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OpenAI vs Boundless
OpenAI sells its closed GPT-6 lineup direct. Boundless serves open models per token and resells GPT-6 Sol and Luna through the same API.
By The Subconscious Team · Updated
OpenAI vs Boundless: key differences
OpenAI's lineup runs from GPT-6 Astra at $10 in and $50 out per million tokens to GPT-6 Luna at $0.10 in and $0.50 out, all with 1.05M context, plus the Responses API's hosted tools, the Agents SDK and Batch at half price. Boundless launched in September 2026 and serves open models such as DeepSeek V4 Flash at $0.04 in and $0.21 out and GLM-5.3 at $1.12 in and $3.52 out, most with 1M context. It also resells GPT-6 Sol at $2 in and $10 out and GPT-6 Luna at $0.10 in and $0.50 out, the same as OpenAI's list price for Luna.
OpenAI is the pick for GPT-6 Astra, hosted tools and the largest SDK ecosystem. Boundless fits startups that want to move some traffic from GPT to an open model while keeping GPT-6 on the same API and prepaid credits. Its tailored tier reserves capacity and writes uptime, throughput and time-to-first-token targets into the contract, after a free two-week evaluation on real traffic. Boundless is new and has published no benchmarks or customers.
What OpenAI and Boundless 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 profileBoundless
Boundless launched in September 2026 as an inference partner for AI-native startups. It sells two things. The Model API serves pre-tuned models on shared capacity, billed per token from prepaid credits with a budget per API key. Tailored inference reserves capacity for one customer, and Boundless engineers pick the model on the customer’s own evals, tune precision, batching, caching, speculative decoding and parallelism, then run the deployment against uptime, throughput and latency targets written into the contract. A free two-week evaluation tests those targets on real traffic against the customer’s current provider first.
Example models: GLM-5.3, DeepSeek V4.1 Flash, Kimi K3
Full Boundless profileShould you choose OpenAI or Boundless?
OpenAI vs Boundless at a glance
| Attribute | OpenAI | Boundless |
|---|---|---|
| Model access | Closed, plus open gpt-oss | Open weights, plus closed GPT and Claude |
| Flagship models | GPT-6 Astra, GPT-6.1 Sol, GPT-6 Luna | GLM-5.3, DeepSeek V4.1 Flash, Kimi K3 |
| Speed | Fast mode; Ultrafast on GPT-6 Astra | 342 tok/s on DeepSeek V4.1 Flash 1017 (Vercel) |
| Price | $0.10–$10 in, $0.50–$50 out per 1M | $0.04–$2.30 in, $0.21–$11.40 out per 1M (open) |
| Customization | N/A | Serving-stack tuning, no fine-tuning |
| Deployment | API, Azure OpenAI, Bedrock | Serverless API, reserved tailored capacity |
| Long context | 1.05M; 2x input past 272K | 1M context on most models |
Frequently asked questions
What is the difference between OpenAI and Boundless?
OpenAI sells its closed GPT-6 lineup direct. Boundless serves open models per token and resells GPT-6 Sol and Luna through the same API.
When should I choose OpenAI over Boundless?
GPT-6 Astra for coding and computer use; Hosted tools and the Agents SDK; Batch and cached-input discounts.
When should I choose Boundless over OpenAI?
Open models and GPT-6 on one API; Moving traffic from GPT to open models; Contracted latency and uptime targets.
Is OpenAI or Boundless cheaper?
OpenAI: $0.10–$10 in, $0.50–$50 out per 1M. Boundless: $0.04–$2.30 in, $0.21–$11.40 out per 1M (open). The cheaper choice depends on the model and workload.
Which has more context, OpenAI or Boundless?
OpenAI: 1.05M; 2x input past 272K. Boundless: 1M context on most models.
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.