Laguna S 2.1 vs Kimi K3
Both shipped inside the same week of July 2026 and both call themselves open weight, but they answer different questions. Kimi K3 is the largest open-weight system ever released: 2.8 trillion total parameters, roughly 16 active per token across 896 experts, 1 million token context, native vision, and hosted pricing of $3 per million input tokens and $15 per million output. Laguna S 2.1 is poolside's first major public model at 118 billion total parameters with about 8 billion active, the same 1 million token context, and hosted pricing of $0.10 and $0.20. The gap that matters is not on a leaderboard, it is on a rack. K3 quantized to 4-bit MXFP4 still needs roughly 1,450GB of accelerator memory before the KV cache, which means eight B200-class cards, so for almost everyone open weights resolves to a hosted API call. Laguna S 2.1 at 8 billion active parameters is genuinely self-hostable by a small team. K3 is the stronger general model and the only one of the two with vision; Laguna is a coding specialist that costs a thirtieth as much per input token and publishes its full benchmark trajectories. Pick on deployment reality, not parameter count.
Head-to-Head Specs
| Spec | Laguna S 2.1 | Kimi K3 |
|---|---|---|
| Provider | poolside | Moonshot AI |
| Input Price | $0.10/1M | $3.00/1M |
| Output Price | $0.20/1M | $15.00/1M |
| Context Window | 1.0M | 1M |
| Released | 2026-07 | 2026-07 |
| Capabilities | text, code, tool-use, reasoning | text, vision, code, tool-use, reasoning |
Category Breakdown
$0.10 vs $3.00 on a cache miss, a 30x gap; K3 narrows it to $0.30 on a cache hit
$0.20 vs $15.00, a 75x gap
~8B active parameters against a 2.8T model needing roughly 1,450GB at 4-bit, so only one of these is runnable without a rack
K3 sits around 57 on the Artificial Analysis Intelligence Index, third or fourth overall, and posts 93.5 percent on GPQA Diamond
K3 takes text, image, and video natively; Laguna S 2.1 is a text and code model
poolside reports 70.2 percent on Terminal-Bench 2.1 in thinking mode and 78.5 on SWE-Bench Multilingual, leading open models of disclosed size
poolside published every unedited benchmark trajectory and disclosed its own harness; K3 shipped vendor benchmarks only
Laguna weights hit Hugging Face day one under OpenMDW-1.1; K3 weights were promised for July 27 and were not public at launch
Both ship 1,048,576 tokens; Laguna allows 131K output
Calls to Moonshot's own API are processed under Chinese jurisdiction, which matters for compliance-exposed buyers; poolside is US-based
Choose Laguna S 2.1 when:
- ▸Cost-sensitive agentic coding at volume
- ▸Any team that actually intends to self-host rather than call a hosted endpoint
- ▸Terminal and shell-driven automation
- ▸Compliance-exposed buyers who need the inference to stay inside their own jurisdiction
Choose Kimi K3 when:
- ▸General knowledge work and science reasoning beyond code
- ▸Workloads that need native image or video input
- ▸Frontier-adjacent quality where a 30x price gap is still worth paying
- ▸Teams comparing against the top of the open-weight index rather than the top of the coding niche
Frequently Asked Questions
Which is better, Laguna S 2.1 or Kimi K3?
It depends on your use case. Laguna S 2.1 from poolside excels at cost-sensitive agentic coding at volume, while Kimi K3 from Moonshot AI is better for general knowledge work and science reasoning beyond code. See the full comparison above for detailed benchmarks and pricing.
How much does Laguna S 2.1 cost compared to Kimi K3?
Laguna S 2.1 costs $0.10 input and $0.20 output per 1M tokens. Kimi K3 costs $3.00 input and $15.00 output per 1M tokens.
What is the context window difference between Laguna S 2.1 and Kimi K3?
Laguna S 2.1 supports 1.0M tokens, while Kimi K3 supports 1M tokens.