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xAI vs Wafer
A closed lab selling Grok against a young startup running tuned open models. Grok for live data and closed quality; Wafer for fast open weights on a flat weekly pass.
By The Subconscious Team · Updated
xAI vs Wafer: key differences
Wafer sells speed on open models that it tunes with its own agents. Those agents test configurations across batching, decoding, quantization, kernels and hardware, on NVIDIA or AMD, and keep re-tuning as traffic changes. 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. Its Wafer Pass, from $10 a week, covers every hosted model and plugs into Claude Code, Cline and OpenHands. xAI sells only Grok, per token, with native X Search.
For coding agents, the comparison is Wafer's flat rate against Grok's per-token bill, which is low on output ($6 per million on Grok 4.6) but doubles past 200K prompt tokens. Heavy coding sessions with long contexts may cost less on a flat pass. Wafer is very young with a small catalog, and its speedups are self-reported against stock baselines. xAI offers a coding model in grok-build, 1M context on Grok 4.20, and data no open model has. Teams with a latency SLO and no kernel staff can also buy a dedicated Wafer deployment.
What xAI and Wafer do
xAI
xAI sells the Grok models through its own API. Grok 4.6 is the current flagship and xAI tells developers to use it for everything outside audio, image and video, code included. It has a 500K context window and costs $2 in and $6 out per million tokens under 200K prompt tokens. Older Grok 4.20 and 4.3 models keep a 1M window at $1.25 in and $2.50 out, which is aggressive for that capability class.
Example models: Grok 4.6, Grok 4.20
Full xAI 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 xAI or Wafer?
xAI vs Wafer at a glance
| Attribute | xAI | Wafer |
|---|---|---|
| Model access | Closed | Open weights |
| Flagship models | Grok 4.6, Grok 4.20, grok-build | Qwen 3.5 397B Turbo, GLM 5.1 Turbo |
| Speed | ~54 tok/s on Grok 4.6 | 2–2.8x vs stock vLLM or SGLang |
| Price | $2 in, $6 out (Grok 4.6); 2x past 200K | Wafer Pass from $10 a week |
| Customization | Unknown | Agent-tuned dedicated deployments |
| Deployment | First-party API | Serverless pass, dedicated |
| Long context | 500K (4.6), 1M (4.20, 4.3) | Varies by model |
Frequently asked questions
What is the difference between xAI and Wafer?
A closed lab selling Grok against a young startup running tuned open models. Grok for live data and closed quality; Wafer for fast open weights on a flat weekly pass.
When should I choose xAI over Wafer?
Closed models with X and web search built in; Per-token billing for moderate, bursty usage; 1M context on Grok 4.20.
When should I choose Wafer over xAI?
Flat-rate open models inside coding harnesses; Dedicated endpoints tuned to a latency target; Teams hedging across NVIDIA and AMD.
Is xAI or Wafer cheaper?
xAI: $2 in, $6 out (Grok 4.6); 2x past 200K. Wafer: Wafer Pass from $10 a week. The cheaper choice depends on the model and workload.
Which has more context, xAI or Wafer?
xAI: 500K (4.6), 1M (4.20, 4.3). 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.