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OpenAI vs Wafer
OpenAI's closed GPT API against Wafer, a young host that tunes open models with agents. Wafer offers fast open models for coding; OpenAI offers frontier models and scale.
By The Subconscious Team · Updated
OpenAI vs Wafer: key differences
Wafer and OpenAI sell very different things to coding teams. OpenAI charges per token for GPT-6 Astra, GPT-6.1 Sol and GPT-6 Luna, with a 1.05M window and Fast mode at double the price for up to 2.5x speed. Wafer sells open models on inference stacks its own agents tune, and its serverless API bills per token and drops into Claude Code, Cline and OpenHands. Wafer reports its tuned Qwen 3.5 397B running 2.8x faster than stock SGLang, a self-reported figure against a stock baseline.
For dedicated work, Wafer builds a deployment around a customer's model, traffic shape and SLO, then keeps re-tuning on NVIDIA or AMD as conditions change. That suits teams with a strict latency target and no kernel engineers. OpenAI's listing has no equivalent service for customer models, but it has a far larger ecosystem and closed frontier models, while Wafer is a very young company with a small catalog. A developer who wants cheap, fast open models in an agent harness can try Wafer. A business product that needs frontier quality stays on OpenAI.
What OpenAI and Wafer do
OpenAI
OpenAI runs the most widely adopted closed-model API. Its October 2026 lineup has GPT-6 Astra at the top for computer use, coding and long agentic runs, priced at $10 in and $50 out per million tokens. Below it sit GPT-6.1 Sol, the default for coding, research and computer use at $2 in and $10 out, and GPT-6 Luna for high-volume jobs at $0.10 in and $0.50 out. All three carry a 1.05M token context window with up to 128K output.
Example models: GPT-6 Astra, GPT-6.1 Sol, GPT-6 Luna
Full OpenAI 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 OpenAI or Wafer?
OpenAI
Choose OpenAI for
- GPT-6 Astra for the hardest coding and computer-use work
- Products that need a mature vendor and large ecosystem
- Usage-based billing across many tiers
Wafer
Choose Wafer for
- Fast open-model access inside agent harnesses
- Dedicated endpoints with a strict latency SLO
- Teams hedging GPU supply across NVIDIA and AMD
OpenAI vs Wafer at a glance
| Attribute | OpenAI | Wafer |
|---|---|---|
| Model access | Closed, plus open gpt-oss | Open weights |
| Flagship models | GPT-6 Astra, GPT-6.1 Sol, GPT-6 Luna | GLM-5.3, DeepSeek V4.1 Flash |
| Speed | Fast mode; Ultrafast on GPT-6 Astra | ~176 tok/s on GLM-5.3; 2–2.8x vs stock vLLM or SGLang (vendor) |
| Price | $0.10–$10 in, $0.50–$50 out per 1M | Per token, prepaid credits |
| Customization | N/A | Agent-tuned dedicated deployments |
| Deployment | API, Azure OpenAI, Bedrock | Serverless, dedicated |
| Long context | 1.05M; 2x input past 272K | Up to 1M (GLM-5.3, DeepSeek V4.1 Flash) |
Frequently asked questions
What is the difference between OpenAI and Wafer?
OpenAI's closed GPT API against Wafer, a young host that tunes open models with agents. Wafer offers fast open models for coding; OpenAI offers frontier models and scale.
When should I choose OpenAI over Wafer?
GPT-6 Astra for the hardest coding and computer-use work; Products that need a mature vendor and large ecosystem; Usage-based billing across many tiers.
When should I choose Wafer over OpenAI?
Fast open-model access inside agent harnesses; Dedicated endpoints with a strict latency SLO; Teams hedging GPU supply across NVIDIA and AMD.
Is OpenAI or Wafer cheaper?
OpenAI: $0.10–$10 in, $0.50–$50 out per 1M. Wafer: Per token, prepaid credits. The cheaper choice depends on the model and workload.
Which has more context, OpenAI or Wafer?
OpenAI: 1.05M; 2x input past 272K. 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.