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Novita AI vs General Compute
Novita AI sells 200+ cheap open models plus GPUs and sandboxes. General Compute sells a handful of open models with fast ASIC decode.
By The Subconscious Team · Updated
Novita AI vs General Compute: key differences
Novita serves 200+ open models across text, image, video and speech from $0.02 per million tokens, with batch at 50% off and both OpenAI and Anthropic formats. DeepSeek V4 Pro runs about 36 tokens per second with its full 1M context. It adds a GPU cloud, dedicated endpoints with hot-swappable LoRA and agent sandboxes. General Compute serves MiniMax M2.7, DeepSeek V3.2, DeepSeek V3.1 and GPT-OSS 120B from $0.21 in, over OpenAI format only. It reports over 1,000 tokens per second on GPT-OSS 120B in its own benchmark.
Novita is the pick for cost-first products, wide model choice and multimodal work on one bill. General Compute fits teams whose bottleneck is output speed on a known open model, such as coding agents or voice. Its catalog is small and older, with DeepSeek V3.2 capped at 32K context.
What Novita AI and General Compute do
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
General Compute
General Compute is a San Francisco inference cloud built on alternative chips. It splits each request across hardware, with NVIDIA GPUs handling prefill and ASICs from Cerebras, SambaNova, Positron AI and d-Matrix handling decode. In its own open benchmark on GPT-OSS 120B it measured 1,025 to 1,234 output tokens per second against 100 to 121 on an unnamed GPU cloud, and up to 16.2x faster end to end. Founders Finn Puklowski and Jason Goodison raised a $15M seed led by FUSE, an up to $400M debt facility from Upper90 and, on October 27, 2026, a $100M Series A co-led by Blackbird and Abstract.
Example models: MiniMax M2.7, DeepSeek V3.2, GPT-OSS 120B
Full General Compute profileShould you choose Novita AI or General Compute?
Novita AI
Choose Novita AI for
- Lowest-cost open-model calls
- A wide multimodal catalog
- GPUs and agent sandboxes on one bill
General Compute
Choose General Compute for
- High decode speed
- Coding and voice agents
- Dedicated tokens-per-second SLAs
Novita AI vs General Compute at a glance
| Attribute | Novita AI |  General Compute |
|---|---|---|
| Model access | Open weights | Open weights |
| Flagship models | DeepSeek V4 Pro, Gemma 4 | MiniMax M2.7, DeepSeek V3.2, GPT-OSS 120B |
| Speed | ~36 tok/s on DeepSeek V4 Pro | 1,000+ tok/s on GPT-OSS 120B (vendor) |
| Price | From $0.02 per 1M; batch 50% off | $0.21–$0.28 in, $0.38–$1.20 out per 1M |
| Customization | Hot-swappable LoRA adapters | Custom LoRA and fine-tuned checkpoints |
| Deployment | Serverless, GPU cloud, dedicated | Serverless API, dedicated racks, bare metal |
| Long context | Full 1M on DeepSeek V4 Pro | 192K (MiniMax M2.7) |
Frequently asked questions
What is the difference between Novita AI and General Compute?
Novita AI sells 200+ cheap open models plus GPUs and sandboxes. General Compute sells a handful of open models with fast ASIC decode.
When should I choose Novita AI over General Compute?
Lowest-cost open-model calls; A wide multimodal catalog; GPUs and agent sandboxes on one bill.
When should I choose General Compute over Novita AI?
High decode speed; Coding and voice agents; Dedicated tokens-per-second SLAs.
Is Novita AI or General Compute cheaper?
Novita AI: From $0.02 per 1M; batch 50% off. General Compute: $0.21–$0.28 in, $0.38–$1.20 out per 1M. The cheaper choice depends on the model and workload.
Which has more context, Novita AI or General Compute?
Novita AI: Full 1M on DeepSeek V4 Pro. General Compute: 192K (MiniMax M2.7).
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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.