Gemma 4 12B vs NVIDIA Nemotron 3.5 Lightning 30B A3B

At a Glance

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Gemma 4 12BGoogle DeepMind
Pricing and Limits
Context windowMaximum documented tokens262K1,049K
Model facts checkedSep 3, 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

FieldGemma 4 12BNVIDIA Nemotron 3.5 Lightning 30B-A3B
DeveloperGoogle DeepMindNVIDIA
FamilyGemma 4Nvidia Nemotron 3 5 Lightning
ModelGemma 4 12BNVIDIA Nemotron 3.5 Lightning 30B-A3B
VersionGemma 4 12BNVIDIA Nemotron 3.5 Lightning 30B-A3B
Lifecycleactiveactive
Released2026-05-232026-08-11
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, Video, AudioText
Output modalitiesTextText
Context window262K1,049K
Total parameters12B30B
Active parametersUnknown3B
Licenseapache-2.0openmdw-1.1
Open weightsYesYes
API availableYesYes
Self-hostableYesYes
Provider accessTogether Ai (Standard)Fireworks Ai (Standard)
Capabilitieschat, generation, reasoning, structured_outputs, toolsagents, chat, generation, reasoning, tools

Gemma 4 12B Capabilities

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

NVIDIA Nemotron 3.5 Lightning 30B A3B Capabilities

agentschatgenerationreasoningtools
Serving providers1
Canonical IDnvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16

Primary Evidence

Sources and Freshness

Questions

Gemma 4 12B vs NVIDIA Nemotron 3.5 Lightning 30B A3B FAQs

Is Gemma 4 12B or NVIDIA Nemotron 3.5 Lightning 30B A3B better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Gemma 4 12B and NVIDIA Nemotron 3.5 Lightning 30B A3B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Gemma 4 12B or NVIDIA Nemotron 3.5 Lightning 30B A3B?+

Neither model has a directly sourced input price in this comparison. Neither model has a directly sourced output price in this comparison.

Which has a larger context window, Gemma 4 12B or NVIDIA Nemotron 3.5 Lightning 30B A3B?+

NVIDIA Nemotron 3.5 Lightning 30B A3B has the larger sourced context window. Gemma 4 12B supports 262K and NVIDIA Nemotron 3.5 Lightning 30B A3B supports 1,049K.

Which performs better in benchmarks, Gemma 4 12B or NVIDIA Nemotron 3.5 Lightning 30B A3B?+

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

Can Gemma 4 12B or NVIDIA Nemotron 3.5 Lightning 30B A3B be self-hosted?+

Both models have the same recorded self-hosting status: supported. Gemma 4 12B is open weight; NVIDIA Nemotron 3.5 Lightning 30B A3B is open weight.

Can Gemma 4 12B and NVIDIA Nemotron 3.5 Lightning 30B A3B understand images?+

Gemma 4 12B is documented with image input; NVIDIA Nemotron 3.5 Lightning 30B A3B is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Gemma 4 12B or NVIDIA Nemotron 3.5 Lightning 30B A3B?+

Neither has a larger sourced maximum output. Gemma 4 12B is — and NVIDIA Nemotron 3.5 Lightning 30B A3B is —.

Do Gemma 4 12B and NVIDIA Nemotron 3.5 Lightning 30B A3B support reasoning and tool use?+

Gemma 4 12B: reasoning, tool calling, and image input. NVIDIA Nemotron 3.5 Lightning 30B A3B: reasoning and tool calling. Feature support does not establish relative quality.

Which is available from more inference providers, Gemma 4 12B or NVIDIA Nemotron 3.5 Lightning 30B A3B?+

Gemma 4 12B has 1 sourced provider route; NVIDIA Nemotron 3.5 Lightning 30B A3B has 1, a tie.

Which offers better value, Gemma 4 12B or NVIDIA Nemotron 3.5 Lightning 30B A3B?+

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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