Laguna XS 2.1 vs Reka Flash 3.1

Poolside · US  |  Reka AI · US · Updated June 2026

Quick verdict

Pick Laguna XS 2.1 for remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters or open weights under openmdw-1.1, shipped day one in bf16, fp8, nvfp4 and int4 across every major runtime. Pick Reka Flash 3.1 for a 21b-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists) or strong base for fine-tuning agentic tasks - notably improved coding since the original flash 3. On a tight budget at scale, Reka Flash 3.1 is the value pick.

Laguna XS 2.1 (Poolside) and Reka Flash 3.1 (Reka AI) are two of the models people most often weigh against each other in 2026. Laguna XS 2.1 is a 33B open-weight coding MoE running on 3B active parameters — 70.9% SWE-Bench Verified and very cheap, but unproven. Reka Flash 3.1 is reka AI's compact 21B open-weight reasoning model - small enough to run locally, from a team of DeepMind and Meta alumni. They diverge most on price and context window — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecLaguna XS 2.1Reka Flash 3.1
ProviderPoolside (US) Reka AI (US)
ReleasedJuly 2, 2026 July 2025
Context window256K (~393 pages) 32K (~49 pages)
Price (in/out)$0.1/$0.2 per 1M tokens Open weight (self-host / free)
Open weight?Yes — self-hostable Yes — self-hostable
Modalitiestext, code text, code
SWE-Bench Verified70.9% Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Remarkable efficiency — 70.9% on SWE-Bench Verified from only 3B active parameters

Laguna XS 2.1

A 33B open-weight coding MoE running on 3B active parameters — 70.9% SWE-Bench Verified and very cheap, but unproven — and it carries the larger 256K context.

Open weights under OpenMDW-1.1, shipped day one in BF16, FP8, NVFP4 and INT4 across every major runtime

Laguna XS 2.1

Reka Flash 3.1 is comparatively weak here — smaller and less capable overall than flagship frontier models from major labs

Cheap even on the paid tier, at roughly a sixth of GLM 4.7's input price

Laguna XS 2.1

A 33B open-weight coding MoE running on 3B active parameters — 70.9% SWE-Bench Verified and very cheap, but unproven — and it is the newer of the two.

A 21B-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists)

Reka Flash 3.1

Reka Flash 3.1 lists a 21B-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists) among its strengths; Laguna XS 2.1 does not.

Strong base for fine-tuning agentic tasks - notably improved coding since the original Flash 3

Reka Flash 3.1

Laguna XS 2.1 is comparatively weak here — weak on harder agentic work (37.5 on Terminal-Bench 2.0), and its gain over XS.2 is barely above noise

Fully open weights (Apache 2.0) from a frontier-caliber research team

Reka Flash 3.1

Reka Flash 3.1 lists fully open weights (Apache 2.0) from a frontier-caliber research team among its strengths; Laguna XS 2.1 does not.

Lowest cost at scale

Reka Flash 3.1

Its weights are open, so at volume you pay for your own hardware instead of Laguna XS 2.1's $0.1/$0.2 per 1M tokens.

Largest single-prompt input

Laguna XS 2.1

Its 256K window is about 8× larger than Reka Flash 3.1's 32K, fitting roughly 393 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

Reka Flash 3.1

At Open weight (self-host / free) it undercuts Laguna XS 2.1, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

Laguna XS 2.1

Larger 256K window fits more in one prompt.

Anyone whose priority is remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters

Laguna XS 2.1

It is specifically built for that.

Anyone whose priority is a 21b-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists)

Reka Flash 3.1

That is its strongest area.

Laguna XS 2.1: where it fits

A 33B open-weight coding MoE running on 3B active parameters — 70.9% SWE-Bench Verified and very cheap, but unproven. Released July 2, 2026 by Poolside, it is built for remarkable efficiency — 70.9% on SWE-Bench Verified from only 3B active parameters, open weights under OpenMDW-1.1, shipped day one in BF16, FP8, NVFP4 and INT4 across every major runtime, cheap even on the paid tier, at roughly a sixth of GLM 4.7's input price, and unusually transparent evaluation — it publishes its harness, step limits, and sandbox specs.

Its trade-offs are real: weeks old with no independent replication; every published score traces back to Poolside's own harness, the free endpoint trains on your inputs and outputs — disqualifying for proprietary code, which is its main use case, and weak on harder agentic work (37.5 on Terminal-Bench 2.0), and its gain over XS.2 is barely above noise. At $0.1 in / $0.2 out per million tokens, it sits in the budget price band.

Reka Flash 3.1: where it fits

Reka AI's compact 21B open-weight reasoning model - small enough to run locally, from a team of DeepMind and Meta alumni. Released July 2025 by Reka AI, it is built for a 21B-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists), strong base for fine-tuning agentic tasks - notably improved coding since the original Flash 3, fully open weights (Apache 2.0) from a frontier-caliber research team, and built by alumni from Google DeepMind and Meta's AI labs.

Its trade-offs: a relatively small 32K context window next to million-token frontier models, smaller and less capable overall than flagship frontier models from major labs, and reka's broader roadmap has shifted toward robotics/world models after merging with Moonvalley in June 2026, raising questions about ongoing LLM investment. As an open-weight model, its running cost is your own hardware rather than a per-token fee.

The bottom line for this matchup

Laguna XS 2.1 and Reka Flash 3.1 overlap enough that the right pick depends on your specific job. Reka Flash 3.1 costs less per token; Laguna XS 2.1 holds the larger context; and each leads in its own area — Laguna XS 2.1 for remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters, Reka Flash 3.1 for a 21b-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists). Rather than crowning one, run the same hard task through both once and let the results decide.

Want both Laguna XS 2.1 and Reka Flash 3.1 without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.

See pricing

Frequently asked questions

Is Laguna XS 2.1 or Reka Flash 3.1 better for coding?

Public SWE-Bench figures are not available for Reka Flash 3.1, so the honest test is your own repository — run an identical real bug through both. By design, Laguna XS 2.1 leans toward remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters while Reka Flash 3.1 leans toward a 21b-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists), and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Laguna XS 2.1 or Reka Flash 3.1?

Reka Flash 3.1 is cheaper — $0.1/$0.2 per 1M tokens vs Open weight (self-host / free).

Which has the bigger context window?

Laguna XS 2.1 — 256K vs 32K, about 8× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Laguna XS 2.1 and Reka Flash 3.1 together?

Yes — a multi-model platform like LumiChats gives you Laguna XS 2.1, Reka Flash 3.1 and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.

Which is newer, Laguna XS 2.1 or Reka Flash 3.1?

Laguna XS 2.1 — released July 2, 2026, about 12 months after Reka Flash 3.1.

Related comparisons

Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.