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Meta: Muse Spark 1.1 vs MoonshotAI: Kimi K3

Try both on us. Starter credit covers your first requests, then one key routes Muse Spark 1.1 and Kimi K3. 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

Muse Spark 1.1 has the lower published input-plus-output rate. MoonshotAI: Kimi K3 has more Credits provider routes and the lower measured p50 time to first token. Their context windows are the same size.

$5.8025/1MMeta: Muse Spark 1.1 cheapest route

$11.97425/1MMoonshotAI: Kimi K3 cheapest route

1966 msMeta: Muse Spark 1.1 fastest measured p50 TTFT via meta

980 msMoonshotAI: Kimi K3 fastest measured p50 TTFT via lyceum

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: Muse Spark 1.1MoonshotAI: Kimi K3

Model idmeta/muse-spark-1.1moonshotai/kimi-k3

Publisher Meta Kimi

AI IQ IQ 119#47 IQ 127#21

Context1,048,576 tokens1,048,576 tokens

Credits provider routes128

Cheapest route$5.8025/1M$11.97425/1M

Cached input$0.15825/1M$0.195175/1M to $1.897945/1M

Privacy postureprovider posture varieshas Confidential route (E2EE + ZDR)

Fastest measured p50 TTFT1966 ms via meta980 ms via lyceum

Highest measured throughputnot enough data172 tok/s via sail-research

Recent route uptime range100.00%0.00% to 100.00%

Modes chat chat

Meta: Muse Spark 1.1 routes

Overview1 Credits routes

MoonshotAI: Kimi K3 routes

Overview28 Credits routes
PricingInput, cached input, and output rates
ProvidersAll serving providers

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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/muse-spark-1.1",
    messages=messages,
)

Questions

Which should I use, Meta: Muse Spark 1.1 or MoonshotAI: Kimi K3?

Is Meta: Muse Spark 1.1 or MoonshotAI: Kimi K3 cheaper?

The current cheapest TrustedRouter route is $5.8025/1M for Meta: Muse Spark 1.1 and $11.97425/1M for MoonshotAI: Kimi K3. The comparison uses current catalog prices and updates as provider pricing changes.

Is Meta: Muse Spark 1.1 or MoonshotAI: Kimi K3 faster?

Current measured p50 time to first token is 1966 ms for Meta: Muse Spark 1.1 and 980 ms for MoonshotAI: Kimi K3. These are routed probe measurements, not vendor-advertised speeds, and update as new samples arrive.

Can I test Meta: Muse Spark 1.1 and MoonshotAI: Kimi K3 with the same API?

Yes. Use the same OpenAI-compatible TrustedRouter base URL and API key, then change only the model id between meta/muse-spark-1.1 and moonshotai/kimi-k3. This makes side-by-side evals possible without maintaining two provider integrations.