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Meta vs StreamLake
Two big internet companies selling closed models for coding. Meta offers Muse Spark with 1M context on a US preview API; StreamLake offers Kuaishou's KAT-Coder, mainly for China.
By The Subconscious Team · Updated
Meta vs StreamLake: key differences
Both come from consumer internet giants and both aim models at agentic coding. Meta's Muse Spark 1.3 is a general multimodal reasoning model for coding, tool use and computer use, with 1M context at $1.25 in and $4.25 out, and endpoints for OpenAI, Anthropic and a stateful agentic format. StreamLake, Kuaishou's AI cloud, sells KAT-Coder-Pro V2.5, a proprietary coding model that StreamLake says was trained with large-scale agentic reinforcement learning for repository work, per token or through a KwaiKAT Coding Plan with a Claude-protocol proxy for Claude Code.
Region is likely the deciding factor. StreamLake's pricing and documentation lead with China and yuan, and its data residency in China rules it out for many US and EU buyers. It also sells bare-metal compute to Chinese internet businesses. Meta's API is still in preview with a short track record, and its cheap Contributor tier requires sharing data. Western teams wanting a general agentic model pick Meta. Developers after a coding subscription, or businesses inside China, look at StreamLake.
What Meta and StreamLake do
Meta
Meta has moved from open Llama releases toward its own closed API. Meta Superintelligence Labs builds the Muse family, and in July 2026 Meta opened a public preview of the Meta Model API with Muse Spark 1.1, a multimodal reasoning model aimed at agentic coding, tool use and computer use. The current lineup runs through Muse Spark 1.3 with a 1M token context. Standard pricing is $1.25 in and $4.25 out per million tokens, with cached input at $0.15, and the endpoint speaks OpenAI Chat Completions, Anthropic Messages and a stateful agentic format.
Example models: Muse Spark 1.3, Muse Glimmer
Full Meta profileStreamLake
StreamLake is the AI cloud brand of Kuaishou, the Chinese short-video company behind the Kling video models. It sells model-as-a-service inference and bare-metal compute to internet businesses, drawing on the infrastructure Kuaishou built to serve video at massive scale. Its developer site offers APIs, SDKs and integration guides aimed at taking teams from testing to production.
Example models: KAT-Coder-Pro V2.5, KAT-Coder-Air
Full StreamLake profileShould you choose Meta or StreamLake?
Meta
Choose Meta for
- A general agentic model with 1M context
- Western teams that avoid China data residency
- Media and transcription on the same key
StreamLake
Choose StreamLake for
- Subscription pricing for agentic coding
- Running KAT-Coder inside Claude Code
- Chinese businesses that want domestic MaaS
Meta vs StreamLake at a glance
| Attribute | Meta | StreamLake |
|---|---|---|
| Model access | Closed API; open Muse Glimmer | Proprietary coding models |
| Flagship models | Muse Spark 1.3, Muse Glimmer | KAT-Coder-Pro V2.5, KAT-Coder-Air |
| Speed | ~145–233 tok/s on Muse Spark 1.3 | Unknown |
| Price | $1.25 in, $4.25 out; Contributor tier cheaper | Per token or KwaiKAT Coding Plan |
| Customization | Open Muse Glimmer weights to fine-tune | Unknown |
| Deployment | Meta Model API (preview) | MaaS API, bare metal |
| Long context | 1M | Unknown |
Frequently asked questions
What is the difference between Meta and StreamLake?
Two big internet companies selling closed models for coding. Meta offers Muse Spark with 1M context on a US preview API; StreamLake offers Kuaishou's KAT-Coder, mainly for China.
When should I choose Meta over StreamLake?
A general agentic model with 1M context; Western teams that avoid China data residency; Media and transcription on the same key.
When should I choose StreamLake over Meta?
Subscription pricing for agentic coding; Running KAT-Coder inside Claude Code; Chinese businesses that want domestic MaaS.
Is Meta or StreamLake cheaper?
Meta: $1.25 in, $4.25 out; Contributor tier cheaper. StreamLake: Per token or KwaiKAT Coding Plan. The cheaper choice depends on the model and workload.
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