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Meta: Llama 3.1 8B Instruct vs Qwen: Qwen3.5-9B

Try both on us. Starter credit covers your first requests, then one key routes Llama 3.1 8B Instruct and Qwen3.5-9B. 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. Qwen: Qwen3.5-9B has more Credits provider routes, the larger context window and the lower measured p50 time to first token.

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

$0.110775/1MQwen: Qwen3.5-9B cheapest route

865 msMeta: Llama 3.1 8B Instruct fastest measured p50 TTFT via telnyx

497 msQwen: Qwen3.5-9B fastest measured p50 TTFT via together

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 InstructQwen: Qwen3.5-9B

Model idmeta-llama/llama-3.1-8b-instructqwen/qwen3.5-9b

Publisher Meta Alibaba Cloud Model Studio

AI IQ — IQ 96#145

Context131,072 tokens262,144 tokens

Credits provider routes56

Cheapest route$0.07385/1M$0.110775/1M

Cached input$0.211/1M$0.0211/1M

Privacy posturehas ZDR routehas ZDR route

Fastest measured p50 TTFT865 ms via telnyx497 ms via together

Highest measured throughputnot enough datanot enough data

Recent route uptime range100.00%100.00%

Modes chat chat

Meta: Llama 3.1 8B Instruct routes

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

Qwen: Qwen3.5-9B routes

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

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Meta: Llama 3.1 8B Instruct vs Z.ai: GLM 5.338 combined provider routes
Qwen: Qwen3.5-9B vs Z.ai: GLM 5.238 combined provider routes
Qwen: Qwen3.5-9B vs Z.ai: GLM 5.3 Flash38 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
MoonshotAI: Kimi K3 vs Qwen: Qwen3.5-9B34 combined provider routes
Meta: Llama 3.1 8B Instruct vs MoonshotAI: Kimi K333 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 Qwen: Qwen3.5-9B?

Is Meta: Llama 3.1 8B Instruct or Qwen: Qwen3.5-9B cheaper?

The current cheapest TrustedRouter route is $0.07385/1M for Meta: Llama 3.1 8B Instruct and $0.110775/1M for Qwen: Qwen3.5-9B. The comparison uses current catalog prices and updates as provider pricing changes.

Is Meta: Llama 3.1 8B Instruct or Qwen: Qwen3.5-9B faster?

Current measured p50 time to first token is 865 ms for Meta: Llama 3.1 8B Instruct and 497 ms for Qwen: Qwen3.5-9B. 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 Qwen: Qwen3.5-9B 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 qwen/qwen3.5-9b. This makes side-by-side evals possible without maintaining two provider integrations.