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Meta: Llama 3.1 8B Instruct vs Qwen: Qwen3.8 2.4T A95B

Try both on us. Starter credit covers your first requests, then one key routes Llama 3.1 8B Instruct and Qwen3.8 2.4T A95B. 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: Qwen3.8 2.4T A95B has more Credits provider routes and the larger context window. There is not enough recent probe data to compare speed.

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

$5.275/1MQwen: Qwen3.8 2.4T A95B cheapest route

not enough dataMeta: Llama 3.1 8B Instruct fastest measured p50 TTFT

not enough dataQwen: Qwen3.8 2.4T A95B 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 InstructQwen: Qwen3.8 2.4T A95B

Model idmeta-llama/llama-3.1-8b-instructqwen/qwen3.8-2.4t-a95b

Publisher Meta Alibaba Cloud Model Studio

AI IQ — IQ 123#32

Context131,072 tokens1,000,000 tokens

Credits provider routes56

Cheapest route$0.07385/1M$5.275/1M

Cached input$0.211/1M$0.26375/1M to $0.66465/1M

Privacy posturehas ZDR routehas 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 chat

Meta: Llama 3.1 8B Instruct routes

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

Qwen: Qwen3.8 2.4T A95B routes

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

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Qwen: Qwen3.8 2.4T A95B vs Z.ai: GLM 5.339 combined provider routes
Meta: Llama 3.1 8B Instruct vs Z.ai: GLM 5.338 combined provider routes
Qwen: Qwen3.8 2.4T A95B vs Z.ai: GLM 5.238 combined provider routes
Qwen: Qwen3.8 2.4T A95B 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: Qwen3.8 2.4T A95B34 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-azure.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: Qwen3.8 2.4T A95B?

Is Meta: Llama 3.1 8B Instruct or Qwen: Qwen3.8 2.4T A95B cheaper?

The current cheapest TrustedRouter route is $0.07385/1M for Meta: Llama 3.1 8B Instruct and $5.275/1M for Qwen: Qwen3.8 2.4T A95B. The comparison uses current catalog prices and updates as provider pricing changes.

Is Meta: Llama 3.1 8B Instruct or Qwen: Qwen3.8 2.4T A95B faster?

Current measured p50 time to first token is not enough data for Meta: Llama 3.1 8B Instruct and not enough data for Qwen: Qwen3.8 2.4T A95B. 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: Qwen3.8 2.4T A95B 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/qwen3.8-2.4t-a95b. This makes side-by-side evals possible without maintaining two provider integrations.