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SambaNova vs OpenScale
SambaNova sells fast decode on large open models from its own chip. OpenScale sells DeepSeek V4.1 Flash at very low prices on serverless GPUs.
By The Subconscious Team · Updated
SambaNova vs OpenScale: key differences
SambaNova runs models like MiniMax M3 and M2.7, DeepSeek and GPT-OSS 120B on its RDU chip, with an independent benchmark measuring about 800 tokens per second on MiniMax M2.7. GPT-OSS 120B costs $0.22 in and $0.59 out per million tokens. OpenScale serves DeepSeek V4.1 Flash with a 1M context at $0.06 in, $0.003 cached and $0.24 out under a 50% promotion, at 200+ tokens per second, with a faster tier at about twice the speed.
SambaNova is the pick when decode speed on a large model is the bottleneck, as in interactive coding agents. OpenScale is the pick when per-token cost on cache-heavy agent traffic matters most, and it adds tested setups for Claude Code, Codex and other agents.
What SambaNova and OpenScale do
SambaNova
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), MiniMax M2.7, DeepSeek, Gemma 4 31B and GPT-OSS 120B, with speeds reported by Artificial Analysis.
Example models: MiniMax M3, MiniMax M2.7, GPT-OSS 120B
Full SambaNova profileOpenScale
OpenScale is a Y Combinator-backed inference API built by LatentSpace and run out of Singapore. It sells open models behind one endpoint that speaks Chat Completions, the OpenAI Responses API and Anthropic Messages, so Claude Code, Codex, OpenCode, Cline, GitHub Copilot, Zed and other coding agents can point at it with a base URL and key. Its public catalog today is DeepSeek V4.1 Flash in two versions, standard and fast, both with a 1M token context window and up to 384K output tokens.
Example models: DeepSeek V4.1 Flash
Full OpenScale profileShould you choose SambaNova or OpenScale?
SambaNova vs OpenScale at a glance
| Attribute | SambaNova | OpenScale |
|---|---|---|
| Model access | Open weights | Open weights |
| Flagship models | MiniMax M3, MiniMax M2.7, GPT-OSS 120B | DeepSeek V4.1 Flash |
| Speed | ~800 tok/s on MiniMax M2.7 (SN50, SemiAnalysis) | 200+ tok/s; fast tier ~2x |
| Price | $0.22 in, $0.59 out (GPT-OSS 120B) | $0.06 in, $0.24 out (promo, DeepSeek V4.1 Flash) |
| Customization | Unknown | N/A |
| Deployment | SambaCloud, racks for neoclouds | Serverless API |
| Long context | Up to 1M (MiniMax M3, preview) | 1M context |
Frequently asked questions
What is the difference between SambaNova and OpenScale?
SambaNova sells fast decode on large open models from its own chip. OpenScale sells DeepSeek V4.1 Flash at very low prices on serverless GPUs.
When should I choose SambaNova over OpenScale?
Fast decode on large open models; Interactive coding copilots; Racks for neoclouds.
When should I choose OpenScale over SambaNova?
Lower per-token prices; Very cheap cached input; Quick setup in coding agents.
Is SambaNova or OpenScale cheaper?
SambaNova: $0.22 in, $0.59 out (GPT-OSS 120B). OpenScale: $0.06 in, $0.24 out (promo, DeepSeek V4.1 Flash). The cheaper choice depends on the model and workload.
Which has more context, SambaNova or OpenScale?
SambaNova: Up to 1M (MiniMax M3, preview). OpenScale: 1M context.
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.