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Gemini 2.5 Pro Preview 06-05 vs Gemma 4 31B

Gemma 4 31B costs 25.0x less per output token than Gemini 2.5 Pro Preview 06-05 ($0.40 vs $10.00 per million). Updated October 21, 2026; 4 benchmarks have a result for both.

Gemini 2.5 Pro Preview 06-05Google
Coding
No data
Overall
No data
Output $/M
$10.00
1
Gemma 4 31BGoogle
Coding
39.0
Overall
42.1
Output $/M
$0.40
3
vs boards won of 4 shared

Gemini 2.5 Pro Preview 06-05 or Gemma 4 31B: which to pick

Only Gemma 4 31B has a coding score, 39.0 on the Vibe Coding Index; Gemini 2.5 Pro Preview 06-05 has none, so the two cannot be ranked on coding.

Pick Gemini 2.5 Pro Preview 06-05 for a larger context window (1.05M against 262K tokens) and work measured by GPQA Diamond (Epoch AI run), where it leads.

Shared results
4
Gemini 2.5 Pro Preview 06-05 only
11
Gemma 4 31B only
67
Like-for-like categories
2 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
Gemini 2.5 Pro Preview 06-05 not benchmarked, Gemma 4 31B 39.0 from 2 benchmarks.
Widest category
Reasoning, 5.0 points apart on average over 3 shared benchmarks.
Widest single gap
GPQA Diamond (Epoch AI run): Gemini 2.5 Pro Preview 06-05 84.8% against Gemma 4 31B 75.8%, 9.1 points apart on a 0 to 100 scale.
Output price
Gemini 2.5 Pro Preview 06-05 $10.00 against Gemma 4 31B $0.40 per million output tokens, 25.0x apart.
Context window
Gemini 2.5 Pro Preview 06-05 takes 1.05M 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
CategoryGemini 2.5 Pro Preview 06-05BarsGemma 4 31BTies
Coding01
Reasoning12
All shared boards13
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
Coding benchmarks for Gemini 2.5 Pro Preview 06-05 and Gemma 4 31B
BenchmarkGemini 2.5 Pro Preview 06-05Gemma 4 31BLeader
Aider polyglot83.1%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
LiveBench codingNot measured49.0%Only Gemma 4 31B measured
LMArena WebDevNot measured1367Only Gemma 4 31B measured
AA Coding IndexNot measured43.4Only Gemma 4 31B measured
LiveCodeBench (Vals AI)79.2%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
LiveCodeBench (Artificial Analysis run)77.8%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
SciCodeNot measured45.5%Only Gemma 4 31B measured
Terminal-Bench Hard (Artificial Analysis run)Not measured36.4%Only Gemma 4 31B measured
GSO-Bench3.9%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
WeirdMLNot measured52.3%Only Gemma 4 31B measured
ALE-BenchNot measured925.5Only Gemma 4 31B measured
LMArena Text: codingNot measured1459Only Gemma 4 31B measured
LMArena Text: software and ITNot measured1463Only Gemma 4 31B measured
LMArena WebDev: ReactNot measured1359Only Gemma 4 31B measured
LMArena WebDev: HTMLNot measured1380Only Gemma 4 31B measured
SciCode7.7%8.9%Gemma 4 31B
Neither model has a result on the other 23 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Intelligence benchmarks for Gemini 2.5 Pro Preview 06-05 and Gemma 4 31B
BenchmarkGemini 2.5 Pro Preview 06-05Gemma 4 31BLeader
AA Intelligence IndexNot measured14.7Only Gemma 4 31B measured
Epoch Capabilities IndexNot measured143.0Only Gemma 4 31B measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Agentic work benchmarks for Gemini 2.5 Pro Preview 06-05 and Gemma 4 31B
BenchmarkGemini 2.5 Pro Preview 06-05Gemma 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 TelecomNot measured65.5%Only Gemma 4 31B measured
METR time horizons55.4%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
Remote Labor Index0.8%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
DeepResearch Bench42.8%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index12
Reasoning benchmarks for Gemini 2.5 Pro Preview 06-05 and Gemma 4 31B
BenchmarkGemini 2.5 Pro Preview 06-05Gemma 4 31BLeader
LiveBench reasoningNot measured68.3%Only Gemma 4 31B measured
GPQA Diamond (Artificial Analysis run)83.6%85.7%Gemma 4 31B
Humanity's Last Exam (Artificial Analysis run)19.9%23.6%Gemma 4 31B
GPQA Diamond (Epoch AI run)84.8%75.8%Gemini 2.5 Pro Preview 06-05
SimpleBench62.4%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
VPCT46.4%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
LMArena Text: hard promptsNot measured1448Only Gemma 4 31B measured
LMArena Text: expertNot measured1464Only Gemma 4 31B measured
LMArena Text: hard prompts in EnglishNot measured1456Only Gemma 4 31B measured
LMArena Vision: diagramsNot measured1283Only Gemma 4 31B measured
LMArena Vision: homeworkNot measured1310Only Gemma 4 31B measured
Kagi LLM BenchmarkNot measured63.5%Only Gemma 4 31B measured
Neither model has a result on the other 10 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
Math benchmarks for Gemini 2.5 Pro Preview 06-05 and Gemma 4 31B
BenchmarkGemini 2.5 Pro Preview 06-05Gemma 4 31BLeader
OTIS Mock AIMENot measured73.3%Only Gemma 4 31B measured
LMArena Text: mathNot measured1463Only Gemma 4 31B measured
LMArena Text: mathematical workNot measured1465Only Gemma 4 31B measured
Neither model has a result on the other 10 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Knowledge benchmarks for Gemini 2.5 Pro Preview 06-05 and Gemma 4 31B
BenchmarkGemini 2.5 Pro Preview 06-05Gemma 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)85.8%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
SimpleQA VerifiedNot measured10.4%Only Gemma 4 31B measured
LMArena Text: scienceNot measured1457Only Gemma 4 31B measured
LMArena Text: medicine and healthcareNot measured1451Only Gemma 4 31B measured
LMArena Text: legal and governmentNot measured1438Only Gemma 4 31B measured
LMArena Text: business and financeNot measured1430Only Gemma 4 31B measured
FACTS Benchmark SuiteNot measured45.3%Only Gemma 4 31B measured
Neither model has a result on the other 9 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Web design benchmarks for Gemini 2.5 Pro Preview 06-05 and Gemma 4 31B
BenchmarkGemini 2.5 Pro Preview 06-05Gemma 4 31BLeader
LiveBench designNot measured58.4%Only Gemma 4 31B measured
Neither model has a result on the other 8 boards here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index31 boards, none shared
General benchmarks for Gemini 2.5 Pro Preview 06-05 and Gemma 4 31B
BenchmarkGemini 2.5 Pro Preview 06-05Gemma 4 31BLeader
LiveBench data analysisNot measured53.3%Only Gemma 4 31B measured
SAGE (Vals AI)Not measured55.0%Only Gemma 4 31B measured
IFBenchNot measured75.6%Only Gemma 4 31B measured
Long context reasoning (AA-LCR)Not measured69.7%Only Gemma 4 31B measured
DTBenchNot measured82.7%Only Gemma 4 31B measured
LMCANot measured39.3%Only Gemma 4 31B measured
Fiction.LiveBench91.7%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
Surface Evolver BenchNot measured30.6%Only Gemma 4 31B measured
LMArena TextNot measured1443Only Gemma 4 31B measured
LMArena Text: instruction followingNot measured1433Only Gemma 4 31B measured
LMArena Text: creative writingNot measured1417Only Gemma 4 31B measured
LMArena VisionNot measured1276Only Gemma 4 31B measured
LMArena Text: EnglishNot measured1451Only Gemma 4 31B measured
LMArena Text: non-EnglishNot measured1430Only Gemma 4 31B measured
LMArena Text: ChineseNot measured1475Only Gemma 4 31B measured
LMArena Text: FrenchNot measured1436Only Gemma 4 31B measured
LMArena Text: SpanishNot measured1442Only Gemma 4 31B measured
LMArena Text: RussianNot measured1460Only Gemma 4 31B measured
LMArena Text: PolishNot measured1433Only Gemma 4 31B measured
LMArena Text: multi-turnNot measured1452Only Gemma 4 31B measured
LMArena Text: longer queriesNot measured1446Only Gemma 4 31B measured
LMArena Text: writing and languageNot measured1432Only Gemma 4 31B measured
LMArena Text: entertainment and mediaNot measured1405Only Gemma 4 31B measured
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 15 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Gemini 2.5 Pro Preview 06-05 and Gemma 4 31B
BenchmarkGemini 2.5 Pro Preview 06-05Gemma 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

Gemini 2.5 Pro Preview 06-05$0.0063Fits in one request
Gemma 4 31B$0.0003Fits in one request

Gemma 4 31B costs less

Repository review

50K input + 3K output tokens

Gemini 2.5 Pro Preview 06-05$0.092Fits in one request
Gemma 4 31B$0.0082Fits in one request

Gemma 4 31B costs less

Agent loop

200K input + 10K output tokens

Gemini 2.5 Pro Preview 06-05$0.350Fits in one request
Gemma 4 31B$0.032Fits 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 Gemini 2.5 Pro Preview 06-05 and Gemma 4 31B
SpecificationGoogle Gemini 2.5 Pro Preview 06-05Google Gemma 4 31B
MakerGoogleGoogle
WeightsNo public weights listedOpen weights
ReleasedJune 5, 2025April 2, 2026
Context window1.05M262K
Input $/Mtok$1.25$0.14
Output $/Mtok$10.00$0.40
Coding indexNot benchmarked39.0
Independent measurements behind it02
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemini 2.5 Pro Preview 06-05 better than Gemma 4 31B for coding?

Only Gemma 4 31B has a coding score, 39.0 on the Vibe Coding Index; Gemini 2.5 Pro Preview 06-05 has none, so the two cannot be ranked on coding. Pick Gemini 2.5 Pro Preview 06-05 for a larger context window (1.05M against 262K tokens) and work measured by GPQA Diamond (Epoch AI run), where it leads.

Which is cheaper, Gemini 2.5 Pro Preview 06-05 or Gemma 4 31B?

Gemini 2.5 Pro Preview 06-05 costs $1.25 per million input tokens and $10.00 per million output tokens; Gemma 4 31B costs $0.14 and $0.40. Cost per solved task is known for neither of them.

Which has the bigger context window, Gemini 2.5 Pro Preview 06-05 or Gemma 4 31B?

Gemini 2.5 Pro Preview 06-05, with 1.05M tokens against 262K.

How many benchmarks is the Gemini 2.5 Pro Preview 06-05 vs Gemma 4 31B comparison based on?

4 benchmarks have a result for both models as of October 21, 2026. Gemini 2.5 Pro Preview 06-05's coding index rests on 0 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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