# Meta vs Boundless

> Meta sells its closed Muse Spark models through a preview API. Boundless serves open models from several labs plus GPT and Claude, with zero data retention.

Canonical: https://www.subconscious.dev/compare/meta-vs-boundless · By The Subconscious Team · Updated October 29, 2026

## How they compare

Meta's Muse Spark 1.3 has a 1M context at $1.25 in and $4.25 out per million tokens, with cached input at $0.15 and about 145 to 233 tokens per second. A Contributor tier drops that to $0.10 in and $0.20 out if Meta can train on your prompts and completions, and the same key covers Muse Image, Muse Voice Transcribe and web search grounding. The API is still in public preview. Boundless serves open models such as DeepSeek V4.1 Flash at $0.20 in and $1.00 out and GLM-5.3 at $1.12 in and $3.52 out, most with 1M context, and resells GPT and Claude. It keeps zero data retention and runs requests in the US or Europe.

Meta is the pick for a first-party agentic model with image, voice and search on one key, or near-free prototyping where sharing data is acceptable. Boundless fits teams that want a choice of open models next to GPT and Claude, or a reserved deployment its engineers tune against contract targets. Both speak OpenAI and Anthropic formats.

## What each one does

### Meta

Meta has moved from open Llama releases toward its own closed API. Meta Superintelligence Labs builds the Muse family, and in July 2026 Meta opened a public preview of the Meta Model API with Muse Spark 1.1, a multimodal reasoning model aimed at agentic coding, tool use and computer use. The current lineup runs through Muse Spark 1.3 with a 1M token context. Standard pricing is $1.25 in and $4.25 out per million tokens, with cached input at $0.15, and the endpoint speaks OpenAI Chat Completions, Anthropic Messages and a stateful agentic format.

### Boundless

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.

## Which is best, and when

### Choose Meta for

- A first-party agentic model
- Near-free Contributor tier
- Web search grounding on the API

### Choose Boundless for

- A choice of open models
- Zero data retention
- Contract targets on reserved capacity

## At a glance

| Attribute | Meta | Boundless |
|---|---|---|
| Model access | Closed API; open Muse Glimmer | Open weights, plus closed GPT and Claude |
| Flagship models | Muse Spark 1.3, Muse Glimmer | GLM-5.3, DeepSeek V4.1 Flash, Kimi K3 |
| Speed | ~145–233 tok/s on Muse Spark 1.3 | 342 tok/s on DeepSeek V4.1 Flash 1017 (Vercel) |
| Price | $1.25 in, $4.25 out; Contributor tier cheaper | $0.04–$2.30 in, $0.21–$11.40 out per 1M (open) |
| Customization | Open Muse Glimmer weights to fine-tune | Serving-stack tuning, no fine-tuning |
| Deployment | Meta Model API (preview) | Serverless API, reserved tailored capacity |
| Long context | 1M | 1M context on most models |

## FAQ

### What is the difference between Meta and Boundless?

Meta sells its closed Muse Spark models through a preview API. Boundless serves open models from several labs plus GPT and Claude, with zero data retention.

### When should I choose Meta over Boundless?

A first-party agentic model; Near-free Contributor tier; Web search grounding on the API.

### When should I choose Boundless over Meta?

A choice of open models; Zero data retention; Contract targets on reserved capacity.

### Is Meta or Boundless cheaper?

Meta: $1.25 in, $4.25 out; Contributor tier cheaper. 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, Meta or Boundless?

Meta: 1M. Boundless: 1M context on most models.

## Running long-horizon agents?

If your agents run past 200K tokens, compare both against Subconscious: [Subconscious vs Meta](https://www.subconscious.dev/compare/subconscious-vs-meta.md), [Subconscious vs Boundless](https://www.subconscious.dev/compare/subconscious-vs-boundless.md).

Full profiles: [Meta](https://www.subconscious.dev/providers/meta.md), [Boundless](https://www.subconscious.dev/providers/boundless.md).