Enter opens the highlighted result. Esc closes.

Gemma 4 31B vs Step 3.5 Flash

Step 3.5 Flash leads Gemma 4 31B on the Artificial Analysis Intelligence Index, 17.0 to 14.7, and Step 3.5 Flash costs 1.3x less per output token ($0.30 vs $0.40 per million). Updated October 21, 2026; 32 benchmarks have a result for both.

Gemma 4 31BGoogle
Coding
39.0
Overall
14.7
Output $/M
$0.40
29
Step 3.5 FlashStepFun
Coding
No data
Overall
17.0
Output $/M
$0.30
3
vs boards won of 32 shared

Gemma 4 31B or Step 3.5 Flash: which to pick

Only Gemma 4 31B has a coding score, 39.0 on the Vibe Coding Index; Step 3.5 Flash has none, so the two cannot be ranked on coding.

Pick Step 3.5 Flash for output tokens 1.3x cheaper ($0.30 vs $0.40 per million) and work measured by AA Intelligence Index or τ²-Bench Telecom, where it leads.

Shared results
32
Gemma 4 31B only
36
Step 3.5 Flash only
3
Like-for-like categories
7 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
Gemma 4 31B 39.0 from 2 benchmarks, Step 3.5 Flash not benchmarked.
Widest category
Agentic work, 28.9 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: Gemma 4 31B 65.5% against Step 3.5 Flash 94.4%, 28.9 points apart on a 0 to 100 scale.
Output price
Gemma 4 31B $0.40 against Step 3.5 Flash $0.30 per million output tokens, 1.3x apart.
Context window
Both take 262K tokens.

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
CategoryGemma 4 31BBarsStep 3.5 FlashTies
Coding30
Intelligence01
Agentic work01
Reasoning41
Math20
Knowledge40
General160
All shared boards293
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
Coding benchmarks for Gemma 4 31B and Step 3.5 Flash
BenchmarkGemma 4 31BStep 3.5 FlashLeader
LiveBench coding49.0%Not measuredOnly Gemma 4 31B measured
LMArena WebDev1367Not measuredOnly Gemma 4 31B measured
AA Coding Index43.4Not measuredOnly Gemma 4 31B measured
SciCode45.5%Not measuredOnly Gemma 4 31B measured
Terminal-Bench Hard (Artificial Analysis run)36.4%32.6%Gemma 4 31B
WeirdML52.3%Not measuredOnly Gemma 4 31B measured
ALE-Bench925.5Not measuredOnly Gemma 4 31B measured
LMArena Text: coding14591436Gemma 4 31B
LMArena Text: software and IT14631434Gemma 4 31B
LMArena WebDev: React1359Not measuredOnly Gemma 4 31B measured
LMArena WebDev: HTML1380Not measuredOnly Gemma 4 31B measured
Neither model has a result on the other 27 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Gemma 4 31B and Step 3.5 Flash
BenchmarkGemma 4 31BStep 3.5 FlashLeader
AA Intelligence Index14.717.0Step 3.5 Flash
Epoch Capabilities Index143.0Not measuredOnly Gemma 4 31B measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Gemma 4 31B and Step 3.5 Flash
BenchmarkGemma 4 31BStep 3.5 FlashLeader
AA Agentic Index4.2Not measuredOnly Gemma 4 31B measured
LiveBench research44.4%Not measuredOnly Gemma 4 31B measured
LiveBench browser4.0%Not measuredOnly Gemma 4 31B measured
LiveBench long agentic13.3%Not measuredOnly Gemma 4 31B measured
τ²-Bench Telecom65.5%94.4%Step 3.5 Flash
Neither model has a result on the other 34 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index41
Reasoning benchmarks for Gemma 4 31B and Step 3.5 Flash
BenchmarkGemma 4 31BStep 3.5 FlashLeader
LiveBench reasoning68.3%Not measuredOnly Gemma 4 31B measured
GPQA Diamond (Artificial Analysis run)85.7%83.1%Gemma 4 31B
Humanity's Last Exam (Artificial Analysis run)23.6%24.5%Step 3.5 Flash
GPQA Diamond (Epoch AI run)75.8%Not measuredOnly Gemma 4 31B measured
LMArena Text: hard prompts14481411Gemma 4 31B
LMArena Text: expert14641421Gemma 4 31B
LMArena Text: hard prompts in English14561421Gemma 4 31B
LMArena Vision: diagrams1283Not measuredOnly Gemma 4 31B measured
LMArena Vision: homework1310Not measuredOnly 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 index20
Math benchmarks for Gemma 4 31B and Step 3.5 Flash
BenchmarkGemma 4 31BStep 3.5 FlashLeader
OTIS Mock AIME73.3%Not measuredOnly Gemma 4 31B measured
LMArena Text: math14631408Gemma 4 31B
LMArena Text: mathematical work14651421Gemma 4 31B
Neither model has a result on the other 10 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for Gemma 4 31B and Step 3.5 Flash
BenchmarkGemma 4 31BStep 3.5 FlashLeader
CaseLaw (Vals AI)52.6%Not measuredOnly Gemma 4 31B measured
MortgageTax (Vals AI)61.4%Not measuredOnly Gemma 4 31B measured
SimpleQA Verified10.4%Not measuredOnly Gemma 4 31B measured
LMArena Text: science14571415Gemma 4 31B
LMArena Text: medicine and healthcare14511408Gemma 4 31B
LMArena Text: legal and government14381408Gemma 4 31B
LMArena Text: business and finance14301394Gemma 4 31B
Neither model has a result on the other 10 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Web design benchmarks for Gemma 4 31B and Step 3.5 Flash
BenchmarkGemma 4 31BStep 3.5 FlashLeader
LiveBench design58.4%Not measuredOnly Gemma 4 31B measured
Neither model has a result on the other 8 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index160
General benchmarks for Gemma 4 31B and Step 3.5 Flash
BenchmarkGemma 4 31BStep 3.5 FlashLeader
LiveBench data analysis53.3%Not measuredOnly Gemma 4 31B measured
SAGE (Vals AI)55.0%Not measuredOnly Gemma 4 31B measured
IFBench75.6%66.5%Gemma 4 31B
Long context reasoning (AA-LCR)69.7%63.0%Gemma 4 31B
DTBench82.7%Not measuredOnly Gemma 4 31B measured
LMCA39.3%Not measuredOnly Gemma 4 31B measured
Surface Evolver Bench30.6%Not measuredOnly Gemma 4 31B measured
LMArena Text14431403Gemma 4 31B
LMArena Text: instruction following14331385Gemma 4 31B
LMArena Text: creative writing14171357Gemma 4 31B
LMArena Vision1276Not measuredOnly Gemma 4 31B measured
LMArena Text: English14511417Gemma 4 31B
LMArena Text: non-English14301385Gemma 4 31B
LMArena Text: Chinese14751446Gemma 4 31B
LMArena Text: GermanNot measured1405Only Step 3.5 Flash measured
LMArena Text: JapaneseNot measured1353Only Step 3.5 Flash measured
LMArena Text: KoreanNot measured1351Only Step 3.5 Flash measured
LMArena Text: French14361421Gemma 4 31B
LMArena Text: Spanish14421419Gemma 4 31B
LMArena Text: Russian14601385Gemma 4 31B
LMArena Text: Polish14331389Gemma 4 31B
LMArena Text: multi-turn14521404Gemma 4 31B
LMArena Text: longer queries14461402Gemma 4 31B
LMArena Text: writing and language14321372Gemma 4 31B
LMArena Text: entertainment and media14051360Gemma 4 31B
LMArena Vision: English1271Not measuredOnly Gemma 4 31B measured
LMArena Vision: Chinese1326Not measuredOnly Gemma 4 31B measured
LMArena Vision: reading text1282Not measuredOnly Gemma 4 31B measured
LMArena Vision: humor1280Not measuredOnly Gemma 4 31B measured
LMArena Vision: captioning1218Not measuredOnly Gemma 4 31B measured
LMArena Vision: recognizing things1226Not measuredOnly Gemma 4 31B measured
LMArena Vision: creative writing1285Not measuredOnly Gemma 4 31B measured
LMArena Document1425Not measuredOnly Gemma 4 31B measured
Neither model has a result on the other 13 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Gemma 4 31B and Step 3.5 Flash
BenchmarkGemma 4 31BStep 3.5 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

Gemma 4 31B$0.0003Fits in one request
Step 3.5 Flash$0.0003Fits in one request

Step 3.5 Flash costs less

Repository review

50K input + 3K output tokens

Gemma 4 31B$0.0082Fits in one request
Step 3.5 Flash$0.0059Fits in one request

Step 3.5 Flash costs less

Agent loop

200K input + 10K output tokens

Gemma 4 31B$0.032Fits in one request
Step 3.5 Flash$0.023Fits in one request

Step 3.5 Flash 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 Gemma 4 31B and Step 3.5 Flash
SpecificationGoogle Gemma 4 31BStepFun Step 3.5 Flash
MakerGoogleStepFun
WeightsOpen weightsOpen weights
ReleasedApril 2, 2026January 29, 2026
Context window262K262K
Input $/Mtok$0.14$0.10
Output $/Mtok$0.40$0.30
Coding index39.0Not benchmarked
Independent measurements behind it20
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemma 4 31B better than Step 3.5 Flash for coding?

Only Gemma 4 31B has a coding score, 39.0 on the Vibe Coding Index; Step 3.5 Flash has none, so the two cannot be ranked on coding. Pick Step 3.5 Flash for output tokens 1.3x cheaper ($0.30 vs $0.40 per million) and work measured by AA Intelligence Index or τ²-Bench Telecom, where it leads.

Which is cheaper, Gemma 4 31B or Step 3.5 Flash?

Gemma 4 31B costs $0.14 per million input tokens and $0.40 per million output tokens; Step 3.5 Flash costs $0.10 and $0.30. Cost per solved task is known for neither of them.

Which has the bigger context window, Gemma 4 31B or Step 3.5 Flash?

Neither: both take 262K tokens.

How many benchmarks is the Gemma 4 31B vs Step 3.5 Flash comparison based on?

32 benchmarks have a result for both models as of October 21, 2026. Gemma 4 31B's coding index rests on 2 independent benchmarks and Step 3.5 Flash's on 0. Where only one model has a result, the page says so instead of naming a winner.

Compare next