OpenAI compatible API · Attested · Public status

Meta: Llama 3.3 70B Instruct vs Qwen: Qwen3 Coder Next

Compare Meta: Llama 3.3 70B Instruct and Qwen: Qwen3 Coder Next by providers, context, price, and TrustedRouter route support.

Verify gateway

Onebase URL to migrate

100sof models and routes

0prompt or output logs. Always.

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

Meta: Llama 3.3 70B Instruct has the lower cheapest prompt+completion route on TrustedRouter. Meta: Llama 3.3 70B Instruct has more provider fallback routes, while Qwen: Qwen3 Coder Next has the larger context window. Probe-backed speed data is shown when enough recent samples exist.

$0.56175/1MMeta: Llama 3.3 70B Instruct cheapest route

$0.966/1MQwen: Qwen3 Coder Next cheapest route

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

not enough dataQwen: Qwen3 Coder Next measured p50 TTFT

Meta: Llama 3.3 70B InstructQwen: Qwen3 Coder Next

Model idmeta-llama/llama-3.3-70b-instructqwen/qwen3-coder-next

PublisherCerebrasNovita AI

AI IQ — —

Context131,072 tokens262,144 tokens

Provider routes145

Cheapest route$0.56175/1M$0.966/1M

Privacy posturehas provider E2EE routehas ZDR route

Modes chat chat

Meta: Llama 3.3 70B Instruct routes

Overview14 endpoints
PricingPrompt and completion rates
BenchmarksTrustedRouter and external sources
ProvidersAll serving providers

Qwen: Qwen3 Coder Next routes

Overview5 endpoints
PricingPrompt and completion rates
ProvidersAll serving providers

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://gchircrcif.eu-west-3.awsapprunner.com/v1",
    api_key="sk-tr-v1-..."
)

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