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Google: Gemma 3 12B vs Meta: Llama 3.3 70B Instruct

Try both on us. Starter credit covers your first requests, then one key routes Gemma 3 12B and Llama 3.3 70B Instruct. 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. Meta: Llama 3.3 70B Instruct has more Credits provider routes. Their context windows are the same size. There is not enough recent probe data to compare speed.

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

$0.564425/1MMeta: Llama 3.3 70B Instruct cheapest route

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

not enough dataMeta: Llama 3.3 70B Instruct 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 12BMeta: Llama 3.3 70B Instruct

Model idgoogle/gemma-3-12b-itmeta-llama/llama-3.3-70b-instruct

Publisher Google AI Studio Meta

AI IQ — —

Context131,072 tokens131,072 tokens

Credits provider routes410

Cheapest route$0.15825/1M$0.564425/1M

Cached input$0.05275/1M$0.1055/1M to $0.633/1M

Privacy posturehas ZDR routehas Confidential route (E2EE + ZDR)

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 decide

Google: Gemma 3 12B routes

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

Meta: Llama 3.3 70B Instruct routes

Overview10 Credits routes
PricingInput, cached input, and output rates
BenchmarksTrustedRouter and external sources
ProvidersAll serving providers

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Meta: Llama 3.3 70B Instruct vs MoonshotAI: Kimi K338 combined provider routes
Google: Gemma 3 12B vs Z.ai: GLM 5.337 combined provider routes
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Google: Gemma 3 12B vs Z.ai: GLM 5.236 combined provider routes
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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-azure.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 Meta: Llama 3.3 70B Instruct?

Is Google: Gemma 3 12B or Meta: Llama 3.3 70B Instruct cheaper?

The current cheapest TrustedRouter route is $0.15825/1M for Google: Gemma 3 12B and $0.564425/1M for Meta: Llama 3.3 70B Instruct. The comparison uses current catalog prices and updates as provider pricing changes.

Is Google: Gemma 3 12B or Meta: Llama 3.3 70B Instruct faster?

Current measured p50 time to first token is not enough data for Google: Gemma 3 12B and not enough data for Meta: Llama 3.3 70B Instruct. These are routed probe measurements, not vendor-advertised speeds, and update as new samples arrive.

Can I test Google: Gemma 3 12B and Meta: Llama 3.3 70B Instruct 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 meta-llama/llama-3.3-70b-instruct. This makes side-by-side evals possible without maintaining two provider integrations.