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Meta vs Wafer
Meta sells its closed Muse Spark per token. Wafer sells agent-tuned open models per token 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 Serverless bills per token 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 closed versus open. Wafer 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 new. 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 launched the Meta Model API with Muse Spark 1.1, a multimodal reasoning model aimed at agentic coding, tool use and computer use. At Meta Connect in September, Meta made the API generally available globally. Muse Spark 1.3 has a 1M token context and costs $1.25 in and $4.25 out per million tokens, with cached input at $0.15. Muse Wonder, released October 12, 2026, sits above it for longer, harder agent work at $4 in and $20 out, also with 1M context. The endpoint speaks OpenAI Chat Completions, Anthropic Messages and a stateful agentic format.
Example models: Muse Wonder, Muse Spark 1.3, Muse Spark Open
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. Wafer raised a $4M seed led by Fifty Years in April 2026 and a $40M Series A co-led by Marathon and Chemistry in September 2026 at a $220M post-money valuation, with AMD Ventures and Wing joining. 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: GLM-5.3, DeepSeek V4.1 Flash
Full Wafer profileShould you choose Meta or Wafer?
Meta vs Wafer at a glance
| Attribute | Meta | Wafer |
|---|---|---|
| Model access | Closed API; open Muse Spark Open and Glimmer | Open weights |
| Flagship models | Muse Wonder, Muse Spark 1.3, Muse Spark Open | GLM-5.3, DeepSeek V4.1 Flash |
| Speed | ~145–233 tok/s on Muse Spark 1.3 | ~168 tok/s on GLM-5.3; 2–2.8x vs stock vLLM or SGLang (vendor) |
| Price | $1.25–$4 in, $4.25–$20 out; Contributor tier cheaper | Per token, prepaid credits |
| Customization | Open Muse weights to fine-tune | Agent-tuned dedicated deployments |
| Deployment | Meta Model API, Oracle Cloud | Serverless, dedicated |
| Long context | 1M | Up to 1M (GLM-5.3, DeepSeek V4.1 Flash) |
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 per token 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?
Fast 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–$4 in, $4.25–$20 out; Contributor tier cheaper. Wafer: Per token, prepaid credits. The cheaper choice depends on the model and workload.
Which has more context, Meta or Wafer?
Meta: 1M. Wafer: Up to 1M (GLM-5.3, DeepSeek V4.1 Flash).
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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.