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DeepInfra vs xAI
A closed lab with live X data against the cheapest shared host for open models. The choice turns on whether you need Grok itself or just low-cost tokens.
By The Subconscious Team · Updated
DeepInfra vs xAI: key differences
xAI sells one family, Grok, through its own API. DeepInfra sells 150+ open models it did not build, priced to be the floor. On raw cost DeepInfra wins by a wide margin: DeepSeek V4 Flash lists at $0.14 in and $0.28 out, against $2 in and $6 out for Grok 4.6 below 200K prompt tokens. What xAI offers in return is something no host can resell. Its server-side Web Search and X Search tools pull posts and current events straight from X, and it ships a multi-agent Grok 4.20 and a coding model called grok-build. For social listening or news agents, that live feed is the whole reason to pay more.
Context is the other sharp divide. Grok 4.6 reads 500K tokens and Grok 4.20 and 4.3 keep a 1M window at $1.25 in and $2.50 out, though any prompt past 200K bills the whole request at double. DeepInfra's FP4 DeepSeek V4 Pro stops at 66K, and quantization elsewhere in its catalog can trim context and quality, so long documents need a per-model check. For high-volume tagging, extraction or synthetic data with no need for fresh data, DeepInfra costs a fraction of Grok.
What DeepInfra and xAI do
DeepInfra
DeepInfra is the price floor for open-model inference. Developers treat it as the reference point for what a token should cost, with small models like Llama 3.1 8B at $0.02 per million and DeepSeek V4 Flash at $0.14 in and $0.28 out. The catalog covers 150+ open models across text, image and speech behind a fully OpenAI-compatible API. There are no minimums, setup fees or contracts on the shared API.
Example models: DeepSeek V4 Flash, Llama 3.1 8B
Full DeepInfra 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 DeepInfra or xAI?
DeepInfra
Choose DeepInfra for
- High-volume extraction and tagging where price per token decides
- Switching between many open models on one OpenAI-compatible key
- Synthetic data runs with no contract
xAI
Choose xAI for
- Agents that need live posts and news from X
- Prompts up to 1M tokens on Grok 4.20 or 4.3
- Teams that want image, video and audio APIs from the same lab
DeepInfra vs xAI at a glance
| Attribute | DeepInfra | xAI |
|---|---|---|
| Model access | Open weights | Closed |
| Flagship models | DeepSeek V4 Flash, Llama 3.1 8B | Grok 4.6, Grok 4.20, grok-build |
| Speed | ~33 tok/s on DeepSeek V4 Pro (FP4) | ~54 tok/s on Grok 4.6 |
| Price | From $0.02 per 1M | $2 in, $6 out (Grok 4.6); 2x past 200K |
| Customization | No managed fine-tuning | Unknown |
| Deployment | Shared API, no contracts | First-party API |
| Long context | 66K on FP4 DeepSeek V4 Pro | 500K (4.6), 1M (4.20, 4.3) |
Frequently asked questions
What is the difference between DeepInfra and xAI?
A closed lab with live X data against the cheapest shared host for open models. The choice turns on whether you need Grok itself or just low-cost tokens.
When should I choose DeepInfra over xAI?
High-volume extraction and tagging where price per token decides; Switching between many open models on one OpenAI-compatible key; Synthetic data runs with no contract.
When should I choose xAI over DeepInfra?
Agents that need live posts and news from X; Prompts up to 1M tokens on Grok 4.20 or 4.3; Teams that want image, video and audio APIs from the same lab.
Is DeepInfra or xAI cheaper?
DeepInfra: From $0.02 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, DeepInfra or xAI?
DeepInfra: 66K on FP4 DeepSeek V4 Pro. 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.