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xAI vs SambaNova
A closed model lab against a chip company serving open models fast. Pick Grok for its models and X data; pick SambaNova for fast decode on big open weights.
By The Subconscious Team · Updated
xAI vs SambaNova: key differences
xAI sells models. SambaNova sells speed on other people's models. SambaCloud runs open weights like MiniMax M2.7, DeepSeek, Gemma 4 31B and GPT-OSS 120B on SambaNova's Reconfigurable Dataflow Unit, which can hot swap between large models in milliseconds. SambaNova says its SN50 rack runs MiniMax M2.7 near 820 tokens per second in its fastest configuration, though many headline numbers are vendor benchmarks on hardware still ramping. xAI serves only Grok, but Grok has things SambaNova cannot offer: native X Search, a coding model called grok-build, and 1M context on Grok 4.20.
The choice is mostly about which models you want. Teams committed to open weights, or running agents that bounce between several models, get more from SambaNova, whose value often arrives through hardware sales and neocloud partnerships. Teams that want a closed model with cheap output, $6 per million on Grok 4.6 and $2.50 on Grok 4.20, and live data from X, get more from xAI. Grok's catch is the 2x bill past 200K prompt tokens. SambaNova's is a smaller public catalog than GPU clouds.
What xAI and SambaNova do
xAI
xAI sells the Grok models through its own API. Grok 4.6 is the current flagship and xAI tells developers to use it for everything outside audio, image and video, code included. It has a 500K context window and costs $2 in and $6 out per million tokens under 200K prompt tokens. Older Grok 4.20 and 4.3 models keep a 1M window at $1.25 in and $2.50 out, which is aggressive for that capability class.
Example models: Grok 4.6, Grok 4.20
Full xAI 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 xAI or SambaNova?
xAI vs SambaNova at a glance
| Attribute | xAI | SambaNova |
|---|---|---|
| Model access | Closed | Open weights |
| Flagship models | Grok 4.6, Grok 4.20, grok-build | MiniMax M2.7, GPT-OSS 120B, DeepSeek |
| Speed | ~54 tok/s on Grok 4.6 | ~820 tok/s on MiniMax M2.7 (SN50) |
| Price | $2 in, $6 out (Grok 4.6); 2x past 200K | $0.22 in, $0.59 out (GPT-OSS 120B) |
| Customization | Unknown | Unknown |
| Deployment | First-party API | SambaCloud, racks for neoclouds |
| Long context | 500K (4.6), 1M (4.20, 4.3) | Up to 192K (MiniMax M2.7) |
Frequently asked questions
What is the difference between xAI and SambaNova?
A closed model lab against a chip company serving open models fast. Pick Grok for its models and X data; pick SambaNova for fast decode on big open weights.
When should I choose xAI over SambaNova?
Closed models with cheap output tokens; Agents that need current posts from X; 1M context on Grok 4.20.
When should I choose SambaNova over xAI?
Fast decode on large open models; Agents that switch between models mid-task; Neoclouds adding a premium speed tier.
Is xAI or SambaNova cheaper?
xAI: $2 in, $6 out (Grok 4.6); 2x past 200K. SambaNova: $0.22 in, $0.59 out (GPT-OSS 120B). The cheaper choice depends on the model and workload.
Which has more context, xAI or SambaNova?
xAI: 500K (4.6), 1M (4.20, 4.3). SambaNova: Up to 192K (MiniMax M2.7).
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.