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AI Choice Engine

Poolside

Laguna S 2.1

Laguna S 2.1 is a Poolside model with 1.05M context, $0.09 in / $0.18 out per million tokens, last verified October 2, 2026. Open weights: yes. Public-board scores are linked to the publisher; vendor-only figures stay in the watch-out, not on the board.

Open-weight long-horizon coding model with up to 1M context and max/off thinking modes.

Recorded profile verification October 2, 2026Individual provider fields may change

AI model specification details
SpecificationLaguna S 2.1
ProviderPoolside
TierBalanced
Context window1.05M
Max output131K
Input / 1M tokens$0.09
Output / 1M tokens$0.18
WeightsOpen
Parameters118B total / 8B active (MoE)
Reasoning levelsNot verifiedUnverified
Modalitiestext
LicenseOpenMDW-1.1
API model idpoolside/laguna-s-2.1
ReleasedJuly 21, 2026

Pricing tiers: Poolside route on OpenRouter $0.09/$0.18 per MTok, cache read $0.009, FP4 serving, 1,048,576 context and 131,072 maximum completion. The feed reports a 10% discount; no end date is stated. These rates and output cap are route-specific. Direct Poolside inference documents max_tokens default 32,768 and accepted range 1–262,144; this is a request range, not the OpenRouter cap. Downloaded weights use OpenMDW-1.1 and incur compute costs.

Publisher evidence

Published benchmark results

Each result keeps its source date visible. Dates refer to source publication or capture unless the source explicitly states when it measured the result. A missing benchmark is not treated as a zero.

Published benchmark results for Laguna S 2.1
BenchmarkScoreSource dateSource
Terminal-Bench 2.1 [max]Poolside vendor result; pool agent harness, thinking mode max; not mini-swe-agent leaderboard runCross-model comparability not verified70.2%2026-07-21Poolside vendor evaluation · View source
DeepSWE 1.1 [max]Poolside vendor result; pool agent harness, thinking mode; not mini-swe-agent leaderboard runCross-model comparability not verified40.4%2026-07-21Poolside vendor evaluation · View source

Best for

  • Long-context coding
  • Agentic research
  • Self-hosting

Watch out

Thinking off/max are the two model behaviors, max enabled by default; direct API toggles enable_thinking, not a low/medium/high effort enum. Preserve prior reasoning_content. Native context is 1,048,576. OpenMDW-1.1 redistribution requires retaining the agreement and applicable notices. July 21 vendor scores use pool harness with four attempts per task for Terminal and three for DeepSWE; the comparison table mixes maximum scores across different harnesses, so cross-model verdicts are withheld.

Source receipts

Catalog figures for this model were checked against the following sources.

Common questions

Laguna S 2.1

Answered from the verified figures on this page rather than general guidance.

How much does Laguna S 2.1 cost per million tokens?
Laguna S 2.1 lists base rates of $0.09 per million input tokens and $0.18 per million output tokens. Estimate both input and output token volume and any additional charges for your workload. Poolside route on OpenRouter $0.09/$0.18 per MTok, cache read $0.009, FP4 serving, 1,048,576 context and 131,072 maximum completion. The feed reports a 10% discount; no end date is stated. These rates and output cap are route-specific. Direct Poolside inference documents max_tokens default 32,768 and accepted range 1–262,144; this is a request range, not the OpenRouter cap. Downloaded weights use OpenMDW-1.1 and incur compute costs.
What is Laguna S 2.1's context window?
Laguna S 2.1 accepts about 1.05M tokens of context. That only matters if you routinely send very long documents, large codebases, or multi-turn histories that approach that limit.
What is Laguna S 2.1 best for?
Laguna S 2.1 is a balanced tier from Poolside. It suits long-context coding, agentic research, self-hosting. Thinking off/max are the two model behaviors, max enabled by default; direct API toggles enable_thinking, not a low/medium/high effort enum. Preserve prior reasoning_content. Native context is 1,048,576. OpenMDW-1.1 redistribution requires retaining the agreement and applicable notices. July 21 vendor scores use pool harness with four attempts per task for Terminal and three for DeepSWE; the comparison table mixes maximum scores across different harnesses, so cross-model verdicts are withheld.
Can I self-host Laguna S 2.1?
Laguna S 2.1 publishes open weights, but self-hosting depends on the licence, hardware footprint, quantisation quality, and serving stack. A hosted API is often cheaper until you have measured throughput and concurrency on your own hardware.