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DeepSeek: DeepSeek V4.1 Flash vs Meta: Muse Spark 1.3

Try both on us. Starter credit covers your first requests, then one key routes DeepSeek V4.1 Flash and Muse Spark 1.3. 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

DeepSeek V4.1 Flash has the lower published input-plus-output rate and more Credits provider routes. Their context windows are the same size. There is not enough recent probe data to compare speed.

$0.1266/1MDeepSeek: DeepSeek V4.1 Flash cheapest route

$5.8025/1MMeta: Muse Spark 1.3 cheapest route

not enough dataDeepSeek: DeepSeek V4.1 Flash fastest measured p50 TTFT

not enough dataMeta: Muse Spark 1.3 fastest measured p50 TTFT

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.

DeepSeek: DeepSeek V4.1 FlashMeta: Muse Spark 1.3

Model iddeepseek/deepseek-v4.1-flashmeta/muse-spark-1.3

Publisher DeepSeek Meta

AI IQ IQ 126#22 IQ 128#17

Context1,048,576 tokens1,048,576 tokens

Credits provider routes241

Cheapest route$0.1266/1M$5.8025/1M

Cached input$0.01/1M to $0.1899/1M$0.15825/1M

Privacy posturehas Confidential route (E2EE + ZDR)provider posture varies

Fastest measured p50 TTFTnot enough datanot enough data

Highest measured throughputnot enough datanot enough data

Recent route uptime rangenot enough datanot enough data

Modes chat decide chat

DeepSeek: DeepSeek V4.1 Flash routes

Overview24 Credits routes
PricingInput, cached input, and output rates
ProvidersAll serving providers

Meta: Muse Spark 1.3 routes

Overview1 Credits routes

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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-azure.trustedrouter.com/v1",
    api_key="sk-tr-v1-..."
)

response = client.chat.completions.create(
    model="deepseek/deepseek-v4.1-flash",
    messages=messages,
)

Questions

Which should I use, DeepSeek: DeepSeek V4.1 Flash or Meta: Muse Spark 1.3?

DeepSeek V4.1 Flash has the lower published input-plus-output rate and more Credits provider routes. Their context windows are the same size. There is not enough recent probe data to compare speed.

Is DeepSeek: DeepSeek V4.1 Flash or Meta: Muse Spark 1.3 cheaper?

The current cheapest TrustedRouter route is $0.1266/1M for DeepSeek: DeepSeek V4.1 Flash and $5.8025/1M for Meta: Muse Spark 1.3. The comparison uses current catalog prices and updates as provider pricing changes.

Is DeepSeek: DeepSeek V4.1 Flash or Meta: Muse Spark 1.3 faster?

Current measured p50 time to first token is not enough data for DeepSeek: DeepSeek V4.1 Flash and not enough data for Meta: Muse Spark 1.3. These are routed probe measurements, not vendor-advertised speeds, and update as new samples arrive.

Can I test DeepSeek: DeepSeek V4.1 Flash and Meta: Muse Spark 1.3 with the same API?

Yes. Use the same OpenAI-compatible TrustedRouter base URL and API key, then change only the model id between deepseek/deepseek-v4.1-flash and meta/muse-spark-1.3. This makes side-by-side evals possible without maintaining two provider integrations.