Palmyra X6 vs Reka Flash 3.1

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

Quick verdict

Pick Palmyra X6 for enterprise agentic workflows - built specifically for marketing/revenue teams running multi-step agents or writer says its harness upgrades cut agent-workflow token costs by roughly 52% and improve speed by roughly 48% (writer's own figures). 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. Choose Reka Flash 3.1 if you need self-hosting or data privacy; Palmyra X6 if you want a managed API.

Palmyra X6 (Writer) and Reka Flash 3.1 (Reka AI) are two of the models people most often weigh against each other in 2026. Palmyra X6 is writer's enterprise agentic flagship - a GLM-5.2-based model with harness upgrades Writer says cut agent token costs by roughly half. 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 context window and open vs. closed weights — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecPalmyra X6Reka Flash 3.1
ProviderWriter (US) Reka AI (US)
ReleasedAugust 13, 2026 July 2025
Context window128K (~192 pages) 32K (~49 pages)
Price (in/out)Not published Open weight (self-host / free)
Open weight?No — API only Yes — self-hostable
Modalitiestext, code text, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Enterprise agentic workflows - built specifically for marketing/revenue teams running multi-step agents

Palmyra X6

Writer's enterprise agentic flagship - a GLM-5.2-based model with harness upgrades Writer says cut agent token costs by roughly half — and it carries the larger 128K context.

Writer says its harness upgrades cut agent-workflow token costs by roughly 52% and improve speed by roughly 48% (Writer's own figures)

Palmyra X6

Its 128K window holds about 3.9× more than Reka Flash 3.1's 32K in a single prompt.

A post-trained variant built on Z.ai's open-weight GLM-5.2, tuned specifically for business agent use

Palmyra X6

Writer's enterprise agentic flagship - a GLM-5.2-based model with harness upgrades Writer says cut agent token costs by roughly half — 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

Open weights make this possible at all — Palmyra X6 is API-only, so it cannot leave the vendor's servers.

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

Reka Flash 3.1

Reka AI's compact 21B open-weight reasoning model - small enough to run locally, from a team of DeepMind and Meta alumni — and its weights are open while Palmyra X6 is API-only.

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; Palmyra X6 does not.

Largest single-prompt input

Palmyra X6

Its 128K window is about 3.9× larger than Reka Flash 3.1's 32K, fitting roughly 192 pages in one prompt.

Which should you pick?

Someone analysing very long documents or codebases

Palmyra X6

Larger 128K window fits more in one prompt.

A team with data-privacy or self-hosting needs

Reka Flash 3.1

Open weights let you run it on your own hardware; Palmyra X6 is API-only.

Anyone whose priority is enterprise agentic workflows - built specifically for marketing/revenue teams running multi-step agents

Palmyra X6

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.

Palmyra X6: where it fits

Writer's enterprise agentic flagship - a GLM-5.2-based model with harness upgrades Writer says cut agent token costs by roughly half. Released August 13, 2026 by Writer, it is built for enterprise agentic workflows - built specifically for marketing/revenue teams running multi-step agents, writer says its harness upgrades cut agent-workflow token costs by roughly 52% and improve speed by roughly 48% (Writer's own figures), and a post-trained variant built on Z.ai's open-weight GLM-5.2, tuned specifically for business agent use.

Its trade-offs are real: no public per-token API price - sold through Writer's enterprise platform, not a self-serve API, not independently benchmarked on general leaderboards like SWE-bench or Artificial Analysis, and built for a narrower enterprise-agent use case rather than general-purpose chat.

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

The defining split here is open vs. closed. Reka Flash 3.1 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Palmyra X6 gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.

Want both Palmyra X6 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 Palmyra X6 or Reka Flash 3.1 better for coding?

Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Palmyra X6 leans toward enterprise agentic workflows - built specifically for marketing/revenue teams running multi-step agents 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, Palmyra X6 or Reka Flash 3.1?

Reka Flash 3.1 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Palmyra X6 is API-metered at Not published. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.

Which has the bigger context window?

Palmyra X6 — 128K vs 32K, about 3.9× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Palmyra X6 and Reka Flash 3.1 together?

Yes — a multi-model platform like LumiChats gives you Palmyra X6, 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, Palmyra X6 or Reka Flash 3.1?

Palmyra X6 — released August 13, 2026, about 14 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.