Gemma 4 12B vs Inkling

Benchmark Performance

Available Benchmarks

No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.
FieldAt a Glance
Google DeepMind · activeGemma 4 12BVerified Sep 3, 2026
Thinking Machines Lab · activeInklingVerified Sep 3, 2026

Technical Differences

Side-by-Side Facts

Indexable
FieldGemma 4 12BInkling
DeveloperGoogle DeepMindThinking Machines Lab
FamilyGemma 4Inkling
ModelGemma 4 12BInkling
VersionGemma 4 12BInkling
Lifecycleactiveactive
Released2026-05-232026-07-15
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, Video, AudioText, Image, Video, Audio
Output modalitiesTextText
Context window262,1441,048,576
Total parameters12,000,000,000975,000,000,000
Active parametersUnknown41,000,000,000
Licenseapache-2.0apache-2.0
Open weightsYesYes
API availableYesYes
Self-hostableYesYes
Provider accessTogether Ai (Standard)Deepinfra (Standard), Fireworks Ai (Standard), Openrouter (Standard), Together Ai (Standard)
Capabilitieschat, generation, reasoning, structured_outputs, toolsagents, chat, coding, generation, reasoning, tools, vision

16 comparable fields · 9 material differences · Pair passes the primary-source comparison gate

Gemma 4 12B Capabilities

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

Inkling Capabilities

agentschatcodinggenerationreasoningtoolsvision
Input price$0.95
Output price$4.05
Serving providers4
Canonical IDthinkingmachines/Inkling

Internal Comparison Graph

Related Comparisons

All audio comparisons →
APairBContext
vsfamily variantsimage, text
vscross-developer peerstext
vscross-developer peersimage, text
vscross-developer peerstext
vscross-developer peersaudio, image, text, video
vscross-developer peersaudio, image, text, video
vsfamily variantsaudio, image, text, video
vsfamily variantsaudio, image, text, video
vscross-developer peersaudio, image, text, video
vscross-developer peersimage, text, video
vsfamily variantsimage, text
vscross-developer peersaudio, image, text

Primary Evidence

Sources and Freshness

Questions

Gemma 4 12B vs Inkling FAQs

Is Gemma 4 12B or Inkling better for coding?+

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

Which is cheaper, Gemma 4 12B or Inkling?+

Only Inkling has a directly sourced input price: $0.95 per million tokens. Only Inkling has a directly sourced output price: $4.05 per million tokens.

Which has a larger context window, Gemma 4 12B or Inkling?+

Inkling has the larger sourced context window. Gemma 4 12B supports 262,144 and Inkling supports 1,048,576.

Which performs better in benchmarks, Gemma 4 12B or Inkling?+

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 Inkling be self-hosted?+

Both models have the same recorded self-hosting status: supported. Gemma 4 12B is open weight; Inkling is open weight.

Can Gemma 4 12B and Inkling understand images?+

Gemma 4 12B is documented with image input; Inkling is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Gemma 4 12B or Inkling?+

Neither has a larger sourced maximum output. Gemma 4 12B is — and Inkling is —.

Do Gemma 4 12B and Inkling support reasoning and tool use?+

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

Which is available from more inference providers, Gemma 4 12B or Inkling?+

Gemma 4 12B has 1 sourced provider route; Inkling has 4, so Inkling has broader tracked availability.

Which offers better value, Gemma 4 12B or Inkling?+

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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