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OpenAI vs Luminal
OpenAI sells closed GPT models through the most-used API. Luminal sells a compiler that makes your own open models run faster on GPUs you choose.
By The Subconscious Team · Updated
OpenAI vs Luminal: key differences
These two barely overlap. OpenAI runs a closed-model API with GPT-6 Astra at the top and the GPT-5.6 family below it, plus hosted tools, the Agents SDK and 1.05M token context. You never touch weights or hardware. Luminal is an inference compiler: it turns a PyTorch or Hugging Face model into native GPU kernels ahead of time, and sells that as early-access serverless endpoints or an on-prem license.
The real question is whether you need a frontier closed model or control over an open one. OpenAI wins on capability, ecosystem and time to value. Luminal fits teams that already chose an open model, perhaps OpenAI's own gpt-oss, and want more throughput per GPU; it reports GPT-OSS 120B at 36K tokens per second on 8 H100s versus 26K for vLLM. That figure is self-reported and Luminal publishes no per-token prices.
What OpenAI and Luminal do
OpenAI
OpenAI runs the most widely adopted closed-model API. Its September 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 sits the GPT-5.6 family: Sol for hard professional work, Terra as the balanced default, and Luna for high-volume jobs at $0.20 in and $1.20 out. All of them carry a 1.05M token context window with up to 128K output.
Example models: GPT-6 Astra, GPT-5.6 Terra
Full OpenAI profileLuminal
Luminal builds an inference compiler. Where vLLM and SGLang interpret a model at runtime, Luminal compiles it ahead of time into native kernels for GPUs and ASICs. Models get lowered to a small graph of 15 primitive ops, and the compiler searches over fusion, tiling, memory and scheduling choices instead of relying on hand-written rules, which it says can find optimizations like Flash Attention on its own. The compiler is open source in Rust under Apache 2.0 or MIT, runs on CUDA and Metal with ROCm on the roadmap, and works as a torch.compile backend.
Example models: GPT-OSS 120B, Llama 3 8B
Full Luminal profileShould you choose OpenAI or Luminal?
OpenAI
Choose OpenAI for
- Frontier closed models for coding and computer use
- Hosted tools and an agent SDK out of the box
- Fastest path from idea to production
Luminal
Choose Luminal for
- Serving gpt-oss or other open weights at higher throughput
- On-prem deployments with custom kernel work and SLAs
- Serving custom or fine-tuned architectures off any catalog
OpenAI vs Luminal at a glance
| Attribute | OpenAI | Luminal |
|---|---|---|
| Model access | Closed, plus open gpt-oss | Bring your own weights |
| Flagship models | GPT-6 Astra, GPT-5.6 Sol, Terra, Luna | No public catalog |
| Speed | Fast mode: up to 2.5x at 2x price | 36K tok/s on GPT-OSS 120B, 8xH100 (vendor) |
| Price | $0.20–$10 in, $1.20–$50 out per 1M | Pay per use; rates not published |
| Customization | N/A | Compiles any PyTorch or HF model |
| Deployment | API, Azure OpenAI, Bedrock | Serverless (early access), on-prem license |
| Long context | 1.05M; 2x input past 272K | Unknown |
Frequently asked questions
What is the difference between OpenAI and Luminal?
OpenAI sells closed GPT models through the most-used API. Luminal sells a compiler that makes your own open models run faster on GPUs you choose.
When should I choose OpenAI over Luminal?
Frontier closed models for coding and computer use; Hosted tools and an agent SDK out of the box; Fastest path from idea to production.
When should I choose Luminal over OpenAI?
Serving gpt-oss or other open weights at higher throughput; On-prem deployments with custom kernel work and SLAs; Serving custom or fine-tuned architectures off any catalog.
Is OpenAI or Luminal cheaper?
OpenAI: $0.20–$10 in, $1.20–$50 out per 1M. Luminal: Pay per use; rates not published. The cheaper choice depends on the model and workload.
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.