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OpenAI vs xAI
Two closed labs with different hooks. OpenAI brings the biggest ecosystem and cloud reach; xAI brings live X data and much cheaper output tokens.
By The Subconscious Team · Updated
OpenAI vs xAI: key differences
Both labs keep their weights closed, so the comparison comes down to price, context and what each model can see. xAI's Grok 4.6 costs $2 in and $6 out under 200K prompt tokens, far below GPT-6 Astra's $50 per million output. Grok 4.20 and 4.3 go further, with a 1M window at $1.25 in and $2.50 out. xAI's other draw is live data: server-side Web Search and X Search tools pull current posts straight from X, which no other lab offers natively. For social listening and news agents, that alone can settle it.
Long prompts hurt on both sides, in different ways. OpenAI bills input at 2x and output at 1.5x past 272K tokens. xAI doubles the whole request once a prompt hits 200K, and Grok 4.6 stops at 500K. OpenAI answers with scale: the largest SDK ecosystem, the Responses API's hosted tools, the Agents SDK, and distribution through Azure OpenAI and Bedrock. xAI has a smaller enterprise footprint and fewer cloud-marketplace options, so buyers who purchase through a cloud marketplace will find OpenAI easier to procure.
What OpenAI and xAI 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 profilexAI
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 profileShould you choose OpenAI or xAI?
OpenAI
Choose OpenAI for
- Enterprises buying through Azure OpenAI or Bedrock
- Frontier computer use and coding on GPT-6 Astra
- Agents built on hosted tools and the Agents SDK
xAI
Choose xAI for
- News, market and social-sentiment agents that need live X data
- Output-heavy workloads where $6 per million beats GPT pricing
- Cheap 1M context on Grok 4.20 and 4.3
OpenAI vs xAI at a glance
| Attribute | OpenAI | xAI |
|---|---|---|
| Model access | Closed, plus open gpt-oss | Closed |
| Flagship models | GPT-6 Astra, GPT-5.6 Sol, Terra, Luna | Grok 4.6, Grok 4.20, grok-build |
| Speed | Fast mode: up to 2.5x at 2x price | ~54 tok/s on Grok 4.6 |
| Price | $0.20–$10 in, $1.20–$50 out per 1M | $2 in, $6 out (Grok 4.6); 2x past 200K |
| Customization | N/A | Unknown |
| Deployment | API, Azure OpenAI, Bedrock | First-party API |
| Long context | 1.05M; 2x input past 272K | 500K (4.6), 1M (4.20, 4.3) |
Frequently asked questions
What is the difference between OpenAI and xAI?
Two closed labs with different hooks. OpenAI brings the biggest ecosystem and cloud reach; xAI brings live X data and much cheaper output tokens.
When should I choose OpenAI over xAI?
Enterprises buying through Azure OpenAI or Bedrock; Frontier computer use and coding on GPT-6 Astra; Agents built on hosted tools and the Agents SDK.
When should I choose xAI over OpenAI?
News, market and social-sentiment agents that need live X data; Output-heavy workloads where $6 per million beats GPT pricing; Cheap 1M context on Grok 4.20 and 4.3.
Is OpenAI or xAI cheaper?
OpenAI: $0.20–$10 in, $1.20–$50 out per 1M. xAI: $2 in, $6 out (Grok 4.6); 2x past 200K. The cheaper choice depends on the model and workload.
Which has more context, OpenAI or xAI?
OpenAI: 1.05M; 2x input past 272K. xAI: 500K (4.6), 1M (4.20, 4.3).
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.