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DeepSeek V3.2 vs Gemini 2.5 Pro

DeepSeek V3.2 leads Gemini 2.5 Pro on coding, 30.0 to 19.1 on the Vibe Coding Index, and DeepSeek V3.2 costs 11.4x less per output token ($0.88 vs $10.00 per million). Updated October 22, 2026; 60 benchmarks have a result for both.

Replace DeepSeek V3.2 Replace Gemini 2.5 Pro
DeepSeek V3.2DeepSeek
Coding
30.0
Overall
37.9
Output $/M
$0.88
26
Gemini 2.5 ProGoogle
Coding
19.1
Overall
41.8
Output $/M
$10.00
33
vs boards won of 60 shared, 1 tied

DeepSeek V3.2 or Gemini 2.5 Pro: which to pick

Pick DeepSeek V3.2 for coding: it scores 30.0 on the Vibe Coding Index against 19.1 for Gemini 2.5 Pro.

Pick Gemini 2.5 Pro for a lower cost per solved task ($0.64 against $0.99) and a larger context window (1.05M against 164K tokens).

Model ADeepSeek V3.2DeepSeek30.0/1003 independent benchmarks$0.99 per solved taskModel BGemini 2.5 ProGoogle19.1/1003 independent benchmarks$0.64 per solved task
Shared results
60
DeepSeek V3.2 only
15
Gemini 2.5 Pro only
30
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 30.0 from 3 benchmarks, Gemini 2.5 Pro 19.1 from 3 benchmarks.
Widest category
Agentic work, 19.7 points apart on average over 2 shared benchmarks.
Widest single gap
τ²-Bench Telecom: DeepSeek V3.2 90.6% against Gemini 2.5 Pro 54.1%, 36.5 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 $0.88 against Gemini 2.5 Pro $10.00 per million output tokens, 11.4x apart.
Context window
DeepSeek V3.2 takes 164K tokens, Gemini 2.5 Pro 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 V3.2BarsGemini 2.5 ProTies
Coding101
Intelligence20
Agentic work20
Reasoning25
Math22
Knowledge061 tie
Web design51
General318
All shared boards26331 tie
CodingLike-for-likeSix of these boards feed the Vibe Coding Index101
Coding benchmarks for DeepSeek V3.2 and Gemini 2.5 Pro
BenchmarkDeepSeek V3.2Gemini 2.5 ProLeader
SWE-bench Verified70.0%53.6%DeepSeek V3.2
SWE-bench Multilingual59.0%Not measuredOnly DeepSeek V3.2 measured
LMArena WebDev13621226DeepSeek V3.2
AA Coding Index44.233.3DeepSeek V3.2
LiveCodeBench (Vals AI)80.7%Not measuredOnly DeepSeek V3.2 measured
SWE-bench Verified (Vals AI)67.6%54.4%DeepSeek V3.2
Vibe Code Bench (Vals AI)5.1%0.4%DeepSeek V3.2
LiveCodeBench (Artificial Analysis run)86.2%80.1%DeepSeek V3.2
SciCodeNot measured46.3%Only Gemini 2.5 Pro measured
Terminal-Bench Hard (Artificial Analysis run)35.6%26.5%DeepSeek V3.2
Terminal-Bench39.6%32.6%DeepSeek V3.2
GSO-BenchNot measured3.9%Only Gemini 2.5 Pro measured
WeirdMLNot measured54.0%Only Gemini 2.5 Pro measured
LMArena Text: coding14531452DeepSeek V3.2
LMArena Text: software and IT14491459Gemini 2.5 Pro
LMArena WebDev: React1350Not measuredOnly DeepSeek V3.2 measured
LMArena WebDev: HTML13711235DeepSeek V3.2
LMArena WebDev: from an imageNot measured1285Only Gemini 2.5 Pro measured
Neither model has a result on the other 21 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for DeepSeek V3.2 and Gemini 2.5 Pro
BenchmarkDeepSeek V3.2Gemini 2.5 ProLeader
AA Intelligence Index21.516.1DeepSeek V3.2
Epoch Capabilities Index146.1145.1DeepSeek V3.2
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
Agentic work benchmarks for DeepSeek V3.2 and Gemini 2.5 Pro
BenchmarkDeepSeek V3.2Gemini 2.5 ProLeader
AA Agentic IndexNot measured1.6Only Gemini 2.5 Pro measured
τ²-Bench Telecom90.6%54.1%DeepSeek V3.2
APEX-Agents21.3%Not measuredOnly DeepSeek V3.2 measured
GDPvalNot measured23.3%Only Gemini 2.5 Pro measured
DeepResearch BenchNot measured41.5%Only Gemini 2.5 Pro measured
Vending-Bench 2$1,034$574DeepSeek V3.2
BrowseComp-PlusNot measured29.5%Only Gemini 2.5 Pro measured
Neither model has a result on the other 34 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index25
Reasoning benchmarks for DeepSeek V3.2 and Gemini 2.5 Pro
BenchmarkDeepSeek V3.2Gemini 2.5 ProLeader
GPQA Diamond80.3%Not measuredOnly DeepSeek V3.2 measured
GPQA Diamond (Artificial Analysis run)84.0%84.4%Gemini 2.5 Pro
Humanity's Last Exam (Artificial Analysis run)24.6%22.5%DeepSeek V3.2
GPQA Diamond (Epoch AI run)Not measured85.3%Only Gemini 2.5 Pro measured
ARC-AGI57.0%41.0%DeepSeek V3.2
ARC-AGI-24.0%4.9%Gemini 2.5 Pro
LMArena Text: hard prompts14341455Gemini 2.5 Pro
LMArena Text: expert14361452Gemini 2.5 Pro
LMArena Text: hard prompts in English14461453Gemini 2.5 Pro
LMArena Vision: diagramsNot measured1274Only Gemini 2.5 Pro measured
LMArena Vision: homeworkNot measured1281Only Gemini 2.5 Pro measured
Kagi LLM BenchmarkNot measured70.3%Only Gemini 2.5 Pro measured
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index22
Math benchmarks for DeepSeek V3.2 and Gemini 2.5 Pro
BenchmarkDeepSeek V3.2Gemini 2.5 ProLeader
AIME84.6%Not measuredOnly DeepSeek V3.2 measured
MGSM (Vals AI)86.0%Not measuredOnly DeepSeek V3.2 measured
AA Math Index92.087.7DeepSeek V3.2
AIME 2025 (Artificial Analysis run)92.0%87.7%DeepSeek V3.2
OTIS Mock AIMENot measured84.7%Only Gemini 2.5 Pro measured
FrontierMath, tiers 1 to 3Not measured24.6%Only Gemini 2.5 Pro measured
FrontierMath, tier 4Not measured0.0%Only Gemini 2.5 Pro measured
LMArena Text: math14351450Gemini 2.5 Pro
LMArena Text: mathematical work14401454Gemini 2.5 Pro
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index06
Knowledge benchmarks for DeepSeek V3.2 and Gemini 2.5 Pro
BenchmarkDeepSeek V3.2Gemini 2.5 ProLeader
MMLU Pro84.9%Not measuredOnly DeepSeek V3.2 measured
LegalBench (Vals AI)76.1%Not measuredOnly DeepSeek V3.2 measured
CaseLaw (Vals AI)55.4%63.9%Gemini 2.5 Pro
CorpFin (Vals AI)51.0%60.8%Gemini 2.5 Pro
TaxEval (Vals AI)68.2%Not measuredOnly DeepSeek V3.2 measured
MortgageTax (Vals AI)Not measured68.9%Only Gemini 2.5 Pro measured
MedQA (Vals AI)93.9%Not measuredOnly DeepSeek V3.2 measured
MedCode (Vals AI)Not measured50.6%Only Gemini 2.5 Pro measured
MMLU Pro (Artificial Analysis run)86.2%86.2%Tie
LMArena Text: science14461480Gemini 2.5 Pro
LMArena Text: medicine and healthcare14401476Gemini 2.5 Pro
LMArena Text: legal and government14251480Gemini 2.5 Pro
LMArena Text: business and finance14101439Gemini 2.5 Pro
FACTS Benchmark SuiteNot measured61.5%Only Gemini 2.5 Pro measured
Neither model has a result on the other 4 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index51
Web design benchmarks for DeepSeek V3.2 and Gemini 2.5 Pro
BenchmarkDeepSeek V3.2Gemini 2.5 ProLeader
Design Arena: websites11751167DeepSeek V3.2
Design Arena: UI components11461137DeepSeek V3.2
Design Arena: game dev11311111DeepSeek V3.2
Design Arena: data viz11581222Gemini 2.5 Pro
Design Arena: 3D11321080DeepSeek V3.2
Design Arena: all code categories11631152DeepSeek V3.2
Design Arena: SVG1032Not measuredOnly DeepSeek V3.2 measured
Design Arena: ASCII art1083Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index318
General benchmarks for DeepSeek V3.2 and Gemini 2.5 Pro
BenchmarkDeepSeek V3.2Gemini 2.5 ProLeader
SAGE (Vals AI)Not measured41.9%Only Gemini 2.5 Pro measured
MedScribe (Vals AI)Not measured73.5%Only Gemini 2.5 Pro measured
IFBench60.7%48.7%DeepSeek V3.2
Long context reasoning (AA-LCR)73.3%69.0%DeepSeek V3.2
DTBench85.6%82.4%DeepSeek V3.2
LMCA28.8%34.8%Gemini 2.5 Pro
Creative writing (Lech Mazur)Not measured83.8%Only Gemini 2.5 Pro measured
CL-bench12.4%Not measuredOnly DeepSeek V3.2 measured
CL-bench Life7.4%Not measuredOnly DeepSeek V3.2 measured
LMArena Text14251458Gemini 2.5 Pro
LMArena Text: instruction following14131437Gemini 2.5 Pro
LMArena Text: creative writing14001454Gemini 2.5 Pro
LMArena VisionNot measured1263Only Gemini 2.5 Pro measured
LMArena Text: English14391458Gemini 2.5 Pro
LMArena Text: non-English14091452Gemini 2.5 Pro
LMArena Text: Chinese14551507Gemini 2.5 Pro
LMArena Text: German14131487Gemini 2.5 Pro
LMArena Text: Japanese13741461Gemini 2.5 Pro
LMArena Text: Korean13701434Gemini 2.5 Pro
LMArena Text: French14291473Gemini 2.5 Pro
LMArena Text: Spanish14241474Gemini 2.5 Pro
LMArena Text: Russian14241461Gemini 2.5 Pro
LMArena Text: Polish14191479Gemini 2.5 Pro
LMArena Text: multi-turn14271453Gemini 2.5 Pro
LMArena Text: longer queries14281449Gemini 2.5 Pro
LMArena Text: writing and language14071454Gemini 2.5 Pro
LMArena Text: entertainment and media13951441Gemini 2.5 Pro
LMArena Vision: EnglishNot measured1261Only Gemini 2.5 Pro measured
LMArena Vision: ChineseNot measured1300Only Gemini 2.5 Pro measured
LMArena Vision: reading textNot measured1270Only Gemini 2.5 Pro measured
LMArena Vision: humorNot measured1262Only Gemini 2.5 Pro measured
LMArena Vision: captioningNot measured1304Only Gemini 2.5 Pro measured
LMArena Vision: recognizing thingsNot measured1283Only Gemini 2.5 Pro measured
LMArena Vision: creative writingNot measured1265Only Gemini 2.5 Pro measured
LMArena DocumentNot measured1421Only Gemini 2.5 Pro 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 2.5 Pro
BenchmarkDeepSeek V3.2Gemini 2.5 ProLeader
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$0.0007Fits in one request
Gemini 2.5 Pro$0.0063Fits in one request

DeepSeek V3.2 costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2$0.017Fits in one request
Gemini 2.5 Pro$0.092Fits in one request

DeepSeek V3.2 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2$0.067Exceeds the context window
Gemini 2.5 Pro$0.350Fits 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 2.5 Pro
SpecificationDeepSeek V3.2Google Gemini 2.5 Pro
MakerDeepSeekGoogle
WeightsOpen weightsNo public weights listed
ReleasedDecember 1, 2025June 17, 2025
Context window164K1.05M
Input $/Mtok$0.29$1.25
Output $/Mtok$0.88$10.00
Coding index30.019.1
Independent measurements behind it33
Cost per solved task$0.99$0.64
SourceOpenRouter listingOpenRouter listing

Questions

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

Pick DeepSeek V3.2 for coding: it scores 30.0 on the Vibe Coding Index against 19.1 for Gemini 2.5 Pro. Pick Gemini 2.5 Pro for a lower cost per solved task ($0.64 against $0.99) and a larger context window (1.05M against 164K tokens).

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

DeepSeek V3.2 costs $0.29 per million input tokens and $0.88 per million output tokens; Gemini 2.5 Pro costs $1.25 and $10.00. Per solved SWE-bench Verified task, DeepSeek V3.2 costs $0.99 and Gemini 2.5 Pro $0.64.

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

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

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

60 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2's coding index rests on 3 independent benchmarks and Gemini 2.5 Pro's on 3. Where only one model has a result, the page says so instead of naming a winner.

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