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Meta: Llama 3.3 70B Instruct vs Qwen: Qwen3.5-122B-A10B

Try both on us. Starter credit covers your first requests, then one key routes Llama 3.3 70B Instruct and Qwen3.5-122B-A10B. 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 3.3 70B Instruct has the lower published input-plus-output rate and more Credits provider routes. Qwen: Qwen3.5-122B-A10B has the larger context window. There is not enough recent probe data to compare speed.

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

$2.4687/1MQwen: Qwen3.5-122B-A10B cheapest route

not enough dataMeta: Llama 3.3 70B Instruct fastest measured p50 TTFT

not enough dataQwen: Qwen3.5-122B-A10B 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 3.3 70B InstructQwen: Qwen3.5-122B-A10B

Model idmeta-llama/llama-3.3-70b-instructqwen/qwen3.5-122b-a10b

Publisher Meta Alibaba Cloud Model Studio

AI IQ — —

Context131,072 tokens262,144 tokens

Credits provider routes106

Cheapest route$0.564425/1M$2.4687/1M

Cached input$0.1055/1M to $0.633/1M$0.0422/1M to $0.3165/1M

Privacy posturehas Confidential route (E2EE + ZDR)has 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 decide chat

Meta: Llama 3.3 70B Instruct routes

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

Qwen: Qwen3.5-122B-A10B routes

Overview6 Credits routes
PricingInput, cached input, and output rates
ProvidersAll serving providers

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Meta: Llama 3.3 70B Instruct vs Z.ai: GLM 5.343 combined provider routes
Meta: Llama 3.3 70B Instruct vs Z.ai: GLM 5.242 combined provider routes
Meta: Llama 3.3 70B Instruct vs Z.ai: GLM 5.3 Flash42 combined provider routes
Qwen: Qwen3.5-122B-A10B vs Z.ai: GLM 5.339 combined provider routes
Meta: Llama 3.3 70B Instruct vs MoonshotAI: Kimi K338 combined provider routes
Qwen: Qwen3.5-122B-A10B vs Z.ai: GLM 5.238 combined provider routes
Qwen: Qwen3.5-122B-A10B vs Z.ai: GLM 5.3 Flash38 combined provider routes
DeepSeek: DeepSeek V4 Flash 0731 vs Meta: Llama 3.3 70B Instruct36 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-azure.trustedrouter.com/v1",
    api_key="sk-tr-v1-..."
)

response = client.chat.completions.create(
    model="meta-llama/llama-3.3-70b-instruct",
    messages=messages,
)

Questions

Which should I use, Meta: Llama 3.3 70B Instruct or Qwen: Qwen3.5-122B-A10B?

Is Meta: Llama 3.3 70B Instruct or Qwen: Qwen3.5-122B-A10B cheaper?

The current cheapest TrustedRouter route is $0.564425/1M for Meta: Llama 3.3 70B Instruct and $2.4687/1M for Qwen: Qwen3.5-122B-A10B. The comparison uses current catalog prices and updates as provider pricing changes.

Is Meta: Llama 3.3 70B Instruct or Qwen: Qwen3.5-122B-A10B faster?

Current measured p50 time to first token is not enough data for Meta: Llama 3.3 70B Instruct and not enough data for Qwen: Qwen3.5-122B-A10B. These are routed probe measurements, not vendor-advertised speeds, and update as new samples arrive.

Can I test Meta: Llama 3.3 70B Instruct and Qwen: Qwen3.5-122B-A10B 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-3.3-70b-instruct and qwen/qwen3.5-122b-a10b. This makes side-by-side evals possible without maintaining two provider integrations.