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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 tuned stacks.
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 serverless API bills per token and plugs into Claude Code, Cline and OpenHands. xAI sells only Grok, per token, with native X Search.
For coding agents, both bill per token, and Grok's bill is low on output ($6 per million on Grok 4.7) but doubles past 200K prompt tokens. 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 and Amazon Bedrock. SpaceX acquired it in February 2026; it became SpaceXAI in July and SpaceX Super Intelligence (SpaceXSI) in October, the name its docs now use. Grok 4.7, released September 21, 2026, is the current flagship, pitched for coding, agentic tasks and knowledge work. 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 4.7, 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. 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 xAI or Wafer?
xAI vs Wafer at a glance
| Attribute | xAI | Wafer |
|---|---|---|
| Model access | Closed | Open weights |
| Flagship models | Grok 4.7, Grok 4.20, grok-build | GLM-5.3, DeepSeek V4.1 Flash |
| Speed | ~54 tok/s on Grok 4.6 | ~168 tok/s on GLM-5.3; 2–2.8x vs stock vLLM or SGLang (vendor) |
| Price | $2 in, $6 out (Grok 4.7); 2x past 200K | Per token, prepaid credits |
| Customization | Unknown | Agent-tuned dedicated deployments |
| Deployment | First-party API, Amazon Bedrock | Serverless, dedicated |
| Long context | 500K (4.7), 1M (4.20, 4.3) | Up to 1M (GLM-5.3, DeepSeek V4.1 Flash) |
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 tuned stacks.
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?
Fast 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 4.7); 2x past 200K. Wafer: Per token, prepaid credits. The cheaper choice depends on the model and workload.
Which has more context, xAI or Wafer?
xAI: 500K (4.7), 1M (4.20, 4.3). Wafer: Up to 1M (GLM-5.3, DeepSeek V4.1 Flash).
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.