DeepSeek V3.2 vs Gemini 3.5 Flash

Updated October 21, 2026. 61 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace DeepSeek V3.2 Replace Gemini 3.5 Flash

DeepSeek V3.2DeepSeek

Coding: 54.4
Overall: 21.5
Output $/M: $0.42

Gemini 3.5 FlashGoogle

Coding: 42.8
Overall: 33.6
Output $/M: $9.00

vs boards won of 61 shared

Decision reading

Pick DeepSeek V3.2 for coding: it scores 54.4 on the Vibe Coding Index against 42.8 for Gemini 3.5 Flash.

Pick Gemini 3.5 Flash when the context window is larger, 1.05M against 164K tokens, or when what matters is LMArena WebDev or LiveCodeBench (Vals AI), where it leads.

Model ADeepSeek V3.2DeepSeek54.4/1002 independent benchmarks$0.99 per solved task Model BGemini 3.5 FlashGoogle42.8/1001 independent benchmark$1.21 per solved task

611449

Shared results: 61
DeepSeek V3.2 only: 14
Gemini 3.5 Flash only: 49
Like-for-like categories: 8 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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, four public coding benchmarks
DeepSeek V3.2
DeepSeek V3.2 scores 54.4 against 42.8.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 33.6 against 21.5.
Same board, both measured
Reasoning
GPQA Diamond
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 92.7% against 80.3%.
Same board, both measured
Math
AIME
No call
Only DeepSeek V3.2 has a AIME result, 84.6%.
Directional only
Knowledge
MMLU Pro
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 89.5% against 84.9%.
Same board, both measured
Web design
Design Arena: websites
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 1268 against 1175.
Same board, both measured
Long documents
Listed context window
Gemini 3.5 Flash
Gemini 3.5 Flash takes 1.05M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.2
DeepSeek V3.2 $0.0005, Gemini 3.5 Flash $0.0060 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
DeepSeek V3.2
DeepSeek V3.2 $0.99, Gemini 3.5 Flash $1.21.
Published attempt cost and pass rate

At a glance

Coding index: DeepSeek V3.2 54.4 from 2 benchmarks, Gemini 3.5 Flash 42.8 from 1 benchmark.
Widest category: Reasoning, 20.9 points apart on average over 8 shared benchmarks.
Widest single gap: ARC-AGI-2: DeepSeek V3.2 4.0% against Gemini 3.5 Flash 72.1%, 68.0 points apart on a 0 to 100 scale.
Output price: DeepSeek V3.2 $0.42 against Gemini 3.5 Flash $9.00 per million output tokens, 21.4x apart.
Context window: DeepSeek V3.2 takes 164K tokens, Gemini 3.5 Flash 1.05M.

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 V3.2	Bars	Gemini 3.5 Flash	Ties
Coding	0		9
Intelligence	0		2
Agentic work	0		3
Reasoning	0		8
Math	0		2
Knowledge	0		8
Web design	0		8
General	0		21
All shared boards	0		61

CodingLike-for-likeFour of these boards feed the Vibe Coding Index09

Coding benchmarks for DeepSeek V3.2 and Gemini 3.5 Flash
Benchmark	DeepSeek V3.2	Gemini 3.5 Flash	Leader
SWE-bench Verified	70.0%	Not measured	Only DeepSeek V3.2 measured
SWE-bench Multilingual	59.0%	Not measured	Only DeepSeek V3.2 measured
LMArena WebDev	1362	1499	Gemini 3.5 Flash
AA Coding Index	44.2	Not measured	Only DeepSeek V3.2 measured
LiveCodeBench (Vals AI)	80.7%	87.6%	Gemini 3.5 Flash
SWE-bench Verified (Vals AI)	67.6%	78.8%	Gemini 3.5 Flash
Terminal-Bench 4 (Vals AI)	Not measured	6.1%	Only Gemini 3.5 Flash measured
Vibe Code Bench (Vals AI)	5.1%	48.7%	Gemini 3.5 Flash
Code migration (Vals AI)	Not measured	26.8%	Only Gemini 3.5 Flash measured
ProgramBench (Vals AI)	Not measured	0.0%	Only Gemini 3.5 Flash measured
Terminal-Bench 2.1 (Vals AI)	Not measured	74.2%	Only Gemini 3.5 Flash measured
SkillsBench (Vals AI)	Not measured	52.7%	Only Gemini 3.5 Flash measured
LiveCodeBench (Artificial Analysis run)	86.2%	Not measured	Only DeepSeek V3.2 measured
SciCode	Not measured	53.9%	Only Gemini 3.5 Flash measured
Terminal-Bench Hard (Artificial Analysis run)	35.6%	46.2%	Gemini 3.5 Flash
Terminal-Bench	39.6%	Not measured	Only DeepSeek V3.2 measured
WeirdML	Not measured	62.6%	Only Gemini 3.5 Flash measured
ALE-Bench	Not measured	911.0	Only Gemini 3.5 Flash measured
LMArena Text: coding	1453	1493	Gemini 3.5 Flash
LMArena Text: software and IT	1449	1494	Gemini 3.5 Flash
LMArena WebDev: React	1350	1495	Gemini 3.5 Flash
LMArena WebDev: HTML	1371	1522	Gemini 3.5 Flash
LMArena WebDev: from an image	Not measured	1504	Only Gemini 3.5 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 V3.2 and Gemini 3.5 Flash
Benchmark	DeepSeek V3.2	Gemini 3.5 Flash	Leader
AA Intelligence Index	21.5	33.6	Gemini 3.5 Flash
Epoch Capabilities Index	146.1	154.9	Gemini 3.5 Flash

Agentic workLike-for-likePublic boards, shown as published, kept out of the index03

Agentic work benchmarks for DeepSeek V3.2 and Gemini 3.5 Flash
Benchmark	DeepSeek V3.2	Gemini 3.5 Flash	Leader
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	1175	Only Gemini 3.5 Flash measured
Design Arena: agents building Android apps	Not measured	1190	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	1178	Only Gemini 3.5 Flash measured
Finance Agent (Vals AI)	Not measured	57.9%	Only Gemini 3.5 Flash measured
Tax Agent Bench (Vals AI)	Not measured	21.6%	Only Gemini 3.5 Flash measured
Legal Research Bench (Vals AI)	Not measured	30.8%	Only Gemini 3.5 Flash measured
Excel Modeling Benchmark (Vals AI)	Not measured	63.5%	Only Gemini 3.5 Flash measured
Terminal-Bench-Science (Vals AI)	Not measured	5.7%	Only Gemini 3.5 Flash measured
τ²-Bench Telecom	90.6%	95.6%	Gemini 3.5 Flash
APEX-Agents	21.3%	27.5%	Gemini 3.5 Flash
Vending-Bench 2	$1,034	$5,396	Gemini 3.5 Flash
Neither model has a result on the other 23 boards here

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

Reasoning benchmarks for DeepSeek V3.2 and Gemini 3.5 Flash
Benchmark	DeepSeek V3.2	Gemini 3.5 Flash	Leader
GPQA Diamond	80.3%	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)	84.0%	92.2%	Gemini 3.5 Flash
Humanity's Last Exam (Artificial Analysis run)	24.6%	42.7%	Gemini 3.5 Flash
GPQA Diamond (Epoch AI run)	Not measured	92.8%	Only Gemini 3.5 Flash measured
ARC-AGI	57.0%	92.5%	Gemini 3.5 Flash
ARC-AGI-2	4.0%	72.1%	Gemini 3.5 Flash
SimpleBench	Not measured	76.7%	Only Gemini 3.5 Flash measured
Chess puzzles	Not measured	45.0%	Only Gemini 3.5 Flash measured
Mystery game puzzles	Not measured	32.0%	Only Gemini 3.5 Flash measured
EBR-bench	Not measured	4.8%	Only Gemini 3.5 Flash measured
LMArena Text: hard prompts	1434	1488	Gemini 3.5 Flash
LMArena Text: expert	1436	1495	Gemini 3.5 Flash
LMArena Text: hard prompts in English	1446	1483	Gemini 3.5 Flash
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 5 boards here

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

Math benchmarks for DeepSeek V3.2 and Gemini 3.5 Flash
Benchmark	DeepSeek V3.2	Gemini 3.5 Flash	Leader
AIME	84.6%	Not measured	Only DeepSeek V3.2 measured
MGSM (Vals AI)	86.0%	Not measured	Only DeepSeek V3.2 measured
AA Math Index	92.0	Not measured	Only DeepSeek V3.2 measured
AIME 2025 (Artificial Analysis run)	92.0%	Not measured	Only DeepSeek V3.2 measured
OTIS Mock AIME	Not measured	95.6%	Only Gemini 3.5 Flash measured
FrontierMath, tiers 1 to 3	Not measured	62.8%	Only Gemini 3.5 Flash measured
FrontierMath, tier 4	Not measured	26.8%	Only Gemini 3.5 Flash measured
ProofBench	Not measured	31.0%	Only Gemini 3.5 Flash measured
LMArena Text: math	1435	1504	Gemini 3.5 Flash
LMArena Text: mathematical work	1440	1495	Gemini 3.5 Flash
Neither model has a result on the other 3 boards here

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

Knowledge benchmarks for DeepSeek V3.2 and Gemini 3.5 Flash
Benchmark	DeepSeek V3.2	Gemini 3.5 Flash	Leader
MMLU Pro	84.9%	89.5%	Gemini 3.5 Flash
LegalBench (Vals AI)	76.1%	83.6%	Gemini 3.5 Flash
CaseLaw (Vals AI)	55.4%	Not measured	Only DeepSeek V3.2 measured
CorpFin (Vals AI)	51.0%	64.7%	Gemini 3.5 Flash
TaxEval (Vals AI)	68.2%	74.4%	Gemini 3.5 Flash
MortgageTax (Vals AI)	Not measured	68.1%	Only Gemini 3.5 Flash measured
MedQA (Vals AI)	93.9%	Not measured	Only DeepSeek V3.2 measured
MedCode (Vals AI)	Not measured	55.8%	Only Gemini 3.5 Flash measured
MMLU Pro (Artificial Analysis run)	86.2%	Not measured	Only DeepSeek V3.2 measured
SimpleQA Verified	Not measured	66.2%	Only Gemini 3.5 Flash measured
LMArena Text: science	1446	1495	Gemini 3.5 Flash
LMArena Text: medicine and healthcare	1440	1483	Gemini 3.5 Flash
LMArena Text: legal and government	1425	1488	Gemini 3.5 Flash
LMArena Text: business and finance	1410	1467	Gemini 3.5 Flash
Neither model has a result on the other 3 boards here

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

Web design benchmarks for DeepSeek V3.2 and Gemini 3.5 Flash
Benchmark	DeepSeek V3.2	Gemini 3.5 Flash	Leader
Design Arena: websites	1175	1268	Gemini 3.5 Flash
Design Arena: UI components	1149	1272	Gemini 3.5 Flash
Design Arena: game dev	1132	1263	Gemini 3.5 Flash
Design Arena: data viz	1158	1236	Gemini 3.5 Flash
Design Arena: 3D	1133	1244	Gemini 3.5 Flash
Design Arena: all code categories	1164	1266	Gemini 3.5 Flash
Design Arena: SVG	1032	1254	Gemini 3.5 Flash
Design Arena: ASCII art	1082	1253	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 index021

General benchmarks for DeepSeek V3.2 and Gemini 3.5 Flash
Benchmark	DeepSeek V3.2	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)	Not measured	76.6%	Only Gemini 3.5 Flash measured
Vals Multimodal Index	Not measured	62.9%	Only Gemini 3.5 Flash measured
IFBench	60.7%	76.3%	Gemini 3.5 Flash
Long context reasoning (AA-LCR)	73.3%	74.3%	Gemini 3.5 Flash
DTBench	85.6%	94.7%	Gemini 3.5 Flash
LMCA	28.8%	47.1%	Gemini 3.5 Flash
CL-bench	12.4%	Not measured	Only DeepSeek V3.2 measured
Surface Evolver Bench	Not measured	58.1%	Only Gemini 3.5 Flash measured
CL-bench Life	7.4%	Not measured	Only DeepSeek V3.2 measured
LMArena Text	1425	1482	Gemini 3.5 Flash
LMArena Text: instruction following	1413	1467	Gemini 3.5 Flash
LMArena Text: creative writing	1400	1470	Gemini 3.5 Flash
LMArena Vision	Not measured	1308	Only Gemini 3.5 Flash measured
LMArena Text: English	1439	1481	Gemini 3.5 Flash
LMArena Text: non-English	1409	1475	Gemini 3.5 Flash
LMArena Text: Chinese	1455	1527	Gemini 3.5 Flash
LMArena Text: German	1413	1494	Gemini 3.5 Flash
LMArena Text: Japanese	1374	1486	Gemini 3.5 Flash
LMArena Text: Korean	1370	1448	Gemini 3.5 Flash
LMArena Text: French	1429	1490	Gemini 3.5 Flash
LMArena Text: Spanish	1424	1480	Gemini 3.5 Flash
LMArena Text: Russian	1424	1493	Gemini 3.5 Flash
LMArena Text: Polish	1419	1516	Gemini 3.5 Flash
LMArena Text: multi-turn	1427	1481	Gemini 3.5 Flash
LMArena Text: longer queries	1428	1483	Gemini 3.5 Flash
LMArena Text: writing and language	1407	1478	Gemini 3.5 Flash
LMArena Text: entertainment and media	1395	1459	Gemini 3.5 Flash
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 11 boards here

Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared

Image generation benchmarks for DeepSeek V3.2 and Gemini 3.5 Flash
Benchmark	DeepSeek V3.2	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 V3.2$0.0005Fits in one request

Gemini 3.5 Flash$0.0060Fits in one request

DeepSeek V3.2 costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2$0.015Fits in one request

Gemini 3.5 Flash$0.102Fits in one request

DeepSeek V3.2 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2$0.060Exceeds the context window

Gemini 3.5 Flash$0.390Fits in one request

DeepSeek V3.2 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 V3.2 and Gemini 3.5 Flash
Specification	DeepSeek V3.2	Google Gemini 3.5 Flash
Maker	DeepSeek	Google
Weights	Open weights	No public weights listed
Released	December 1, 2025	May 19, 2026
Context window	164K	1.05M
Input $/Mtok	$0.28	$1.50
Output $/Mtok	$0.42	$9.00
Coding index	54.4	42.8
Independent measurements behind it	2	1
Cost per solved task	$0.99	$1.21
Source	OpenRouter listing	OpenRouter listing

Questions

Is DeepSeek V3.2 better than Gemini 3.5 Flash for coding?

Pick DeepSeek V3.2 for coding: it scores 54.4 on the Vibe Coding Index against 42.8 for Gemini 3.5 Flash. Pick Gemini 3.5 Flash when the context window is larger, 1.05M against 164K tokens, or when what matters is LMArena WebDev or LiveCodeBench (Vals AI), where it leads.

Which is cheaper, DeepSeek V3.2 or Gemini 3.5 Flash?

DeepSeek V3.2 costs $0.28 per million input tokens and $0.42 per million output tokens; Gemini 3.5 Flash costs $1.50 and $9.00. Per solved SWE-bench Verified task, DeepSeek V3.2 costs $0.99 and Gemini 3.5 Flash $1.21.

Which has the bigger context window, DeepSeek V3.2 or Gemini 3.5 Flash?

Gemini 3.5 Flash, with 1.05M tokens against 164K.

How many benchmarks is the DeepSeek V3.2 vs Gemini 3.5 Flash comparison based on?

61 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2's coding index rests on 2 independent benchmarks and Gemini 3.5 Flash's on 1. Where only one model has a result, the page says so instead of naming a winner.

Decision reading

Which one for your work

Coding

Intelligence

Reasoning

Math

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

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