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Google: Gemini 3.1 Flash Lite vs NVIDIA: Nemotron 3.5 Super VL

Try both on us. Starter credit covers your first requests, then one key routes Gemini 3.1 Flash Lite 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

NVIDIA: Nemotron 3.5 Super VL has the lower published input-plus-output rate and the lower measured p50 time to first token. Both have 4 Credits provider routes. Their context windows are the same size.

$1.84625/1MGoogle: Gemini 3.1 Flash Lite cheapest route

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

1411 msGoogle: Gemini 3.1 Flash Lite fastest measured p50 TTFT via google-ai-studio

1400 msNVIDIA: Nemotron 3.5 Super VL fastest measured p50 TTFT via io-net

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: Gemini 3.1 Flash LiteNVIDIA: Nemotron 3.5 Super VL

Model idgoogle/gemini-3.1-flash-litenvidia/nemotron-3.5-super-vl

Publisher Google AI Studio NVIDIA

AI IQ IQ 104#102 IQ 105#100

Context1,048,576 tokens1,048,576 tokens

Credits provider routes44

Cheapest route$1.84625/1M$0.5275/1M

Cached input$0.026375/1M$0.0211/1M to $0.089675/1M

Privacy postureprovider posture varieshas ZDR route

Fastest measured p50 TTFT1411 ms via google-ai-studio1400 ms via io-net

Highest measured throughputnot enough datanot enough data

Recent route uptime range100.00%50.00% to 100.00%

Modes chat decide chat

Google: Gemini 3.1 Flash Lite routes

Overview4 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
Google: Gemini 3.1 Flash Lite vs Z.ai: GLM 5.3 Flash36 combined provider routes
NVIDIA: Nemotron 3.5 Super VL vs Z.ai: GLM 5.3 Flash36 combined provider routes
Google: Gemini 3.1 Flash Lite vs MoonshotAI: Kimi K332 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.trustedrouter.com/v1",
    api_key="sk-tr-v1-..."
)

response = client.chat.completions.create(
    model="google/gemini-3.1-flash-lite",
    messages=messages,
)

Questions

Which should I use, Google: Gemini 3.1 Flash Lite or NVIDIA: Nemotron 3.5 Super VL?

Is Google: Gemini 3.1 Flash Lite or NVIDIA: Nemotron 3.5 Super VL cheaper?

The current cheapest TrustedRouter route is $1.84625/1M for Google: Gemini 3.1 Flash Lite 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: Gemini 3.1 Flash Lite or NVIDIA: Nemotron 3.5 Super VL faster?

Current measured p50 time to first token is 1411 ms for Google: Gemini 3.1 Flash Lite and 1400 ms 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: Gemini 3.1 Flash Lite 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/gemini-3.1-flash-lite and nvidia/nemotron-3.5-super-vl. This makes side-by-side evals possible without maintaining two provider integrations.