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Model profile
Google

Google: Gemma 3 12B

Google: Gemma 3 12B is a budget-priced multimodal generalist from Google with partial runtime data, extended context posture, and the clearest fit around long-context research / multimodal.

Best for: Long-context research / MultimodalN/A latencyExtended contextBudget pricing
Vision models
Intelligence
N/A

Benchmark blend

Coding
N/A

Dev workflow signal

Context
131K Tokens

Extended

Input Price
$0.04

Budget tier

Decision snapshot
81

Google: Gemma 3 12B currently reads as a budget multimodal option with extended context and a partially published runtime profile.

Overall profile
Flagship profile
Best for
Long-context research / Multimodal
Latency tier
N/A
Price tier
Budget
Source coverage
OpenRouterVision signal
Open compare hubSee leaderboard

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
N/A
N/A

General reasoning and benchmark headroom.

Unavailable
Speed
N/A
N/A

Latency data is partial.

Unavailable
Context
131K Tokens
76

How much prompt and task state can stay in view.

Competitive
Price
$0.04
86

$0.13 output / 1M

Efficient

Editorial Profile

Google: Gemma 3 12B in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Flagship profileCoding score N/AMath score N/AVision enabled

Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities,...

Identity

Google multimodal profile

Positioning

Long-context research / Multimodal with extended context and partially published runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths

  • Large context headroom supports repo-wide prompts and long research sessions.

  • Vision-capable routing opens up multimodal review and extraction workflows.

Tradeoffs

  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Latency data is incomplete, so interactive responsiveness is harder to rank confidently.

Best fit

  • Image-grounded review, multimodal extraction, and UI audit workflows.

  • Long-context summarization, repo analysis, and policy or document review.

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Context
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Input Price
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Context
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Input Price
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Gemini 3 Flash Preview (Reasoning)

Intelligence
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Context
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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

No benchmark data is available for this model yet.

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
131K Tokens
Vision
Enabled
Modalities
image, text
Tokenizer
Gemini
Moderation
No
Supported Parameters
frequency_penaltylogit_biasmax_tokensmin_ppresence_penaltyrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetop_ktop_p
Input Modalities
imagetext
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.04
Output
per 1M output tokens
$0.13
Blended
AA 3:1 mix
N/A

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.