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Meta vs SambaNova
Meta sells its own closed Muse models on a new API. SambaNova sells fast decode on other labs' open models, served on its own dataflow chip.
By The Subconscious Team · Updated
Meta vs SambaNova: key differences
Meta is a model lab and SambaNova is a hardware company, so the comparison is less about price per token than about what you want to run. Meta's Model API, generally available since September 2026, serves Muse Spark 1.3 with 1M context at $1.25 in and $4.25 out per million, and its endpoint speaks OpenAI, Anthropic and a stateful agentic format. SambaCloud serves open weights like MiniMax M3, DeepSeek, Llama 3.3 70B, Gemma 4 31B and GPT-OSS 120B on the Reconfigurable Dataflow Unit, which hot swaps between large models in milliseconds.
Speed claims favor SambaNova, with caveats. SemiAnalysis measured an SN50 rack at about 800 tokens per second on MiniMax M2.7, but many other headline numbers are vendor benchmarks on hardware still ramping. Meta makes no speed pitch. It pitches an agentic model at mid-tier prices plus image generation and transcription on the same key. Meta's risk is a new API with a short track record. SambaNova's is a small public catalog and a business that leans on hardware sales. Agents that bounce between open models fit SambaNova. Agents built on one multimodal model fit Meta.
What Meta and SambaNova do
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 launched the Meta Model API with Muse Spark 1.1, a multimodal reasoning model aimed at agentic coding, tool use and computer use. At Meta Connect in September, Meta made the API generally available globally. Muse Spark 1.3 has a 1M token context and costs $1.25 in and $4.25 out per million tokens, with cached input at $0.15. Muse Wonder, released October 12, 2026, sits above it for longer, harder agent work at $4 in and $20 out, also with 1M context. The endpoint speaks OpenAI Chat Completions, Anthropic Messages and a stateful agentic format.
Example models: Muse Wonder, Muse Spark 1.3, Muse Spark Open
Full Meta profileSambaNova
SambaNova designs its own inference chip, the Reconfigurable Dataflow Unit, and sells fast tokens on large open models through SambaCloud. The RDU maps the model graph onto the chip to cut trips to off-chip memory. A three-tier memory design of SRAM, HBM and bulk DRAM lets one system host very large models and hot swap between several of them in milliseconds. SambaCloud serves models like MiniMax M3 (in preview with a 1M context), DeepSeek V3.1, Llama 3.3 70B, Gemma 4 31B and GPT-OSS 120B, with speeds reported by Artificial Analysis.
Example models: MiniMax M3, GPT-OSS 120B, DeepSeek V3.1
Full SambaNova profileShould you choose Meta or SambaNova?
Meta
Choose Meta for
- One multimodal agentic model with 1M context
- Image generation and transcription on the same key
- Drop-in use from OpenAI or Anthropic SDKs
SambaNova
Choose SambaNova for
- Fast decode on large open models like MiniMax M3
- Agents that hot swap between several models
- Neoclouds adding a premium speed tier
Meta vs SambaNova at a glance
| Attribute | Meta | SambaNova |
|---|---|---|
| Model access | Closed API; open Muse Spark Open and Glimmer | Open weights |
| Flagship models | Muse Wonder, Muse Spark 1.3, Muse Spark Open | MiniMax M3, GPT-OSS 120B, DeepSeek V3.1 |
| Speed | ~145–233 tok/s on Muse Spark 1.3 | ~800 tok/s on MiniMax M2.7 (SN50, SemiAnalysis) |
| Price | $1.25–$4 in, $4.25–$20 out; Contributor tier cheaper | $0.22 in, $0.59 out (GPT-OSS 120B) |
| Customization | Open Muse weights to fine-tune | Unknown |
| Deployment | Meta Model API, Oracle Cloud | SambaCloud, racks for neoclouds |
| Long context | 1M | Up to 1M (MiniMax M3, preview) |
Frequently asked questions
What is the difference between Meta and SambaNova?
Meta sells its own closed Muse models on a new API. SambaNova sells fast decode on other labs' open models, served on its own dataflow chip.
When should I choose Meta over SambaNova?
One multimodal agentic model with 1M context; Image generation and transcription on the same key; Drop-in use from OpenAI or Anthropic SDKs.
When should I choose SambaNova over Meta?
Fast decode on large open models like MiniMax M3; Agents that hot swap between several models; Neoclouds adding a premium speed tier.
Is Meta or SambaNova cheaper?
Meta: $1.25–$4 in, $4.25–$20 out; Contributor tier cheaper. SambaNova: $0.22 in, $0.59 out (GPT-OSS 120B). The cheaper choice depends on the model and workload.
Which has more context, Meta or SambaNova?
Meta: 1M. SambaNova: Up to 1M (MiniMax M3, preview).
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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.