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SambaNova vs Relace
SambaNova serves the big open model a coding agent thinks with. Relace serves the small fast models it works with: apply, search and compaction. Complementary layers of one agent.
By The Subconscious Team · Updated
SambaNova vs Relace: key differences
Relace is mainly a toolkit for coding agents, with a small open-model catalog on the side. relace-apply-3 merges lazy edit snippets at about 10,000 tokens per second with 128K tokens of input and output, its agentic search answers questions about large codebases in seconds, and a compaction model runs at 50,000 tokens per second. It also offers source control with retrieval built in. SambaNova serves the large general models that would drive such an agent, like MiniMax M3, DeepSeek and GPT-OSS 120B, on its own RDU chip.
Teams building a fast coding agent could use both: SambaCloud for the main model's decode, Relace for the utility calls that would otherwise burn frontier tokens. The limits sit in different places. Relace returns an error past 128K tokens, so very large files need a fallback, and it hosts only a handful of open models. SambaNova's catalog is smaller than GPU clouds. Relace offers self-hosted deployment for code that has to stay in-house, while much of SambaNova's hardware goes to neoclouds as racks.
What SambaNova and Relace 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), 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 profileRelace
Relace started out training small, fast models that act as tools for coding agents, and by October 2026 its homepage led with open-model inference instead. It says it served 30 trillion tokens in the 30 days to late October and ranks among the top providers on OpenRouter. The hosted catalog covers DeepSeek V4 Pro, V4 Flash and V4.1 Flash, Kimi K3, GLM 5.3, GLM 5.3 Flash and GLM 5.2 over OpenAI and Anthropic compatible APIs, billed per token with no subscription. Prices run from $0.07 in and $0.14 out per million tokens on DeepSeek V4 Flash to $2.00 in and $14.00 out on Kimi K3, and Relace says it reprices often.
Example models: DeepSeek V4.1 Flash, Kimi K3, GLM 5.3, relace-apply-3
Full Relace profileShould you choose SambaNova or Relace?
SambaNova
Choose SambaNova for
- Fast decode for the agent's main open model.
- Hot swapping between several large models.
- Operators buying racks for a premium speed tier.
Relace
Choose Relace for
- Instant apply and repo search inside coding agents.
- Context compaction at 50,000 tokens per second.
- Self-hosted coding utilities for private code.
SambaNova vs Relace at a glance
| Attribute | SambaNova | Relace |
|---|---|---|
| Model access | Open weights | Open weights, plus specialist coding models |
| Flagship models | MiniMax M3, GPT-OSS 120B, DeepSeek V3.1 | DeepSeek V4.1 Flash, Kimi K3, GLM 5.3, relace-apply-3 |
| Speed | ~800 tok/s on MiniMax M2.7 (SN50, SemiAnalysis) | ~10,000 tok/s apply |
| Price | $0.22 in, $0.59 out (GPT-OSS 120B) | $0.07 in, $0.50 out (DeepSeek V4.1 Flash) |
| Customization | Unknown | Unknown |
| Deployment | SambaCloud, racks for neoclouds | Hosted API, OpenRouter or self-hosted |
| Long context | Up to 1M (MiniMax M3, preview) | 128K on apply; varies by open model |
Frequently asked questions
What is the difference between SambaNova and Relace?
SambaNova serves the big open model a coding agent thinks with. Relace serves the small fast models it works with: apply, search and compaction. Complementary layers of one agent.
When should I choose SambaNova over Relace?
Fast decode for the agent's main open model; Hot swapping between several large models; Operators buying racks for a premium speed tier.
When should I choose Relace over SambaNova?
Instant apply and repo search inside coding agents; Context compaction at 50,000 tokens per second; Self-hosted coding utilities for private code.
Is SambaNova or Relace cheaper?
SambaNova: $0.22 in, $0.59 out (GPT-OSS 120B). Relace: $0.07 in, $0.50 out (DeepSeek V4.1 Flash). The cheaper choice depends on the model and workload.
Which has more context, SambaNova or Relace?
SambaNova: Up to 1M (MiniMax M3, preview). Relace: 128K on apply; varies by open model.
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.