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Meta: Llama 4 Maverick vs Qwen: Qwen3.5 397B A17B

Try both on us. Starter credit covers your first requests, then one key routes Llama 4 Maverick and Qwen3.5 397B A17B. 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 4 Maverick has the lower published input-plus-output rate. Qwen: Qwen3.5 397B A17B has more Credits provider routes and the larger context window. There is not enough recent probe data to compare speed.

$1.1816/1MMeta: Llama 4 Maverick cheapest route

$3.63975/1MQwen: Qwen3.5 397B A17B cheapest route

not enough dataMeta: Llama 4 Maverick fastest measured p50 TTFT

not enough dataQwen: Qwen3.5 397B A17B 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 4 MaverickQwen: Qwen3.5 397B A17B

Model idmeta-llama/llama-4-maverickqwen/qwen3.5-397b-a17b

Publisher Meta Alibaba Cloud Model Studio

AI IQ IQ 87#160 —

Context128,000 tokens262,144 tokens

Credits provider routes311

Cheapest route$1.1816/1M$3.63975/1M

Cached input$0.05275/1M to $0.17935/1M$0.047475/1M to $0.58025/1M

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

Meta: Llama 4 Maverick routes

Overview3 Credits routes
PricingInput, cached input, and output rates
ProvidersAll serving providers

Qwen: Qwen3.5 397B A17B routes

Overview11 Credits routes
PricingInput, cached input, and output rates
ProvidersAll serving providers

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DeepSeek: DeepSeek V4 Flash 0731 vs Qwen: Qwen3.5 397B A17B37 combined provider routes
Meta: Llama 4 Maverick vs Z.ai: GLM 5.336 combined provider routes
DeepSeek: DeepSeek V4.1 Flash vs Qwen: Qwen3.5 397B A17B35 combined provider routes
Meta: Llama 4 Maverick vs Z.ai: GLM 5.235 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-4-maverick",
    messages=messages,
)

Questions

Which should I use, Meta: Llama 4 Maverick or Qwen: Qwen3.5 397B A17B?

Is Meta: Llama 4 Maverick or Qwen: Qwen3.5 397B A17B cheaper?

The current cheapest TrustedRouter route is $1.1816/1M for Meta: Llama 4 Maverick and $3.63975/1M for Qwen: Qwen3.5 397B A17B. The comparison uses current catalog prices and updates as provider pricing changes.

Is Meta: Llama 4 Maverick or Qwen: Qwen3.5 397B A17B faster?

Current measured p50 time to first token is not enough data for Meta: Llama 4 Maverick and not enough data for Qwen: Qwen3.5 397B A17B. These are routed probe measurements, not vendor-advertised speeds, and update as new samples arrive.

Can I test Meta: Llama 4 Maverick and Qwen: Qwen3.5 397B A17B 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-4-maverick and qwen/qwen3.5-397b-a17b. This makes side-by-side evals possible without maintaining two provider integrations.