Gemini 3.1 Flash Lite vs Gemma 4 12B

At a Glance

Compare
Gemini 3.1 Flash LiteGoogle DeepMind
Gemma 4 12BGoogle DeepMind
Pricing and Limits
Input priceFrom · USD / 1M tokens$0.25Google AI · Aug 29, 2026Not reported
Output priceFrom · USD / 1M tokens$1.50Google AI · Aug 29, 2026Not reported
Context windowMaximum documented tokens1,049K262K
Model facts checkedAug 29, 2026View model evidence →Sep 3, 2026View model evidence →

Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →

Available Benchmarks

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldGemini 3.1 Flash-LiteGemma 4 12B
DeveloperGoogle DeepMindGoogle DeepMind
FamilyGemini 3Gemma 4
ModelGemini 3.1 Flash-LiteGemma 4 12B
VersionGemini 3.1 Flash-LiteGemma 4 12B
Lifecycleactiveactive
ReleasedUnknown2026-05-23
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, Video, Audio, DocumentText, Image, Video, Audio
Output modalitiesTextText
Context window1,049K262K
Total parametersUnknown12B
Active parametersUnknownUnknown
LicenseUnknownapache-2.0
Open weightsNoYes
API availableYesYes
Self-hostableNoYes
Provider accessGoogle AI (Standard), Google Gemini (Standard)Together Ai (Standard)
Capabilitieschat, generation, reasoning, toolschat, generation, reasoning, structured_outputs, tools

Gemini 3.1 Flash Lite Capabilities

chatgenerationreasoningtools
Serving providers2
Canonical IDgoogle-deepmind/gemini-3.1-flash-lite

Gemma 4 12B Capabilities

chatgenerationreasoningstructured outputstools
Serving providers1
Canonical IDgoogle/gemma-4-12B-it

Primary Evidence

Sources and Freshness

Questions

Gemini 3.1 Flash Lite vs Gemma 4 12B FAQs

Is Gemini 3.1 Flash Lite or Gemma 4 12B better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.1 Flash Lite and Gemma 4 12B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Gemini 3.1 Flash Lite or Gemma 4 12B?+

Only Gemini 3.1 Flash Lite has a directly sourced input price: $0.25 per million tokens. Only Gemini 3.1 Flash Lite has a directly sourced output price: $1.50 per million tokens.

Which has a larger context window, Gemini 3.1 Flash Lite or Gemma 4 12B?+

Gemini 3.1 Flash Lite has the larger sourced context window. Gemini 3.1 Flash Lite supports 1,049K and Gemma 4 12B supports 262K.

Which performs better in benchmarks, Gemini 3.1 Flash Lite or Gemma 4 12B?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can Gemini 3.1 Flash Lite or Gemma 4 12B be self-hosted?+

Gemma 4 12B is the only model in this pair currently marked as self-hostable. Gemini 3.1 Flash Lite is not marked open weight; Gemma 4 12B is open weight.

Can Gemini 3.1 Flash Lite and Gemma 4 12B understand images?+

Gemini 3.1 Flash Lite is documented with image input; Gemma 4 12B is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Gemini 3.1 Flash Lite or Gemma 4 12B?+

Neither has a larger sourced maximum output. Gemini 3.1 Flash Lite is 66K and Gemma 4 12B is —.

Do Gemini 3.1 Flash Lite and Gemma 4 12B support reasoning and tool use?+

Gemini 3.1 Flash Lite: reasoning, tool calling, and image input. Gemma 4 12B: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Gemini 3.1 Flash Lite or Gemma 4 12B?+

Gemini 3.1 Flash Lite has 2 sourced provider routes; Gemma 4 12B has 1, so Gemini 3.1 Flash Lite has broader tracked availability.

Which offers better value, Gemini 3.1 Flash Lite or Gemma 4 12B?+

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.

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