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Google: Gemma 3 12B vs NVIDIA: Nemotron 3.5 Super VL

Try both on us. Starter credit covers your first requests, then one key routes Gemma 3 12B and Nemotron 3.5 Super VL. Switch by changing the model name.

Compare routesProviders, price, context, and policy posture in one view.

Use auto when uptime mattersKeep a primary model and let fallback handle provider failures.

Same API shapeUse the OpenAI client and set the model you want.

Practical read

Monthly evidence

Google: Gemma 3 12B has the lower published input-plus-output rate and more Credits provider routes. NVIDIA: Nemotron 3.5 Super VL has the larger context window. There is not enough recent probe data to compare speed.

$0.15825/1MGoogle: Gemma 3 12B cheapest route

$0.5275/1MNVIDIA: Nemotron 3.5 Super VL cheapest route

not enough dataGoogle: Gemma 3 12B fastest measured p50 TTFT

not enough dataNVIDIA: Nemotron 3.5 Super VL fastest measured p50 TTFT

The price comparison adds each route's rate for one million input tokens and one million output tokens; your cost depends on your input/output mix. Prices and live route measurements can change. Use the monthly reports when you need a stable evidence window, then run a small task-specific eval before choosing a production default.

Google: Gemma 3 12BNVIDIA: Nemotron 3.5 Super VL

Model idgoogle/gemma-3-12b-itnvidia/nemotron-3.5-super-vl

Publisher Google AI Studio NVIDIA

AI IQ — IQ 105#100

Context131,072 tokens1,048,576 tokens

Credits provider routes54

Cheapest route$0.15825/1M$0.5275/1M

Cached input$0.05275/1M$0.0211/1M to $0.089675/1M

Privacy posturehas ZDR routehas ZDR route

Fastest measured p50 TTFTnot enough datanot enough data

Highest measured throughputnot enough datanot enough data

Recent route uptime rangenot enough datanot enough data

Modes chat chat

Google: Gemma 3 12B routes

Overview5 Credits routes
PricingInput, cached input, and output rates
ProvidersAll serving providers

NVIDIA: Nemotron 3.5 Super VL routes

Overview4 Credits routes
PricingInput, cached input, and output rates
ProvidersAll serving providers

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NVIDIA: Nemotron 3.5 Super VL vs Z.ai: GLM 5.237 combined provider routes
NVIDIA: Nemotron 3.5 Super VL vs Z.ai: GLM 5.337 combined provider routes
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Google: Gemma 3 12B vs MoonshotAI: Kimi K333 combined provider routes
MoonshotAI: Kimi K3 vs NVIDIA: Nemotron 3.5 Super VL32 combined provider routes

Production choice

Pick a default model. Keep fallback enabled.

TrustedRouter is useful when you know the model you want, but still need provider rollover, budget limits, usage records, and a prompt path you can verify.

OpenAI clientPython

client = OpenAI(
    base_url="https://api-aws.trustedrouter.com/v1",
    api_key="sk-tr-v1-..."
)

response = client.chat.completions.create(
    model="google/gemma-3-12b-it",
    messages=messages,
)

Questions

Which should I use, Google: Gemma 3 12B or NVIDIA: Nemotron 3.5 Super VL?

Is Google: Gemma 3 12B or NVIDIA: Nemotron 3.5 Super VL cheaper?

The current cheapest TrustedRouter route is $0.15825/1M for Google: Gemma 3 12B and $0.5275/1M for NVIDIA: Nemotron 3.5 Super VL. The comparison uses current catalog prices and updates as provider pricing changes.

Is Google: Gemma 3 12B or NVIDIA: Nemotron 3.5 Super VL faster?

Current measured p50 time to first token is not enough data for Google: Gemma 3 12B and not enough data for NVIDIA: Nemotron 3.5 Super VL. These are routed probe measurements, not vendor-advertised speeds, and update as new samples arrive.

Can I test Google: Gemma 3 12B and NVIDIA: Nemotron 3.5 Super VL with the same API?

Yes. Use the same OpenAI-compatible TrustedRouter base URL and API key, then change only the model id between google/gemma-3-12b-it and nvidia/nemotron-3.5-super-vl. This makes side-by-side evals possible without maintaining two provider integrations.