OpenAI compatible API · Attested · Public status

Meta: Llama 3.3 70B Instruct vs Qwen: Qwen3.5-122B-A10B

Compare Meta: Llama 3.3 70B Instruct and Qwen: Qwen3.5-122B-A10B by providers, context, price, and TrustedRouter route support.

Verify gateway

Onebase URL to migrate

100sof models and routes

Noneprompt 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: Qwen3.5-122B-A10B has the larger context window. Current TrustedRouter probes show Meta: Llama 3.3 70B Instruct with the lower p50 TTFT.

$0.526575/1MMeta: Llama 3.3 70B Instruct cheapest route

$2.457/1MQwen: Qwen3.5-122B-A10B cheapest route

2217 msMeta: Llama 3.3 70B Instruct measured p50 TTFT

2274 msQwen: Qwen3.5-122B-A10B measured p50 TTFT

Meta: Llama 3.3 70B InstructQwen: Qwen3.5-122B-A10B

Model idmeta-llama/llama-3.3-70b-instructqwen/qwen3.5-122b-a10b

PublisherCerebrasDeepInfra

AI IQ — —

Context131,072 tokens262,144 tokens

Provider routes148

Cheapest route$0.526575/1M$2.457/1M

Privacy posturehas provider E2EE routeprovider posture varies

Modes chat chat

Meta: Llama 3.3 70B Instruct routes

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

Qwen: Qwen3.5-122B-A10B routes

Overview8 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://api.trustedrouter.com/v1",
    api_key="sk-tr-v1-..."
)

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