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Meta: Llama 3.1 8B Instruct vs NVIDIA: Nemotron 3 Super

Try both on us. Starter credit covers your first requests, then one key routes Llama 3.1 8B Instruct and Nemotron 3 Super. 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

Meta: Llama 3.1 8B Instruct has the lower published input-plus-output rate. NVIDIA: Nemotron 3 Super has the larger context window. Both have 5 Credits provider routes. There is not enough recent probe data to compare speed.

$0.07385/1MMeta: Llama 3.1 8B Instruct cheapest route

$0.511675/1MNVIDIA: Nemotron 3 Super cheapest route

1319 msMeta: Llama 3.1 8B Instruct fastest measured p50 TTFT via wandb

not enough dataNVIDIA: Nemotron 3 Super 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.

Meta: Llama 3.1 8B InstructNVIDIA: Nemotron 3 Super

Model idmeta-llama/llama-3.1-8b-instructnvidia/nemotron-3-super-120b-a12b

Publisher Meta NVIDIA

AI IQ — —

Context131,072 tokens262,144 tokens

Credits provider routes55

Cheapest route$0.07385/1M$0.511675/1M

Cached input$0.211/1M$0.15825/1M

Privacy posturehas ZDR routehas ZDR route

Fastest measured p50 TTFT1319 ms via wandbnot enough data

Highest measured throughputnot enough datanot enough data

Recent route uptime range0.00% to 100.00%100.00%

Modes chat chat

Meta: Llama 3.1 8B Instruct routes

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

NVIDIA: Nemotron 3 Super routes

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

Related comparisons

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Meta: Llama 3.1 8B Instruct vs Z.ai: GLM 5.338 combined provider routes
NVIDIA: Nemotron 3 Super vs Z.ai: GLM 5.338 combined provider routes
Meta: Llama 3.1 8B Instruct vs Z.ai: GLM 5.237 combined provider routes
Meta: Llama 3.1 8B Instruct vs Z.ai: GLM 5.3 Flash37 combined provider routes
NVIDIA: Nemotron 3 Super vs Z.ai: GLM 5.237 combined provider routes
NVIDIA: Nemotron 3 Super vs Z.ai: GLM 5.3 Flash37 combined provider routes
Meta: Llama 3.1 8B Instruct vs MoonshotAI: Kimi K333 combined provider routes
MoonshotAI: Kimi K3 vs NVIDIA: Nemotron 3 Super33 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="meta-llama/llama-3.1-8b-instruct",
    messages=messages,
)

Questions

Which should I use, Meta: Llama 3.1 8B Instruct or NVIDIA: Nemotron 3 Super?

Is Meta: Llama 3.1 8B Instruct or NVIDIA: Nemotron 3 Super cheaper?

The current cheapest TrustedRouter route is $0.07385/1M for Meta: Llama 3.1 8B Instruct and $0.511675/1M for NVIDIA: Nemotron 3 Super. The comparison uses current catalog prices and updates as provider pricing changes.

Is Meta: Llama 3.1 8B Instruct or NVIDIA: Nemotron 3 Super faster?

Current measured p50 time to first token is 1319 ms for Meta: Llama 3.1 8B Instruct and not enough data for NVIDIA: Nemotron 3 Super. These are routed probe measurements, not vendor-advertised speeds, and update as new samples arrive.

Can I test Meta: Llama 3.1 8B Instruct and NVIDIA: Nemotron 3 Super with the same API?

Yes. Use the same OpenAI-compatible TrustedRouter base URL and API key, then change only the model id between meta-llama/llama-3.1-8b-instruct and nvidia/nemotron-3-super-120b-a12b. This makes side-by-side evals possible without maintaining two provider integrations.