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DeepSeek vs SambaNova
SambaNova serves DeepSeek and other large open models on its own dataflow chip for fast decode. DeepSeek's own API is the cheaper source of the same weights, without the speed pitch.
By The Subconscious Team · Updated
DeepSeek vs SambaNova: key differences
SambaCloud lists DeepSeek among its models, so this is partly a question of where to run DeepSeek weights. DeepSeek's first-party API is the price anchor: V4 Pro at $1.32 in and $3.96 out at peak, half that off-peak, 1M context and cache hits at a few cents per million. SambaNova sells speed on its Reconfigurable Dataflow Unit, with a memory design that hosts very large models and hot swaps between them in milliseconds. It claims its SN50 rack runs MiniMax M2.7 near 820 tokens per second, though many headline numbers are vendor benchmarks on hardware still ramping.
Pick SambaNova for interactive coding agents and copilots on big open models, or agents that bounce between several models and benefit from fast swaps and input caching. Pick DeepSeek's API when cost is the constraint and latency is not, especially for off-peak batch runs. Data location may also matter, since DeepSeek stores hosted data in China. SambaNova's public catalog is smaller than GPU clouds, and much of its business runs through rack sales to neoclouds rather than a big self-serve platform.
What DeepSeek and SambaNova do
DeepSeek
DeepSeek is the Chinese lab whose open-weight models reset price expectations for the whole market. Its API now serves two models, both with 1M context and 384K max output. V4.1 Flash shipped September 10, 2026 with built-in image understanding at $0.30 in and $1.20 out at peak. V4 Pro, generally available since August 13, costs $1.32 in and $3.96 out at peak. Cache hits cost a few cents per million or less, and the weights ship on Hugging Face under an MIT license.
Example models: DeepSeek V4.1 Flash, DeepSeek V4 Pro
Full DeepSeek 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 M2.7, DeepSeek, Gemma 4 31B and GPT-OSS 120B, with speeds reported by Artificial Analysis.
Example models: MiniMax M2.7, GPT-OSS 120B
Full SambaNova profileShould you choose DeepSeek or SambaNova?
DeepSeek vs SambaNova at a glance
| Attribute | DeepSeek | SambaNova |
|---|---|---|
| Model access | Open weights (MIT) | Open weights |
| Flagship models | DeepSeek V4.1 Flash, V4 Pro | MiniMax M2.7, GPT-OSS 120B, DeepSeek |
| Speed | ~35 tok/s on V4 Pro | ~820 tok/s on MiniMax M2.7 (SN50) |
| Price | Off-peak hours at half price | $0.22 in, $0.59 out (GPT-OSS 120B) |
| Customization | Open weights to fine-tune | Unknown |
| Deployment | First-party API, Hugging Face weights | SambaCloud, racks for neoclouds |
| Long context | 1M, 384K max output | Up to 192K (MiniMax M2.7) |
Frequently asked questions
What is the difference between DeepSeek and SambaNova?
SambaNova serves DeepSeek and other large open models on its own dataflow chip for fast decode. DeepSeek's own API is the cheaper source of the same weights, without the speed pitch.
When should I choose DeepSeek over SambaNova?
Lowest-cost access to DeepSeek weights; Batch jobs scheduled off-peak; Long outputs up to 384K tokens.
When should I choose SambaNova over DeepSeek?
Fast decode for interactive copilots on large open models; Agents that hot swap between models; Neoclouds adding a premium speed tier.
Is DeepSeek or SambaNova cheaper?
DeepSeek: Off-peak hours at half price. SambaNova: $0.22 in, $0.59 out (GPT-OSS 120B). The cheaper choice depends on the model and workload.
Which has more context, DeepSeek or SambaNova?
DeepSeek: 1M, 384K max output. SambaNova: Up to 192K (MiniMax M2.7).
Related comparisons
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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.