vs

xAI vs Novita AI

A closed lab with one model family against a budget cloud with 200+ open models. xAI wins on X data and closed-model quality; Novita wins on price and breadth.

By The Subconscious Team · Updated

xAI vs Novita AI: key differences

Novita AI and xAI sell very different things. Novita is a low-cost inference cloud with 200+ open models across text, image, video, speech, voice cloning and embeddings, LLM prices from $0.02 per million, batch at 50% off, and APIs in both OpenAI and Anthropic formats. It also rents GPUs from RTX 3090s to H200s and runs a per-second agent sandbox. xAI sells one closed family, Grok, with Grok 4.6 at $2 in and $6 out, 1M context on Grok 4.20, and native Web Search and X Search.

Budget and breadth favor Novita. It serves the full 1M context on DeepSeek V4 Pro and offers hot-swappable LoRA adapters on dedicated endpoints, so teams can fine-tune and serve open models. Its weak points are looser serverless SLAs, Discord-based support and no public SOC 2 or HIPAA. xAI suits teams that want a closed model with fresh data, though it bills double past 200K prompt tokens and has fewer cloud-marketplace options. Indie products and prototypes lean to Novita. Real-time sentiment and news agents lean to Grok.

What xAI and Novita AI do

xAI

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 profile

Novita AI

Novita AI is a San Francisco inference cloud founded in late 2023 by Frank Lewis and Junyu Huang, and it competes on price and breadth. Its serverless API covers 200+ open models across LLMs, image, video, speech, voice cloning and embeddings, with LLM prices starting at $0.02 per million tokens. The API speaks both OpenAI and Anthropic formats. It became an official Hugging Face Inference Partner in April 2026 and was the day-zero launch partner for Google's Gemma 4.

Example models: DeepSeek V4 Pro, Gemma 4

Full Novita AI profile

Should you choose xAI or Novita AI?

xAI

Choose xAI for

  • Agents that depend on live X and web data
  • A single closed model family with a clear roadmap
  • Cheap output on Grok 4.20

Novita AI

Choose Novita AI for

  • Cost-first LLM and image work for prototypes
  • Serving LoRA fine-tunes on open models
  • Models, GPUs and sandboxes on one bill

xAI vs Novita AI at a glance

AttributexAINovita AI
Model accessClosedOpen weights
Flagship modelsGrok 4.6, Grok 4.20, grok-buildDeepSeek V4 Pro, Gemma 4
Speed~54 tok/s on Grok 4.6~36 tok/s on DeepSeek V4 Pro
Price$2 in, $6 out (Grok 4.6); 2x past 200KFrom $0.02 per 1M; batch 50% off
CustomizationUnknownHot-swappable LoRA adapters
DeploymentFirst-party APIServerless, GPU cloud, dedicated
Long context500K (4.6), 1M (4.20, 4.3)Full 1M on DeepSeek V4 Pro

Frequently asked questions

What is the difference between xAI and Novita AI?

A closed lab with one model family against a budget cloud with 200+ open models. xAI wins on X data and closed-model quality; Novita wins on price and breadth.

When should I choose xAI over Novita AI?

Agents that depend on live X and web data; A single closed model family with a clear roadmap; Cheap output on Grok 4.20.

When should I choose Novita AI over xAI?

Cost-first LLM and image work for prototypes; Serving LoRA fine-tunes on open models; Models, GPUs and sandboxes on one bill.

Is xAI or Novita AI cheaper?

xAI: $2 in, $6 out (Grok 4.6); 2x past 200K. Novita AI: From $0.02 per 1M; batch 50% off. The cheaper choice depends on the model and workload.

Which has more context, xAI or Novita AI?

xAI: 500K (4.6), 1M (4.20, 4.3). Novita AI: Full 1M on DeepSeek V4 Pro.

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.