OpenAI compatible API. Attested gateway. Public status.

Meta: Llama 3.3 70B Instruct vs Qwen: Qwen3.5-35B-A3B

Compare Meta: Llama 3.3 70B Instruct and Qwen: Qwen3.5-35B-A3B by providers, context, price, and TrustedRouter route support.

Verify gateway

1 URLbase_url migration

100smodels and routes

0prompt logs by default

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: Qwen3.5-35B-A3B has the larger context window. Probe-backed speed data is shown when enough recent samples exist.

$0.5885/1MMeta: Llama 3.3 70B Instruct cheapest route

$1.265/1MQwen: Qwen3.5-35B-A3B cheapest route

4579 msMeta: Llama 3.3 70B Instruct measured p50 TTFT

not enough dataQwen: Qwen3.5-35B-A3B measured p50 TTFT

Meta: Llama 3.3 70B InstructQwen: Qwen3.5-35B-A3B

Model idmeta-llama/llama-3.3-70b-instructqwen/qwen3.5-35b-a3b

PublisherCerebrasParasail

AI IQ — —

Context131,072 tokens262,144 tokens

Provider routes154

Cheapest route$0.5885/1M$1.265/1M

Privacy posturehas provider E2EE routeprovider posture varies

Modes chat chat

Meta: Llama 3.3 70B Instruct routes

Overview15 endpoints
PricingPrompt and completion rates
BenchmarksTrustedRouter and external sources
ProvidersAll serving providers

Qwen: Qwen3.5-35B-A3B routes

Overview4 endpoints
PricingPrompt and completion rates
BenchmarksTrustedRouter and external sources
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://api.trustedrouter.com/v1",
    api_key="sk-tr-v1-..."
)

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