3.5 Flash Lite measured
SciCode	Not measured	41.3%	Only Gemini 3.5 Flash Lite measured
FrontierCode	17.6%	Not measured	Only DeepSeek V4 Pro 0423 measured
WeirdML	48.9%	39.0%	DeepSeek V4 Pro 0423
ALE-Bench	1006.1	765.3	DeepSeek V4 Pro 0423
LMArena Text: coding	1470	1453	DeepSeek V4 Pro 0423
LMArena Text: software and IT	1469	1451	DeepSeek V4 Pro 0423
LMArena WebDev: React	1441	1408	DeepSeek V4 Pro 0423
LMArena WebDev: HTML	1435	Not measured	Only DeepSeek V4 Pro 0423 measured
LMArena WebDev: from an image	Not measured	1448	Only Gemini 3.5 Flash Lite measured
SciCode	Not measured	6.2%	Only Gemini 3.5 Flash Lite measured
Neither model has a result on the other 18 boards here

IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20

Intelligence benchmarks for DeepSeek V4 Pro 0423 and Gemini 3.5 Flash Lite
Benchmark	DeepSeek V4 Pro 0423	Gemini 3.5 Flash Lite	Leader
AA Intelligence Index	30.4	22.2	DeepSeek V4 Pro 0423
Epoch Capabilities Index	149.1	144.9	DeepSeek V4 Pro 0423

Agentic workLike-for-likePublic boards, shown as published, kept out of the index41

Agentic work benchmarks for DeepSeek V4 Pro 0423 and Gemini 3.5 Flash Lite
Benchmark	DeepSeek V4 Pro 0423	Gemini 3.5 Flash Lite	Leader
AA Agentic Index	26.3	14.3	DeepSeek V4 Pro 0423
Design Arena: agents building web apps	1000	Not measured	Only DeepSeek V4 Pro 0423 measured
Design Arena: agents building full-stack apps	948	Not measured	Only DeepSeek V4 Pro 0423 measured
Design Arena: agents building Godot games	1059	Not measured	Only DeepSeek V4 Pro 0423 measured
Finance Agent (Vals AI)	44.1%	47.4%	Gemini 3.5 Flash Lite
Tax Agent Bench (Vals AI)	25.2%	7.2%	DeepSeek V4 Pro 0423
Legal Research Bench (Vals AI)	23.1%	13.9%	DeepSeek V4 Pro 0423
Excel Modeling Benchmark (Vals AI)	51.6%	43.2%	DeepSeek V4 Pro 0423
APEX-Agents	Not measured	29.3%	Only Gemini 3.5 Flash Lite measured
Vending-Bench 2	$3,285	Not measured	Only DeepSeek V4 Pro 0423 measured
Neither model has a result on the other 31 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index52

Reasoning benchmarks for DeepSeek V4 Pro 0423 and Gemini 3.5 Flash Lite
Benchmark	DeepSeek V4 Pro 0423	Gemini 3.5 Flash Lite	Leader
GPQA Diamond	89.4%	83.8%	DeepSeek V4 Pro 0423
MMMU Pro (Vals AI)	Not measured	83.6%	Only Gemini 3.5 Flash Lite measured
GPQA Diamond (Artificial Analysis run)	Not measured	83.8%	Only Gemini 3.5 Flash Lite measured
Humanity's Last Exam (Artificial Analysis run)	Not measured	18.8%	Only Gemini 3.5 Flash Lite measured
GPQA Diamond (Epoch AI run)	90.9%	83.3%	DeepSeek V4 Pro 0423
ARC-AGI	Not measured	53.5%	Only Gemini 3.5 Flash Lite measured
ARC-AGI-2	Not measured	10.3%	Only Gemini 3.5 Flash Lite measured
Chess puzzles	19.0%	21.0%	Gemini 3.5 Flash Lite
Mystery game puzzles	17.0%	19.0%	Gemini 3.5 Flash Lite
LMArena Text: hard prompts	1461	1441	DeepSeek V4 Pro 0423
LMArena Text: expert	1463	1434	DeepSeek V4 Pro 0423
LMArena Text: hard prompts in English	1466	1440	DeepSeek V4 Pro 0423
LMArena Vision: diagrams	Not measured	1284	Only Gemini 3.5 Flash Lite measured
LMArena Vision: homework	Not measured	1291	Only Gemini 3.5 Flash Lite measured
Kagi LLM Benchmark	53.5%	Not measured	Only DeepSeek V4 Pro 0423 measured
Neither model has a result on the other 7 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index60

Math benchmarks for DeepSeek V4 Pro 0423 and Gemini 3.5 Flash Lite
Benchmark	DeepSeek V4 Pro 0423	Gemini 3.5 Flash Lite	Leader
OTIS Mock AIME	96.7%	71.1%	DeepSeek V4 Pro 0423
FrontierMath, tiers 1 to 3	45.3%	26.0%	DeepSeek V4 Pro 0423
FrontierMath, tier 4	2.4%	0.0%	DeepSeek V4 Pro 0423
ProofBench	16.0%	13.0%	DeepSeek V4 Pro 0423
LMArena Text: math	1443	1434	DeepSeek V4 Pro 0423
LMArena Text: mathematical work	1455	1433	DeepSeek V4 Pro 0423
Neither model has a result on the other 7 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index63

Knowledge benchmarks for DeepSeek V4 Pro 0423 and Gemini 3.5 Flash Lite
Benchmark	DeepSeek V4 Pro 0423	Gemini 3.5 Flash Lite	Leader
MMLU Pro	87.3%	85.8%	DeepSeek V4 Pro 0423
LegalBench (Vals AI)	80.3%	84.1%	Gemini 3.5 Flash Lite
CaseLaw (Vals AI)	59.4%	Not measured	Only DeepSeek V4 Pro 0423 measured
CorpFin (Vals AI)	61.4%	60.7%	DeepSeek V4 Pro 0423
TaxEval (Vals AI)	72.1%	72.6%	Gemini 3.5 Flash Lite
MortgageTax (Vals AI)	Not measured	68.7%	Only Gemini 3.5 Flash Lite measured
MedCode (Vals AI)	40.5%	43.5%	Gemini 3.5 Flash Lite
SimpleQA Verified	47.0%	Not measured	Only DeepSeek V4 Pro 0423 measured
LMArena Text: science	1470	1451	DeepSeek V4 Pro 0423
LMArena Text: medicine and healthcare	1466	1438	DeepSeek V4 Pro 0423
LMArena Text: legal and government	1464	1431	DeepSeek V4 Pro 0423
LMArena Text: business and finance	1443	1423	DeepSeek V4 Pro 0423
FACTS Benchmark Suite	Not measured	53.5%	Only Gemini 3.5 Flash Lite measured
Neither model has a result on the other 5 boards here

Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared

Web design benchmarks for DeepSeek V4 Pro 0423 and Gemini 3.5 Flash Lite
Benchmark	DeepSeek V4 Pro 0423	Gemini 3.5 Flash Lite	Leader
Design Arena: websites	1234	Not measured	Only DeepSeek V4 Pro 0423 measured
Design Arena: UI components	1219	Not measured	Only DeepSeek V4 Pro 0423 measured
Design Arena: game dev	1228	Not measured	Only DeepSeek V4 Pro 0423 measured
Design Arena: data viz	1203	Not measured	Only DeepSeek V4 Pro 0423 measured
Design Arena: 3D	1253	Not measured	Only DeepSeek V4 Pro 0423 measured
Design Arena: all code categories	1240	Not measured	Only DeepSeek V4 Pro 0423 measured
Design Arena: SVG	1139	Not measured	Only DeepSeek V4 Pro 0423 measured
Design Arena: ASCII art	1149	Not measured	Only DeepSeek V4 Pro 0423 measured
Neither model has a result on the other 1 board here

GeneralLike-for-likePublic boards, shown as published, kept out of the index210

General benchmarks for DeepSeek V4 Pro 0423 and Gemini 3.5 Flash Lite
Benchmark	DeepSeek V4 Pro 0423	Gemini 3.5 Flash Lite	Leader
Public Benefits Bench (Vals AI)	62.9%	51.8%	DeepSeek V4 Pro 0423
SAGE (Vals AI)	Not measured	47.3%	Only Gemini 3.5 Flash Lite measured
MedScribe (Vals AI)	75.1%	70.9%	DeepSeek V4 Pro 0423
Long context reasoning (AA-LCR)	Not measured	76.0%	Only Gemini 3.5 Flash Lite measured
DTBench	90.7%	83.5%	DeepSeek V4 Pro 0423
LMCA	41.2%	37.6%	DeepSeek V4 Pro 0423
Surface Evolver Bench	40.0%	Not measured	Only DeepSeek V4 Pro 0423 measured
CL-bench Life	13.5%	Not measured	Only DeepSeek V4 Pro 0423 measured
LMArena Text	1451	1435	DeepSeek V4 Pro 0423
LMArena Text: instruction following	1445	1419	DeepSeek V4 Pro 0423
LMArena Text: creative writing	1446	1421	DeepSeek V4 Pro 0423
LMArena Vision	Not measured	1269	Only Gemini 3.5 Flash Lite measured
LMArena Text: English	1459	1438	DeepSeek V4 Pro 0423
LMArena Text: non-English	1439	1427	DeepSeek V4 Pro 0423
LMArena Text: Chinese	1481	1468	DeepSeek V4 Pro 0423
LMArena Text: German	1458	1454	DeepSeek V4 Pro 0423
LMArena Text: Japanese	1446	1426	DeepSeek V4 Pro 0423
LMArena Text: Korean	1409	1407	DeepSeek V4 Pro 0423
LMArena Text: French	1473	1451	DeepSeek V4 Pro 0423
LMArena Text: Spanish	1457	1441	DeepSeek V4 Pro 0423
LMArena Text: Russian	1453	1443	DeepSeek V4 Pro 0423
LMArena Text: Polish	1461	1437	DeepSeek V4 Pro 0423
LMArena Text: multi-turn	1468	1445	DeepSeek V4 Pro 0423
LMArena Text: longer queries	1458	1435	DeepSeek V4 Pro 0423
LMArena Text: writing and language	1447	1428	DeepSeek V4 Pro 0423
LMArena Text: entertainment and media	1431	1414	DeepSeek V4 Pro 0423
LMArena Vision: English	Not measured	1269	Only Gemini 3.5 Flash Lite measured
LMArena Vision: Chinese	Not measured	1311	Only Gemini 3.5 Flash Lite measured
LMArena Vision: reading text	Not measured	1280	Only Gemini 3.5 Flash Lite measured
LMArena Vision: humor	Not measured	1287	Only Gemini 3.5 Flash Lite measured
LMArena Vision: creative writing	Not measured	1260	Only Gemini 3.5 Flash Lite 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 Pro 0423 and Gemini 3.5 Flash Lite
Benchmark	DeepSeek V4 Pro 0423	Gemini 3.5 Flash Lite	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 0423$0.0028Fits in one request

Gemini 3.5 Flash Lite$0.0015Fits in one request

Gemini 3.5 Flash Lite costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Pro 0423$0.077Fits in one request

Gemini 3.5 Flash Lite$0.022Fits in one request

Gemini 3.5 Flash Lite costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Pro 0423$0.301Fits in one request

Gemini 3.5 Flash Lite$0.085Fits in one request

Gemini 3.5 Flash Lite 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 0423 and Gemini 3.5 Flash Lite
Specification	DeepSeek V4 Pro 0423	Google Gemini 3.5 Flash Lite
Maker	DeepSeek	Google
Weights	Open weights	No public weights listed
Released	April 24, 2026	July 21, 2026
Context window	1.05M	1.05M
Input $/Mtok	$1.358	$0.30
Output $/Mtok	$2.976	$2.50
Coding index	38.7	49.0
Independent measurements behind it	5	3
Cost per solved task	$0.57	$0.41
Source	OpenRouter listing	OpenRouter listing

Questions

Is DeepSeek V4 Pro 0423 better than Gemini 3.5 Flash Lite for coding?

Pick Gemini 3.5 Flash Lite for coding: it scores 49.0 on the Vibe Coding Index against 38.7 for DeepSeek V4 Pro 0423, though on 3 benchmarks against 5. Pick DeepSeek V4 Pro 0423 for work measured by LMArena WebDev or AA Coding Index, where it leads.

Which is cheaper, DeepSeek V4 Pro 0423 or Gemini 3.5 Flash Lite?

DeepSeek V4 Pro 0423 costs $1.358 per million input tokens and $2.976 per million output tokens; Gemini 3.5 Flash Lite costs $0.30 and $2.50. Per solved SWE-bench Verified task, DeepSeek V4 Pro 0423 costs $0.57 and Gemini 3.5 Flash Lite $0.41.

Which has the bigger context window, DeepSeek V4 Pro 0423 or Gemini 3.5 Flash Lite?

Neither: both take 1.05M tokens.

How many benchmarks is the DeepSeek V4 Pro 0423 vs Gemini 3.5 Flash Lite comparison based on?

63 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Pro 0423's coding index rests on 5 independent benchmarks and Gemini 3.5 Flash Lite's on 3. 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-pro-vs-gemini-3-5-flash-lite" title="DeepSeek V4 Pro 0423 vs Gemini 3.5 Flash Lite, 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-pro-vs-gemini-3-5-flash-lite">LLMBenchmark.ai, DeepSeek V4 Pro 0423 vs Gemini 3.5 Flash Lite</a></p>

DeepSeek V4 Pro 0423 or Gemini 3.5 Flash Lite: 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