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Meta: Muse Spark 1.3 vs Z.ai: GLM 5.2

Try both on us. Starter credit covers your first requests, then one key routes Muse Spark 1.3 and GLM 5.2. 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

Z.ai: GLM 5.2 has the lower published input-plus-output rate, 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 1.3 cheapest route

$2.2999/1MZ.ai: GLM 5.2 cheapest route

2166 msMeta: Muse Spark 1.3 fastest measured p50 TTFT via meta

898 msZ.ai: GLM 5.2 fastest measured p50 TTFT via parasail

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 1.3Z.ai: GLM 5.2

Model idmeta/muse-spark-1.3z-ai/glm-5.2

Publisher Meta Z.AI

AI IQ IQ 127#23 IQ 118#53

Context1,048,576 tokens1,048,576 tokens

Credits provider routes132

Cheapest route$5.8025/1M$2.2999/1M

Cached input$0.15825/1M$0.131875/1M to $0.84611/1M

Privacy postureprovider posture varieshas Confidential route (E2EE + ZDR)

Fastest measured p50 TTFT2166 ms via meta898 ms via parasail

Highest measured throughput175 tok/s via meta211 tok/s via together

Recent route uptime range100.00%71.43% to 100.00%

Modes chat chat

Meta: Muse Spark 1.3 routes

Overview1 Credits routes

Z.ai: GLM 5.2 routes

Overview32 Credits routes
PricingInput, cached input, and output rates
BenchmarksTrustedRouter and external sources
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-1.3",
    messages=messages,
)

Questions

Which should I use, Meta: Muse Spark 1.3 or Z.ai: GLM 5.2?

Z.ai: GLM 5.2 has the lower published input-plus-output rate, more Credits provider routes and the lower measured p50 time to first token. Their context windows are the same size.

Is Meta: Muse Spark 1.3 or Z.ai: GLM 5.2 cheaper?

The current cheapest TrustedRouter route is $5.8025/1M for Meta: Muse Spark 1.3 and $2.2999/1M for Z.ai: GLM 5.2. The comparison uses current catalog prices and updates as provider pricing changes.

Is Meta: Muse Spark 1.3 or Z.ai: GLM 5.2 faster?

Current measured p50 time to first token is 2166 ms for Meta: Muse Spark 1.3 and 898 ms for Z.ai: GLM 5.2. These are routed probe measurements, not vendor-advertised speeds, and update as new samples arrive.

Can I test Meta: Muse Spark 1.3 and Z.ai: GLM 5.2 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-1.3 and z-ai/glm-5.2. This makes side-by-side evals possible without maintaining two provider integrations.