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DeepSeek V3.2 Exp vs Gemini 2.5 Flash

DeepSeek V3.2 Exp leads Gemini 2.5 Flash on the Artificial Analysis Intelligence Index, 16.6 to 15.5, and DeepSeek V3.2 Exp costs 6.1x less per output token ($0.41 vs $2.50 per million). Updated October 22, 2026; 51 benchmarks have a result for both.

Replace DeepSeek V3.2 Exp Replace Gemini 2.5 Flash
DeepSeek V3.2 ExpDeepSeek
Coding
No data
Overall
No data
Output $/M
$0.41
42
Gemini 2.5 FlashGoogle
Coding
No data
Overall
34.3
Output $/M
$2.50
9
vs boards won of 51 shared

DeepSeek V3.2 Exp or Gemini 2.5 Flash: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick DeepSeek V3.2 Exp for output tokens 6.1x cheaper ($0.41 vs $2.50 per million) and work measured by LMArena Text: expert or Kagi LLM Benchmark, where it leads.

Model ADeepSeek V3.2 ExpDeepSeekNot benchmarked1 independent benchmarkNo cost per solved taskModel BGemini 2.5 FlashGoogleNot benchmarkedNo coding benchmark$0.466 per solved task
Shared results
51
DeepSeek V3.2 Exp only
8
Gemini 2.5 Flash only
22
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.

At a glance

Coding index
DeepSeek V3.2 Exp not benchmarked, Gemini 2.5 Flash not benchmarked.
Widest category
Web design, 13.4 points apart on average over 7 shared benchmarks.
Widest single gap
Design Arena: UI components: DeepSeek V3.2 Exp 1164 against Gemini 2.5 Flash 1002, 20.2 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 Exp $0.41 against Gemini 2.5 Flash $2.50 per million output tokens, 6.1x apart.
Context window
DeepSeek V3.2 Exp takes 164K tokens, Gemini 2.5 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
CategoryDeepSeek V3.2 ExpBarsGemini 2.5 FlashTies
Coding40
Intelligence20
Agentic work20
Reasoning42
Math40
Knowledge41
Web design70
General156
All shared boards429
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
Coding benchmarks for DeepSeek V3.2 Exp and Gemini 2.5 Flash
BenchmarkDeepSeek V3.2 ExpGemini 2.5 FlashLeader
Aider polyglot74.2%Not measuredOnly DeepSeek V3.2 Exp measured
SWE-bench VerifiedNot measured28.7%Only Gemini 2.5 Flash measured
LMArena WebDev1272Not measuredOnly DeepSeek V3.2 Exp measured
LiveCodeBench (Artificial Analysis run)78.9%69.5%DeepSeek V3.2 Exp
Terminal-Bench Hard (Artificial Analysis run)31.1%13.6%DeepSeek V3.2 Exp
Terminal-BenchNot measured17.1%Only Gemini 2.5 Flash measured
WeirdML39.5%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: coding14381423DeepSeek V3.2 Exp
LMArena Text: software and IT14381425DeepSeek V3.2 Exp
LMArena WebDev: HTML1281Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 29 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for DeepSeek V3.2 Exp and Gemini 2.5 Flash
BenchmarkDeepSeek V3.2 ExpGemini 2.5 FlashLeader
AA Intelligence Index16.615.5DeepSeek V3.2 Exp
Epoch Capabilities Index144.9142.9DeepSeek V3.2 Exp
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
Agentic work benchmarks for DeepSeek V3.2 Exp and Gemini 2.5 Flash
BenchmarkDeepSeek V3.2 ExpGemini 2.5 FlashLeader
τ²-Bench Telecom33.9%31.6%DeepSeek V3.2 Exp
BALROGNot measured33.5%Only Gemini 2.5 Flash measured
TheAgentCompany42.9%Not measuredOnly DeepSeek V3.2 Exp measured
Vending-Bench 2Not measured$549Only Gemini 2.5 Flash measured
Berkeley Function Calling Leaderboard (BFCL)56.7%56.2%DeepSeek V3.2 Exp
BrowseComp-PlusNot measured39.9%Only Gemini 2.5 Flash measured
Neither model has a result on the other 35 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index42
Reasoning benchmarks for DeepSeek V3.2 Exp and Gemini 2.5 Flash
BenchmarkDeepSeek V3.2 ExpGemini 2.5 FlashLeader
GPQA Diamond (Artificial Analysis run)79.7%79.0%DeepSeek V3.2 Exp
Humanity's Last Exam (Artificial Analysis run)14.9%12.1%DeepSeek V3.2 Exp
ARC-AGINot measured33.3%Only Gemini 2.5 Flash measured
ARC-AGI-2Not measured2.5%Only Gemini 2.5 Flash measured
SimpleBenchNot measured41.2%Only Gemini 2.5 Flash measured
LMArena Text: hard prompts14291422DeepSeek V3.2 Exp
LMArena Text: expert14221426Gemini 2.5 Flash
LMArena Text: hard prompts in English14441420DeepSeek V3.2 Exp
LMArena Vision: diagramsNot measured1245Only Gemini 2.5 Flash measured
LMArena Vision: homeworkNot measured1244Only Gemini 2.5 Flash measured
Kagi LLM Benchmark52.2%56.8%Gemini 2.5 Flash
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for DeepSeek V3.2 Exp and Gemini 2.5 Flash
BenchmarkDeepSeek V3.2 ExpGemini 2.5 FlashLeader
AA Math Index87.773.3DeepSeek V3.2 Exp
AIME 2025 (Artificial Analysis run)87.7%73.3%DeepSeek V3.2 Exp
LMArena Text: math14221411DeepSeek V3.2 Exp
LMArena Text: mathematical work14331422DeepSeek V3.2 Exp
Neither model has a result on the other 9 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index41
Knowledge benchmarks for DeepSeek V3.2 Exp and Gemini 2.5 Flash
BenchmarkDeepSeek V3.2 ExpGemini 2.5 FlashLeader
MedCode (Vals AI)Not measured40.4%Only Gemini 2.5 Flash measured
MMLU Pro (Artificial Analysis run)85.0%83.2%DeepSeek V3.2 Exp
LMArena Text: science14431433DeepSeek V3.2 Exp
LMArena Text: medicine and healthcare14681428DeepSeek V3.2 Exp
LMArena Text: legal and government14191430Gemini 2.5 Flash
LMArena Text: business and finance14121404DeepSeek V3.2 Exp
FACTS Benchmark SuiteNot measured47.2%Only Gemini 2.5 Flash measured
Neither model has a result on the other 11 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index70
Web design benchmarks for DeepSeek V3.2 Exp and Gemini 2.5 Flash
BenchmarkDeepSeek V3.2 ExpGemini 2.5 FlashLeader
Design Arena: websites11781074DeepSeek V3.2 Exp
Design Arena: UI components11641002DeepSeek V3.2 Exp
Design Arena: game dev11441004DeepSeek V3.2 Exp
Design Arena: data viz11521060DeepSeek V3.2 Exp
Design Arena: 3D11481020DeepSeek V3.2 Exp
Design Arena: all code categories11701059DeepSeek V3.2 Exp
Design Arena: SVG10311020DeepSeek V3.2 Exp
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index156
General benchmarks for DeepSeek V3.2 Exp and Gemini 2.5 Flash
BenchmarkDeepSeek V3.2 ExpGemini 2.5 FlashLeader
SAGE (Vals AI)Not measured44.8%Only Gemini 2.5 Flash measured
MedScribe (Vals AI)Not measured83.0%Only Gemini 2.5 Flash measured
IFBench54.1%50.3%DeepSeek V3.2 Exp
Long context reasoning (AA-LCR)72.3%65.3%DeepSeek V3.2 Exp
DTBench87.7%76.5%DeepSeek V3.2 Exp
LMCA29.1%27.5%DeepSeek V3.2 Exp
Fiction.LiveBench83.3%Not measuredOnly DeepSeek V3.2 Exp measured
CL-bench13.2%Not measuredOnly DeepSeek V3.2 Exp measured
CL-bench Life9.5%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text14251417DeepSeek V3.2 Exp
LMArena Text: instruction following14011405Gemini 2.5 Flash
LMArena Text: creative writing14041400DeepSeek V3.2 Exp
LMArena VisionNot measured1235Only Gemini 2.5 Flash measured
LMArena Text: English14411418DeepSeek V3.2 Exp
LMArena Text: non-English14041409Gemini 2.5 Flash
LMArena Text: Chinese14611449DeepSeek V3.2 Exp
LMArena Text: German14401417DeepSeek V3.2 Exp
LMArena Text: Japanese13311396Gemini 2.5 Flash
LMArena Text: Korean13721385Gemini 2.5 Flash
LMArena Text: French14341432DeepSeek V3.2 Exp
LMArena Text: Spanish14411422DeepSeek V3.2 Exp
LMArena Text: Russian14121415Gemini 2.5 Flash
LMArena Text: Polish14351413DeepSeek V3.2 Exp
LMArena Text: multi-turn14221408DeepSeek V3.2 Exp
LMArena Text: longer queries14181419Gemini 2.5 Flash
LMArena Text: writing and language14071405DeepSeek V3.2 Exp
LMArena Text: entertainment and media14071393DeepSeek V3.2 Exp
LMArena Vision: EnglishNot measured1239Only Gemini 2.5 Flash measured
LMArena Vision: ChineseNot measured1284Only Gemini 2.5 Flash measured
LMArena Vision: reading textNot measured1237Only Gemini 2.5 Flash measured
LMArena Vision: humorNot measured1225Only Gemini 2.5 Flash measured
LMArena Vision: captioningNot measured1277Only Gemini 2.5 Flash measured
LMArena Vision: recognizing thingsNot measured1252Only Gemini 2.5 Flash measured
LMArena Vision: creative writingNot measured1241Only Gemini 2.5 Flash measured
Neither model has a result on the other 12 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V3.2 Exp and Gemini 2.5 Flash
BenchmarkDeepSeek V3.2 ExpGemini 2.5 FlashLeader
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 Exp$0.0005Fits in one request
Gemini 2.5 Flash$0.0015Fits in one request

DeepSeek V3.2 Exp costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2 Exp$0.015Fits in one request
Gemini 2.5 Flash$0.022Fits in one request

DeepSeek V3.2 Exp costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2 Exp$0.058Exceeds the context window
Gemini 2.5 Flash$0.085Fits in one request

DeepSeek V3.2 Exp 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 Exp and Gemini 2.5 Flash
SpecificationDeepSeek V3.2 ExpGoogle Gemini 2.5 Flash
MakerDeepSeekGoogle
WeightsOpen weightsNo public weights listed
ReleasedSeptember 29, 2025June 17, 2025
Context window164K1.05M
Input $/Mtok$0.27$0.30
Output $/Mtok$0.41$2.50
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it10
Cost per solved taskNot computable$0.466
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.2 Exp better than Gemini 2.5 Flash for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.2 Exp for output tokens 6.1x cheaper ($0.41 vs $2.50 per million) and work measured by LMArena Text: expert or Kagi LLM Benchmark, where it leads.

Which is cheaper, DeepSeek V3.2 Exp or Gemini 2.5 Flash?

DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; Gemini 2.5 Flash costs $0.30 and $2.50. Cost per solved task is known for Gemini 2.5 Flash only.

Which has the bigger context window, DeepSeek V3.2 Exp or Gemini 2.5 Flash?

Gemini 2.5 Flash, with 1.05M tokens against 164K.

How many benchmarks is the DeepSeek V3.2 Exp vs Gemini 2.5 Flash comparison based on?

51 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2 Exp's coding index rests on 1 independent benchmark and Gemini 2.5 Flash's on 0. Where only one model has a result, the page says so instead of naming a winner.

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