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Google: Gemma 4 26B A4B vs NVIDIA: Nemotron 3.5 Super VL

Try both on us. Starter credit covers your first requests, then one key routes Gemma 4 26B A4B 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 4 26B A4B has the lower published input-plus-output rate, more Credits provider routes and the lower measured p50 time to first token. NVIDIA: Nemotron 3.5 Super VL has the larger context window.

$0.385075/1MGoogle: Gemma 4 26B A4B cheapest route

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

938 msGoogle: Gemma 4 26B A4B fastest measured p50 TTFT via io-net

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: Gemma 4 26B A4B NVIDIA: Nemotron 3.5 Super VL

Model idgoogle/gemma-4-26b-a4b-itnvidia/nemotron-3.5-super-vl

Publisher Google AI Studio NVIDIA

AI IQ IQ 99#131 IQ 105#100

Context262,144 tokens1,048,576 tokens

Credits provider routes114

Cheapest route$0.385075/1M$0.5275/1M

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

Privacy posturehas ZDR routehas ZDR route

Fastest measured p50 TTFT938 ms via io-net1400 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: Gemma 4 26B A4B routes

Overview11 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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DeepSeek: DeepSeek V4 Flash 0731 vs Google: Gemma 4 26B A4B 37 combined provider routes
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
NVIDIA: Nemotron 3.5 Super VL vs Z.ai: GLM 5.3 Flash36 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/gemma-4-26b-a4b-it",
    messages=messages,
)

Questions

Which should I use, Google: Gemma 4 26B A4B or NVIDIA: Nemotron 3.5 Super VL?

Is Google: Gemma 4 26B A4B or NVIDIA: Nemotron 3.5 Super VL cheaper?

The current cheapest TrustedRouter route is $0.385075/1M for Google: Gemma 4 26B A4B 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 4 26B A4B or NVIDIA: Nemotron 3.5 Super VL faster?

Current measured p50 time to first token is 938 ms for Google: Gemma 4 26B A4B 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: Gemma 4 26B A4B 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-4-26b-a4b-it and nvidia/nemotron-3.5-super-vl. This makes side-by-side evals possible without maintaining two provider integrations.