Moonshot AI vs SambaNova

A model lab with a slow, strong flagship against a chip company selling fast decode on big open models. They meet at the problem of making large open models quick.

By The Subconscious Team · Updated September 30, 2026

Moonshot AI vs SambaNova: key differences

Kimi K3's biggest weakness is exactly what SambaNova sells. K3 always thinks and runs around 33 tokens per second on Moonshot's API. SambaNova's Reconfigurable Dataflow Unit targets fast decode on large open models, and it reports a SambaRack SN50 running MiniMax M2.7 near 820 tokens per second in its fastest configuration. The catch is that SambaCloud's listed models are MiniMax M2.7, DeepSeek, Gemma 4 31B and GPT-OSS 120B, not Kimi. So this is a choice between K3's capability at K3's speed and a different open model at SambaNova's speed.

SambaNova's SN50 claims, 5x the peak speed of an NVIDIA B200 and support for models up to 10 trillion parameters, are vendor benchmarks on hardware still ramping. Moonshot's claims have fared better, with independent testers mostly confirming them and Vals AI and Artificial Analysis both ranking K3 near the top. Self-hosting K3 takes a 64+ accelerator cluster, while SambaNova's air-cooled racks are built to host very large models in existing data centers. For interactive copilots, SambaNova's speed matters more. For hard, unattended coding runs, K3's quality does.

What Moonshot AI and SambaNova do

Moonshot AI

Moonshot AI is the Beijing lab behind the Kimi models. Its flagship Kimi K3 launched July 16, 2026 as a 2.8 trillion parameter mixture-of-experts model that activates 16 of 896 experts per token, with native vision and a 1M token context. It is the first open model in the 3T class, and full weights landed on Hugging Face on July 27. The hosted API costs $3 in and $15 out per million tokens, with cached input at $0.30, and it runs through an OpenAI-compatible endpoint, Kimi Code in the terminal, OpenRouter and Cloudflare Workers AI.

Example models: Kimi K3, Kimi K2.6

Full Moonshot AI profile

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 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 profile

Should you choose Moonshot AI or SambaNova?

Moonshot AI

Choose Moonshot AI for

Unattended coding runs where quality beats speed
Tasks that need 1M context and native vision
Teams that want open flagship weights

SambaNova

Choose SambaNova for

Interactive copilots that need fast decode on large open models
Agents that hot swap between several models
GPU clouds that want to add fast decode racks

Moonshot AI vs SambaNova at a glance

Attribute	Moonshot AI	SambaNova
Model access	Open weights, custom license	Open weights
Flagship models	Kimi K3, Kimi K2.6	MiniMax M2.7, GPT-OSS 120B, DeepSeek
Speed	~33 tok/s on Kimi K3	~820 tok/s on MiniMax M2.7 (SN50)
Price	$3 in, $15 out (Kimi K3)	$0.22 in, $0.59 out (GPT-OSS 120B)
Customization	Open weights to fine-tune	Unknown
Deployment	API, Kimi Code, OpenRouter	SambaCloud, racks for neoclouds
Long context	1M	Up to 192K (MiniMax M2.7)

Frequently asked questions

What is the difference between Moonshot AI and SambaNova?

A model lab with a slow, strong flagship against a chip company selling fast decode on big open models. They meet at the problem of making large open models quick.

When should I choose Moonshot AI over SambaNova?

Unattended coding runs where quality beats speed; Tasks that need 1M context and native vision; Teams that want open flagship weights.

When should I choose SambaNova over Moonshot AI?

Interactive copilots that need fast decode on large open models; Agents that hot swap between several models; GPU clouds that want to add fast decode racks.

Is Moonshot AI or SambaNova cheaper?

Moonshot AI: $3 in, $15 out (Kimi K3). SambaNova: $0.22 in, $0.59 out (GPT-OSS 120B). The cheaper choice depends on the model and workload.

Which has more context, Moonshot AI or SambaNova?

Moonshot AI: 1M. 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.

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