DeepSeek V4 Pro 0423 vs Gemini 3.7 Flash

Gemini 3.7 Flash leads DeepSeek V4 Pro 0423 on coding, 58.0 to 38.7 on the Vibe Coding Index, and DeepSeek V4 Pro 0423 costs 1.3x less per output token ($2.976 vs $3.75 per million). Updated October 22, 2026; 75 benchmarks have a result for both.

Replace DeepSeek V4 Pro 0423 Replace Gemini 3.7 Flash

DeepSeek V4 Pro 0423DeepSeek

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

Gemini 3.7 FlashGoogle

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

vs boards won of 75 shared, 1 tied

DeepSeek V4 Pro 0423 or Gemini 3.7 Flash: which to pick

Pick Gemini 3.7 Flash for coding: it scores 58.0 on the Vibe Coding Index against 38.7 for DeepSeek V4 Pro 0423.

Pick DeepSeek V4 Pro 0423 for output tokens 1.3x cheaper ($2.976 vs $3.75 per million) and a lower cost per solved task ($0.57 against $1.78).

Model ADeepSeek V4 Pro 0423DeepSeek38.7/1005 independent benchmarks$0.57 per solved task Model BGemini 3.7 FlashGoogle58.0/1006 independent benchmarks$1.78 per solved task

75939

Shared results: 75
DeepSeek V4 Pro 0423 only: 9
Gemini 3.7 Flash only: 39
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 3.7 Flash
Gemini 3.7 Flash scores 58.0 against 38.7.
5 and 6 benchmarks behind the scores
Intelligence
AA Intelligence Index
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 39.6 against 30.4.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only DeepSeek V4 Pro 0423 has a AA Agentic Index result, 26.3.
Directional only
Reasoning
GPQA Diamond
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 93.9% against 89.4%.
Same board, both measured
Knowledge
MMLU Pro
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 90.1% against 87.3%.
Same board, both measured
Web design
Design Arena: websites
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 1303 against 1234.
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
Gemini 3.7 Flash
DeepSeek V4 Pro 0423 $0.0028, Gemini 3.7 Flash $0.0026 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 $0.57, Gemini 3.7 Flash $1.78.
Published attempt cost and pass rate

At a glance

Coding index: DeepSeek V4 Pro 0423 38.7 from 5 benchmarks, Gemini 3.7 Flash 58.0 from 6 benchmarks.
Widest category: Math, 20.4 points apart on average over 6 shared benchmarks.
Widest single gap: Chess puzzles: DeepSeek V4 Pro 0423 19.0% against Gemini 3.7 Flash 66.0%, 47.0 points apart on a 0 to 100 scale.
Output price: DeepSeek V4 Pro 0423 $2.976 against Gemini 3.7 Flash $3.75 per million output tokens, 1.3x 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 Pro 0423	Gemini 3.7 Flash	Ties
Coding	1	14	1 tie
Intelligence	0	2
Agentic work	1	6
Reasoning	0	7
Math	0	6
Knowledge	0	9
Web design	0	8
General	0	20
All shared boards	2	72	1 tie

CodingLike-for-likeSix of these boards feed the Vibe Coding Index114

Coding benchmarks for DeepSeek V4 Pro 0423 and Gemini 3.7 Flash
Benchmark	DeepSeek V4 Pro 0423	Gemini 3.7 Flash	Leader
LiveBench coding	Not measured	80.5%	Only Gemini 3.7 Flash measured
LMArena WebDev	1447	1591	Gemini 3.7 Flash
AA Coding Index	59.4	71.5	Gemini 3.7 Flash
LiveCodeBench (Vals AI)	87.5%	88.7%	Gemini 3.7 Flash
SWE-bench Verified (Vals AI)	77.4%	80.8%	Gemini 3.7 Flash
Terminal-Bench 4 (Vals AI)	11.1%	12.1%	Gemini 3.7 Flash
Vibe Code Bench (Vals AI)	49.9%	70.4%	Gemini 3.7 Flash
Code migration (Vals AI)	26.2%	34.8%	Gemini 3.7 Flash
ProgramBench (Vals AI)	0.0%	0.0%	Tie
IOI (Vals AI)	Not measured	67.8%	Only Gemini 3.7 Flash measured
Terminal-Bench 2.1 (Vals AI)	50.2%	77.5%	Gemini 3.7 Flash
SkillsBench (Vals AI)	51.3%	65.9%	Gemini 3.7 Flash
CyberBench (Vals AI)	Not measured	43.8%	Only Gemini 3.7 Flash measured
SciCode	Not measured	59.8%	Only Gemini 3.7 Flash measured
FrontierCode	17.6%	43.6%	Gemini 3.7 Flash
FrontierSWE	Not measured	20.3%	Only Gemini 3.7 Flash measured
WeirdML	48.9%	Not measured	Only DeepSeek V4 Pro 0423 measured
ALE-Bench	1006.1	904.3	DeepSeek V4 Pro 0423
LMArena Text: coding	1470	1499	Gemini 3.7 Flash
LMArena Text: software and IT	1469	1498	Gemini 3.7 Flash
LMArena WebDev: React	1441	1587	Gemini 3.7 Flash
LMArena WebDev: HTML	1435	1593	Gemini 3.7 Flash
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 15 boards here

IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02

Intelligence benchmarks for DeepSeek V4 Pro 0423 and Gemini 3.7 Flash
Benchmark	DeepSeek V4 Pro 0423	Gemini 3.7 Flash	Leader
AA Intelligence Index	30.4	39.6	Gemini 3.7 Flash
Epoch Capabilities Index	149.1	158.0	Gemini 3.7 Flash

Agentic workLike-for-likePublic boards, shown as published, kept out of the index16

Agentic work benchmarks for DeepSeek V4 Pro 0423 and Gemini 3.7 Flash
Benchmark	DeepSeek V4 Pro 0423	Gemini 3.7 Flash	Leader
AA Agentic Index	26.3	Not measured	Only DeepSeek V4 Pro 0423 measured
Design Arena: agents building web apps	1000	1223	Gemini 3.7 Flash
Design Arena: agents building full-stack apps	948	1208	Gemini 3.7 Flash
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	1059	1264	Gemini 3.7 Flash
Design Arena: agents building games	Not measured	1235	Only Gemini 3.7 Flash measured
Design Arena: spreadsheets	Not measured	1058	Only Gemini 3.7 Flash measured
LiveBench research	Not measured	76.5%	Only Gemini 3.7 Flash measured
LiveBench browser	Not measured	56.0%	Only Gemini 3.7 Flash measured
LiveBench long agentic	Not measured	28.3%	Only Gemini 3.7 Flash measured
SREBench (Vals AI)	Not measured	4.6%	Only Gemini 3.7 Flash measured
Finance Agent (Vals AI)	44.1%	59.0%	Gemini 3.7 Flash
Tax Agent Bench (Vals AI)	25.2%	22.8%	DeepSeek V4 Pro 0423
Legal Research Bench (Vals AI)	23.1%	34.6%	Gemini 3.7 Flash
Excel Modeling Benchmark (Vals AI)	51.6%	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
Vending-Bench 2	$3,285	Not measured	Only DeepSeek V4 Pro 0423 measured
Neither model has a result on the other 21 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index07

Reasoning benchmarks for DeepSeek V4 Pro 0423 and Gemini 3.7 Flash
Benchmark	DeepSeek V4 Pro 0423	Gemini 3.7 Flash	Leader
GPQA Diamond	89.4%	93.9%	Gemini 3.7 Flash
LiveBench reasoning	Not measured	87.8%	Only Gemini 3.7 Flash measured
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)	Not measured	47.9%	Only Gemini 3.7 Flash measured
GPQA Diamond (Epoch AI run)	90.9%	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	19.0%	66.0%	Gemini 3.7 Flash
Mystery game puzzles	17.0%	37.0%	Gemini 3.7 Flash
LMArena Text: hard prompts	1461	1495	Gemini 3.7 Flash
LMArena Text: expert	1463	1512	Gemini 3.7 Flash
LMArena Text: hard prompts in English	1466	1487	Gemini 3.7 Flash
LMArena Vision: diagrams	Not measured	1329	Only Gemini 3.7 Flash measured
LMArena Vision: homework	Not measured	1320	Only Gemini 3.7 Flash measured
Kagi LLM Benchmark	53.5%	Not measured	Only DeepSeek V4 Pro 0423 measured
Neither model has a result on the other 6 boards here

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

Math benchmarks for DeepSeek V4 Pro 0423 and Gemini 3.7 Flash
Benchmark	DeepSeek V4 Pro 0423	Gemini 3.7 Flash	Leader
OTIS Mock AIME	96.7%	97.2%	Gemini 3.7 Flash
FrontierMath, tiers 1 to 3	45.3%	71.6%	Gemini 3.7 Flash
FrontierMath, tier 4	2.4%	36.6%	Gemini 3.7 Flash
ProofBench	16.0%	58.0%	Gemini 3.7 Flash
LMArena Text: math	1443	1511	Gemini 3.7 Flash
LMArena Text: mathematical work	1455	1502	Gemini 3.7 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 0423 and Gemini 3.7 Flash
Benchmark	DeepSeek V4 Pro 0423	Gemini 3.7 Flash	Leader
MMLU Pro	87.3%	90.1%	Gemini 3.7 Flash
LegalBench (Vals AI)	80.3%	87.3%	Gemini 3.7 Flash
CaseLaw (Vals AI)	59.4%	Not measured	Only DeepSeek V4 Pro 0423 measured
CorpFin (Vals AI)	61.4%	Not measured	Only DeepSeek V4 Pro 0423 measured
TaxEval (Vals AI)	72.1%	74.7%	Gemini 3.7 Flash
MortgageTax (Vals AI)	Not measured	66.7%	Only Gemini 3.7 Flash measured
MedCode (Vals AI)	40.5%	53.4%	Gemini 3.7 Flash
SimpleQA Verified	47.0%	69.2%	Gemini 3.7 Flash
LMArena Text: science	1470	1511	Gemini 3.7 Flash
LMArena Text: medicine and healthcare	1466	1494	Gemini 3.7 Flash
LMArena Text: legal and government	1464	1496	Gemini 3.7 Flash
LMArena Text: business and finance	1443	1479	Gemini 3.7 Flash
FACTS Benchmark Suite	Not measured	71.2%	Only Gemini 3.7 Flash measured
Neither model has a result on the other 5 boards here

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

Web design benchmarks for DeepSeek V4 Pro 0423 and Gemini 3.7 Flash
Benchmark	DeepSeek V4 Pro 0423	Gemini 3.7 Flash	Leader
Design Arena: websites	1234	1303	Gemini 3.7 Flash
Design Arena: UI components	1219	1296	Gemini 3.7 Flash
Design Arena: game dev	1228	1317	Gemini 3.7 Flash
Design Arena: data viz	1203	1315	Gemini 3.7 Flash
Design Arena: 3D	1253	1307	Gemini 3.7 Flash
Design Arena: all code categories	1240	1308	Gemini 3.7 Flash
Design Arena: SVG	1139	1283	Gemini 3.7 Flash
Design Arena: ASCII art	1149	1276	Gemini 3.7 Flash
LiveBench design	Not measured	73.2%	Only Gemini 3.7 Flash measured

GeneralLike-for-likePublic boards, shown as published, kept out of the index020

General benchmarks for DeepSeek V4 Pro 0423 and Gemini 3.7 Flash
Benchmark	DeepSeek V4 Pro 0423	Gemini 3.7 Flash	Leader
LiveBench data analysis	Not measured	68.0%	Only Gemini 3.7 Flash measured
Public Benefits Bench (Vals AI)	62.9%	Not measured	Only DeepSeek V4 Pro 0423 measured
SAGE (Vals AI)	Not measured	49.2%	Only Gemini 3.7 Flash measured
MedScribe (Vals AI)	75.1%	83.9%	Gemini 3.7 Flash
Long context reasoning (AA-LCR)	Not measured	83.0%	Only Gemini 3.7 Flash measured
DTBench	90.7%	96.8%	Gemini 3.7 Flash
LMCA	41.2%	50.4%	Gemini 3.7 Flash
Surface Evolver Bench	40.0%	Not measured	Only DeepSeek V4 Pro 0423 measured
Furniture assembly	Not measured	26.7%	Only Gemini 3.7 Flash measured
CL-bench Life	13.5%	Not measured	Only DeepSeek V4 Pro 0423 measured
LMArena Text	1451	1489	Gemini 3.7 Flash
LMArena Text: instruction following	1445	1484	Gemini 3.7 Flash
LMArena Text: creative writing	1446	1488	Gemini 3.7 Flash
LMArena Vision	Not measured	1314	Only Gemini 3.7 Flash measured
LMArena Text: English	1459	1483	Gemini 3.7 Flash
LMArena Text: non-English	1439	1487	Gemini 3.7 Flash
LMArena Text: Chinese	1481	1552	Gemini 3.7 Flash
LMArena Text: German	1458	1506	Gemini 3.7 Flash
LMArena Text: Japanese	1446	1511	Gemini 3.7 Flash
LMArena Text: Korean	1409	1481	Gemini 3.7 Flash
LMArena Text: French	1473	1500	Gemini 3.7 Flash
LMArena Text: Spanish	1457	1495	Gemini 3.7 Flash
LMArena Text: Russian	1453	1515	Gemini 3.7 Flash
LMArena Text: Polish	1461	1503	Gemini 3.7 Flash
LMArena Text: multi-turn	1468	1485	Gemini 3.7 Flash
LMArena Text: longer queries	1458	1492	Gemini 3.7 Flash
LMArena Text: writing and language	1447	1491	Gemini 3.7 Flash
LMArena Text: entertainment and media	1431	1477	Gemini 3.7 Flash
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 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 0423 and Gemini 3.7 Flash
Benchmark	DeepSeek V4 Pro 0423	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 Pro 0423$0.0028Fits in one request

Gemini 3.7 Flash$0.0026Fits in one request

Gemini 3.7 Flash costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Pro 0423$0.077Fits in one request

Gemini 3.7 Flash$0.049Fits in one request

Gemini 3.7 Flash costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Pro 0423$0.301Fits in one request

Gemini 3.7 Flash$0.188Fits in one request

Gemini 3.7 Flash 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 3.7 Flash
Specification	DeepSeek V4 Pro 0423	Google Gemini 3.7 Flash
Maker	DeepSeek	Google
Weights	Open weights	No public weights listed
Released	April 24, 2026	August 13, 2026
Context window	1.05M	1.05M
Input $/Mtok	$1.358	$0.75
Output $/Mtok	$2.976	$3.75
Coding index	38.7	58.0
Independent measurements behind it	5	6
Cost per solved task	$0.57	$1.78
Source	OpenRouter listing	OpenRouter listing

Questions

Is DeepSeek V4 Pro 0423 better than Gemini 3.7 Flash for coding?

Pick Gemini 3.7 Flash for coding: it scores 58.0 on the Vibe Coding Index against 38.7 for DeepSeek V4 Pro 0423. Pick DeepSeek V4 Pro 0423 for output tokens 1.3x cheaper ($2.976 vs $3.75 per million) and a lower cost per solved task ($0.57 against $1.78).

Which is cheaper, DeepSeek V4 Pro 0423 or Gemini 3.7 Flash?

DeepSeek V4 Pro 0423 costs $1.358 per million input tokens and $2.976 per million output tokens; Gemini 3.7 Flash costs $0.75 and $3.75. Per solved SWE-bench Verified task, DeepSeek V4 Pro 0423 costs $0.57 and Gemini 3.7 Flash $1.78.

Which has the bigger context window, DeepSeek V4 Pro 0423 or Gemini 3.7 Flash?

Neither: both take 1.05M tokens.

How many benchmarks is the DeepSeek V4 Pro 0423 vs Gemini 3.7 Flash comparison based on?

75 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 3.7 Flash's on 6. Where only one model has a result, the page says so instead of naming a winner.

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DeepSeek V4 Pro 0423 or Gemini 3.7 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