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Inception vs Boundless
Inception sells its own diffusion LLMs built for fast output. Boundless serves open model families plus GPT and Claude, and tunes reserved capacity.
By The Subconscious Team · Updated
Inception vs Boundless: key differences
Inception's closed Mercury models generate blocks of tokens in parallel. Mercury 2.5 is a reasoning model with tool use and 260K context at $0.20 in and $0.75 out per million tokens; Inception reports over 1,100 tokens per second and Artificial Analysis measures about 640. It also sells through AWS Bedrock, Azure Foundry and OpenRouter, with fine-tuning on request. Boundless serves open models from DeepSeek, GLM, Kimi, MiniMax and others, most with 1M context, plus resold GPT and Claude models. Vercel AI Gateway measures Boundless at 342 tokens per second on DeepSeek V4.1 Flash 1017 and 138 on Kimi K3. Boundless does not offer fine-tuning.
Pick Inception when output speed drives the product, as in code edits or voice agents, or to buy through Bedrock or Azure. Pick Boundless for large open models on hard reasoning and long agent tasks, context up to 1M, or a reserved deployment with latency targets in the contract.
What Inception and Boundless do
Inception
Inception builds diffusion large language models (dLLMs). Every other LLM on this list generates text one token at a time; Inception's Mercury models start from a rough draft of a whole block and refine it over a few passes, producing many tokens in parallel. That ties speed less to output length and lets the models run fast on ordinary NVIDIA GPUs rather than custom chips. The company was founded by professors from Stanford, UCLA and Cornell behind core work on diffusion, Flash Attention and DPO, and is led by Stanford's Stefano Ermon. It raised a $50M seed in November 2025 led by Menlo Ventures, with Mayfield, M12, NVentures, Snowflake and Databricks, plus Andrew Ng and Andrej Karpathy as angels.
Example models: Mercury 2.5, Mercury 2
Full Inception 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 Inception or Boundless?
Inception vs Boundless at a glance
| Attribute |  Inception | Boundless |
|---|---|---|
| Model access | Closed weights (Mercury dLLMs) | Open weights, plus closed GPT and Claude |
| Flagship models | Mercury 2.5, Mercury 2 | GLM-5.3, DeepSeek V4.1 Flash, Kimi K3 |
| Speed | 1,100+ tok/s (vendor); ~640 measured | 342 tok/s on DeepSeek V4.1 Flash 1017 (Vercel) |
| Price | $0.20 in, $0.75 out (Mercury 2.5) | $0.04–$2.30 in, $0.21–$11.40 out per 1M (open) |
| Customization | Fine-tuning on request | Serving-stack tuning, no fine-tuning |
| Deployment | Serverless API, Bedrock, Azure, private | Serverless API, reserved tailored capacity |
| Long context | 260K | 1M context on most models |
Frequently asked questions
What is the difference between Inception and Boundless?
Inception sells its own diffusion LLMs built for fast output. Boundless serves open model families plus GPT and Claude, and tunes reserved capacity.
When should I choose Inception over Boundless?
Fast output on standard GPUs; Voice agents and code edits; Buying through Bedrock or Azure.
When should I choose Boundless over Inception?
Large open models for agent tasks; 1M context on most models; Reserved capacity with latency targets.
Is Inception or Boundless cheaper?
Inception: $0.20 in, $0.75 out (Mercury 2.5). 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, Inception or Boundless?
Inception: 260K. 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.