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Meta vs Wafer
Meta sells its closed Muse Spark per token. Wafer sells agent-tuned open models on a flat weekly pass and dedicated deployments tuned to a latency target.
By The Subconscious Team · Updated
Meta vs Wafer: key differences
Wafer's product is a faster stack, not a model. Its agents tune batching, decoding, quantization, kernels and hardware for each deployment, on NVIDIA or AMD, and Wafer reports its Qwen 3.5 397B running 2.8x faster than stock SGLang and GLM 5.1 and DeepSeek V4 Pro 2x faster than a vLLM baseline. Wafer Pass, from $10 a week, covers every hosted model and drops into Claude Code, Cline and OpenHands. Meta sells one closed model family, with Muse Spark 1.3 at $1.25 in and $4.25 out and 1M context.
For coding agents, the question is flat rate versus per token, and closed versus open. Wafer's pass makes heavy use predictable, but the company is very young, its catalog is small and its speedups are self-reported against stock baselines. Meta's per-token pricing is mid-tier, with a Contributor tier at roughly 95% off for teams that accept data sharing, but the API is still a preview. Teams that want to self-host Meta's work can use Muse Glimmer, its open-weight model, on their own stack.
What Meta and Wafer 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 profileWafer
Wafer builds AI agents that act as GPU performance engineers, then sells inference on the stacks those agents tune. The company came out of Y Combinator's Summer 2025 batch as a "Cursor for CUDA" that turned slow PyTorch into custom kernels. Founders Emilio Andere and Steven Arellano are based in San Francisco. Its agents profile a workload, generate candidate configs across batching, decoding, quantization, engines, kernels and hardware, measure each one and deploy the winner.
Example models: Qwen 3.5 397B Turbo, GLM 5.1 Turbo
Full Wafer profileShould you choose Meta or Wafer?
Meta vs Wafer at a glance
| Attribute | Meta | Wafer |
|---|---|---|
| Model access | Closed API; open Muse Glimmer | Open weights |
| Flagship models | Muse Spark 1.3, Muse Glimmer | Qwen 3.5 397B Turbo, GLM 5.1 Turbo |
| Speed | ~145–233 tok/s on Muse Spark 1.3 | 2–2.8x vs stock vLLM or SGLang |
| Price | $1.25 in, $4.25 out; Contributor tier cheaper | Wafer Pass from $10 a week |
| Customization | Open Muse Glimmer weights to fine-tune | Agent-tuned dedicated deployments |
| Deployment | Meta Model API (preview) | Serverless pass, dedicated |
| Long context | 1M | Varies by model |
Frequently asked questions
What is the difference between Meta and Wafer?
Meta sells its closed Muse Spark per token. Wafer sells agent-tuned open models on a flat weekly pass and dedicated deployments tuned to a latency target.
When should I choose Meta over Wafer?
A closed agentic model with 1M context; Per-token billing for moderate use; Image and speech APIs on the same key.
When should I choose Wafer over Meta?
Flat-rate open models inside coding harnesses; Dedicated endpoints tuned to a latency SLO; Teams hedging across NVIDIA and AMD GPUs.
Is Meta or Wafer cheaper?
Meta: $1.25 in, $4.25 out; Contributor tier cheaper. Wafer: Wafer Pass from $10 a week. The cheaper choice depends on the model and workload.
Which has more context, Meta or Wafer?
Meta: 1M. Wafer: Varies by model.
Related comparisons
Running long-horizon agents?
If your agents run past 200K tokens, compare both against Subconscious. Our inference stack treats a long-horizon trace as the primary workload, so speed, cost, and accuracy hold up deep into the trace.