Poolside: Laguna XS 2.1

Poolside: Laguna XS 2.1

poolside · Released Jul 2, 2026
Intelligence #369 / 688
31.1 our score
Speed
Not reported
Input Price #176 / 688
$0.060 per 1M tokens
Output Price #160 / 688
$0.120 per 1M tokens
Context #181 / 688
262,144 tokens

Analysis Summary

Poolside Laguna XS 2.1 is positioned as a low-cost model with a 262K context window, tool use, and function calling. That feature combination could suit repository navigation, structured internal tasks, document workflows, and other applications where long context and economical inference are useful.

The supplied record contains no reasoning, coding, agentic, or instruction-following measurements. Its suitability for autonomous coding or polished client content is therefore unknown, despite the attractive price. Design for Online would consider it for a monitored technical pilot and routine automation tests, but benchmark results and workflow-specific evaluation are needed before it becomes a preferred production model.

Assessed August 9, 2026

Editorial notes

Laguna XS 2.1 combines 262K context, tool use, function calling, and very low listed pricing, but it has no benchmark data confirming its quality or reliability.

Rankings consider pricing, capabilities, benchmarks, and real-world applicability and are refreshed as new models launch. Feedback?

DFO Verdict

Laguna XS 2.1 combines 262K context, tool use, function calling, and very low listed pricing, but it has no benchmark data confirming its quality or reliability.

#369 of 688 overall Up 25 this week

How Poolside: Laguna XS 2.1 compares

Its 262K-token context window is larger than 74% of the models we list. At $0.06 per million input tokens it is cheaper than 74% of comparable models.

Position in the field
Intelligence: smarter than 46% of models #369
Price: cheaper than 74% of models #176
Context: larger than 74% of models #181
worst in fieldmedianbest in field
Price vs frontier peers · $ per 1M tokens
Poolside: Laguna XS 2.1 $0.06 in $0.12 out
Claude Opus 5 $5.00 in $25.00 out
Qwen: Qwen3.8 Max $2.00 in $6.00 out
Anthropic: Claude Fable 5 $10.00 in $50.00 out

Dark bar = input · light bar = output, scaled to the priciest peer.

Context window vs peers · tokens
Poolside: Laguna XS 2.1 262K

1M tokens ≈ 8 full-length novels or ~2,500 pages of business documents in a single request.

Intelligence0Technical0Value8.3Content5
Performance profile

Strongest on value. The pulled-in technical corner is the trade-off, and if the shape matters more than the price, this is your model.

Compare shapes side-by-side →

Pricing

Token Type Cost per 1M tokens Cost per 1K tokens
Input $0.06 $0.000060
Output $0.12 $0.000120

What would Poolside: Laguna XS 2.1 cost your business?

Pick the job that looks most like yours, then fine-tune with the sliders. Estimates update live.

A website chatbot handling around 100 customer conversations a day, a few short messages each.

3,000
One request is one message, email, draft or automation call.
1,200 tokens

$0/mo Poolside: Laguna XS 2.1

Full calculator with 688 models → Price Calculator

DFO AI AUTOMATION

These numbers get smaller with the right architecture.

We route routine calls to cheap models and save Poolside: Laguna XS 2.1 for the hard ones. Most clients cut their estimate by 60-80%.

Talk to our team

About Poolside: Laguna XS 2.1

Laguna XS 2.1 is the latest coding agent model in the 33B-A3B category from Poolside and a step forward from their Laguna XS.2 model (released in April 2026). It combines..

Frequently asked questions about Poolside: Laguna XS 2.1

How much does Poolside: Laguna XS 2.1 cost?

Poolside: Laguna XS 2.1 costs $0.06 per million input tokens and $0.12 per million output tokens.

What is the context window of Poolside: Laguna XS 2.1?

Poolside: Laguna XS 2.1 has a context window of 262,144 tokens (262K).

What can Poolside: Laguna XS 2.1 do?

Poolside: Laguna XS 2.1 supports tool use and function calling.

Who created Poolside: Laguna XS 2.1?

Poolside: Laguna XS 2.1 is developed by Poolside and was released on July 2, 2026.

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