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DeepSeek V3.2 Exp vs Gemma 4 31B

Updated October 21, 2026. 38 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 Exp Replace Gemma 4 31B
DeepSeek V3.2 ExpDeepSeek
Coding
55.0
Overall
16.6
Output $/M
$0.41
7
Gemma 4 31BGoogle
Coding
38.3
Overall
14.7
Output $/M
$0.40
31
vs boards won of 38 shared

Decision reading

Pick DeepSeek V3.2 Exp for coding: it scores 55.0 on the Vibe Coding Index against 38.3 for Gemma 4 31B.

Pick Gemma 4 31B when output tokens cost 1.0x less, or when the context window is larger, 262K against 164K tokens.

Model ADeepSeek V3.2 ExpDeepSeek55.0/1002 independent benchmarksNo cost per solved taskModel BGemma 4 31BGoogle38.3/1002 independent benchmarksNo cost per solved task
Shared results
38
DeepSeek V3.2 Exp only
19
Gemma 4 31B only
30
Like-for-like categories
7 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.

At a glance

Coding index
DeepSeek V3.2 Exp 55.0 from 2 benchmarks, Gemma 4 31B 38.3 from 2 benchmarks.
Widest category
Agentic work, 31.6 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: DeepSeek V3.2 Exp 33.9% against Gemma 4 31B 65.5%, 31.6 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 Exp $0.41 against Gemma 4 31B $0.40 per million output tokens, 1.0x apart.
Context window
DeepSeek V3.2 Exp takes 164K tokens, Gemma 4 31B 262K.

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 ExpBarsGemma 4 31BTies
Coding06
Intelligence20
Agentic work01
Reasoning05
Math02
Knowledge13
General414
All shared boards731
CodingLike-for-likeFour of these boards feed the Vibe Coding Index06
Coding benchmarks for DeepSeek V3.2 Exp and Gemma 4 31B
BenchmarkDeepSeek V3.2 ExpGemma 4 31BLeader
Aider polyglot74.2%Not measuredOnly DeepSeek V3.2 Exp measured
LiveBench codingNot measured49.0%Only Gemma 4 31B measured
LMArena WebDev12721367Gemma 4 31B
AA Coding IndexNot measured43.4Only Gemma 4 31B measured
LiveCodeBench (Artificial Analysis run)78.9%Not measuredOnly DeepSeek V3.2 Exp measured
SciCodeNot measured45.5%Only Gemma 4 31B measured
Terminal-Bench Hard (Artificial Analysis run)31.1%36.4%Gemma 4 31B
WeirdML39.5%52.3%Gemma 4 31B
ALE-BenchNot measured925.5Only Gemma 4 31B measured
LMArena Text: coding14381459Gemma 4 31B
LMArena Text: software and IT14381463Gemma 4 31B
LMArena WebDev: ReactNot measured1359Only Gemma 4 31B measured
LMArena WebDev: HTML12811380Gemma 4 31B
Neither model has a result on the other 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for DeepSeek V3.2 Exp and Gemma 4 31B
BenchmarkDeepSeek V3.2 ExpGemma 4 31BLeader
AA Intelligence Index16.614.7DeepSeek V3.2 Exp
Epoch Capabilities Index144.9143.0DeepSeek V3.2 Exp
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for DeepSeek V3.2 Exp and Gemma 4 31B
BenchmarkDeepSeek V3.2 ExpGemma 4 31BLeader
AA Agentic IndexNot measured4.2Only Gemma 4 31B measured
LiveBench researchNot measured44.4%Only Gemma 4 31B measured
LiveBench browserNot measured4.0%Only Gemma 4 31B measured
LiveBench long agenticNot measured13.3%Only Gemma 4 31B measured
τ²-Bench Telecom33.9%65.5%Gemma 4 31B
TheAgentCompany42.9%Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for DeepSeek V3.2 Exp and Gemma 4 31B
BenchmarkDeepSeek V3.2 ExpGemma 4 31BLeader
LiveBench reasoningNot measured68.3%Only Gemma 4 31B measured
GPQA Diamond (Artificial Analysis run)79.7%85.7%Gemma 4 31B
Humanity's Last Exam (Artificial Analysis run)14.9%23.6%Gemma 4 31B
GPQA Diamond (Epoch AI run)Not measured75.8%Only Gemma 4 31B measured
LMArena Text: hard prompts14291448Gemma 4 31B
LMArena Text: expert14221464Gemma 4 31B
LMArena Text: hard prompts in English14441456Gemma 4 31B
LMArena Vision: diagramsNot measured1283Only Gemma 4 31B measured
LMArena Vision: homeworkNot measured1310Only Gemma 4 31B measured
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for DeepSeek V3.2 Exp and Gemma 4 31B
BenchmarkDeepSeek V3.2 ExpGemma 4 31BLeader
AA Math Index87.7Not measuredOnly DeepSeek V3.2 Exp measured
AIME 2025 (Artificial Analysis run)87.7%Not measuredOnly DeepSeek V3.2 Exp measured
OTIS Mock AIMENot measured73.3%Only Gemma 4 31B measured
LMArena Text: math14221463Gemma 4 31B
LMArena Text: mathematical work14331465Gemma 4 31B
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index13
Knowledge benchmarks for DeepSeek V3.2 Exp and Gemma 4 31B
BenchmarkDeepSeek V3.2 ExpGemma 4 31BLeader
CaseLaw (Vals AI)Not measured52.6%Only Gemma 4 31B measured
MortgageTax (Vals AI)Not measured61.4%Only Gemma 4 31B measured
MMLU Pro (Artificial Analysis run)85.0%Not measuredOnly DeepSeek V3.2 Exp measured
SimpleQA VerifiedNot measured10.4%Only Gemma 4 31B measured
LMArena Text: science14431457Gemma 4 31B
LMArena Text: medicine and healthcare14681451DeepSeek V3.2 Exp
LMArena Text: legal and government14191438Gemma 4 31B
LMArena Text: business and finance14121430Gemma 4 31B
Neither model has a result on the other 9 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for DeepSeek V3.2 Exp and Gemma 4 31B
BenchmarkDeepSeek V3.2 ExpGemma 4 31BLeader
Design Arena: websites1179Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: UI components1166Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: game dev1145Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: data viz1152Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: 3D1148Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: all code categories1171Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: SVG1031Not measuredOnly DeepSeek V3.2 Exp measured
LiveBench designNot measured58.4%Only Gemma 4 31B measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index414
General benchmarks for DeepSeek V3.2 Exp and Gemma 4 31B
BenchmarkDeepSeek V3.2 ExpGemma 4 31BLeader
LiveBench data analysisNot measured53.3%Only Gemma 4 31B measured
SAGE (Vals AI)Not measured55.0%Only Gemma 4 31B measured
IFBench54.1%75.6%Gemma 4 31B
Long context reasoning (AA-LCR)72.3%69.7%DeepSeek V3.2 Exp
DTBench87.7%82.7%DeepSeek V3.2 Exp
LMCA29.1%39.3%Gemma 4 31B
Fiction.LiveBench83.3%Not measuredOnly DeepSeek V3.2 Exp measured
CL-bench13.2%Not measuredOnly DeepSeek V3.2 Exp measured
Surface Evolver BenchNot measured30.6%Only Gemma 4 31B measured
CL-bench Life9.5%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text14251443Gemma 4 31B
LMArena Text: instruction following14011433Gemma 4 31B
LMArena Text: creative writing14041417Gemma 4 31B
LMArena VisionNot measured1276Only Gemma 4 31B measured
LMArena Text: English14411451Gemma 4 31B
LMArena Text: non-English14041430Gemma 4 31B
LMArena Text: Chinese14611475Gemma 4 31B
LMArena Text: German1440Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: Japanese1331Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: Korean1372Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: French14341436Gemma 4 31B
LMArena Text: Spanish14411442Gemma 4 31B
LMArena Text: Russian14121460Gemma 4 31B
LMArena Text: Polish14351433DeepSeek V3.2 Exp
LMArena Text: multi-turn14221452Gemma 4 31B
LMArena Text: longer queries14181446Gemma 4 31B
LMArena Text: writing and language14071432Gemma 4 31B
LMArena Text: entertainment and media14071405DeepSeek V3.2 Exp
LMArena Vision: EnglishNot measured1271Only Gemma 4 31B measured
LMArena Vision: ChineseNot measured1326Only Gemma 4 31B measured
LMArena Vision: reading textNot measured1282Only Gemma 4 31B measured
LMArena Vision: humorNot measured1280Only Gemma 4 31B measured
LMArena Vision: captioningNot measured1218Only Gemma 4 31B measured
LMArena Vision: recognizing thingsNot measured1226Only Gemma 4 31B measured
LMArena Vision: creative writingNot measured1285Only Gemma 4 31B measured
LMArena DocumentNot measured1425Only Gemma 4 31B measured
Neither model has a result on the other 10 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 Gemma 4 31B
BenchmarkDeepSeek V3.2 ExpGemma 4 31BLeader
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
Gemma 4 31B$0.0004Fits in one request

Gemma 4 31B costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2 Exp$0.015Fits in one request
Gemma 4 31B$0.011Fits in one request

Gemma 4 31B costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2 Exp$0.058Exceeds the context window
Gemma 4 31B$0.044Fits in one request

Gemma 4 31B 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 Gemma 4 31B
SpecificationDeepSeek V3.2 ExpGoogle Gemma 4 31B
MakerDeepSeekGoogle
WeightsOpen weightsOpen weights
ReleasedSeptember 29, 2025April 2, 2026
Context window164K262K
Input $/Mtok$0.27$0.20
Output $/Mtok$0.41$0.40
Coding index55.038.3
Independent measurements behind it22
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.2 Exp better than Gemma 4 31B for coding?

Pick DeepSeek V3.2 Exp for coding: it scores 55.0 on the Vibe Coding Index against 38.3 for Gemma 4 31B. Pick Gemma 4 31B when output tokens cost 1.0x less, or when the context window is larger, 262K against 164K tokens.

Which is cheaper, DeepSeek V3.2 Exp or Gemma 4 31B?

DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; Gemma 4 31B costs $0.20 and $0.40. Cost per solved task is known for neither of them.

Which has the bigger context window, DeepSeek V3.2 Exp or Gemma 4 31B?

Gemma 4 31B, with 262K tokens against 164K.

How many benchmarks is the DeepSeek V3.2 Exp vs Gemma 4 31B comparison based on?

38 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2 Exp's coding index rests on 2 independent benchmarks and Gemma 4 31B's on 2. Where only one model has a result, the page says so instead of naming a winner.

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