LongCat-2.0 vs Palmyra X6

Meituan · China  |  Writer · US · Updated June 2026

Quick verdict

Pick LongCat-2.0 for near-frontier agentic coding — topped openrouter anonymously as 'owl alpha' for two months or massive native 1m context at near-linear cost via sparse attention. 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). Choose LongCat-2.0 if you need self-hosting or data privacy; Palmyra X6 if you want a managed API.

LongCat-2.0 (Meituan, China) and Palmyra X6 (Writer, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. LongCat-2.0 is a trillion-parameter, MIT-licensed open MoE delivering near-frontier agentic coding at 1M context — trained entirely on Chinese chips. 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. 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

SpecLongCat-2.0Palmyra X6
ProviderMeituan (China) Writer (US)
ReleasedJuly 5, 2026 August 13, 2026
Context window1M (~1,500 pages) 128K (~192 pages)
Price (in/out)Open weight (self-host / free) Not published
Open weight?Yes — self-hostable No — API only
Modalitiestext, code text, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Near-frontier agentic coding — topped OpenRouter anonymously as 'Owl Alpha' for two months

LongCat-2.0

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

Massive native 1M context at near-linear cost via sparse attention

LongCat-2.0

Its 1M window holds about 7.8× more than Palmyra X6's 128K in a single prompt.

Fully MIT-licensed 1.6T-parameter mixture-of-experts (about 48B active)

LongCat-2.0

A trillion-parameter, MIT-licensed open MoE delivering near-frontier agentic coding at 1M context — trained entirely on Chinese chips — and it carries the larger 1M context.

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 is the newer of the two.

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

Palmyra X6 lists writer says its harness upgrades cut agent-workflow token costs by roughly 52% and improve speed by roughly 48% (Writer's own figures) among its strengths; LongCat-2.0 does not.

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

Palmyra X6

Palmyra X6 lists a post-trained variant built on Z.ai's open-weight GLM-5.2, tuned specifically for business agent use among its strengths; LongCat-2.0 does not.

Largest single-prompt input

LongCat-2.0

Its 1M window is about 7.8× larger than Palmyra X6's 128K, fitting roughly 1,500 pages in one prompt.

Which should you pick?

Someone analysing very long documents or codebases

LongCat-2.0

Larger 1M window fits more in one prompt.

A team with data-privacy or self-hosting needs

LongCat-2.0

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

Anyone whose priority is near-frontier agentic coding — topped openrouter anonymously as 'owl alpha' for two months

LongCat-2.0

It is specifically built for that.

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

Palmyra X6

That is its strongest area.

An enterprise with regional data-residency rules

Palmyra X6 or LongCat-2.0

Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.

LongCat-2.0: where it fits

A trillion-parameter, MIT-licensed open MoE delivering near-frontier agentic coding at 1M context — trained entirely on Chinese chips. Released July 5, 2026 by Meituan, it is built for near-frontier agentic coding — topped OpenRouter anonymously as 'Owl Alpha' for two months, massive native 1M context at near-linear cost via sparse attention, fully MIT-licensed 1.6T-parameter mixture-of-experts (about 48B active), and trained end to end on domestic Chinese chips, independent of Nvidia hardware.

Its trade-offs are real: a 1.6T model is extremely expensive to self-host, so most use leans on the China-hosted API, and headline scores are vendor-reported on SWE-Bench Pro, not the Verified set. As an open-weight model, its running cost is your own hardware rather than a per-token fee.

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: 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.

The bottom line for this matchup

The defining split here is open vs. closed. LongCat-2.0 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 LongCat-2.0 and Palmyra X6 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 LongCat-2.0 or Palmyra X6 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, LongCat-2.0 leans toward near-frontier agentic coding — topped openrouter anonymously as 'owl alpha' for two months while Palmyra X6 leans toward enterprise agentic workflows - built specifically for marketing/revenue teams running multi-step agents, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, LongCat-2.0 or Palmyra X6?

LongCat-2.0 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?

LongCat-2.0 — 1M vs 128K, about 7.8× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both LongCat-2.0 and Palmyra X6 together?

Yes — a multi-model platform like LumiChats gives you LongCat-2.0, Palmyra X6 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, LongCat-2.0 or Palmyra X6?

Palmyra X6 — released August 13, 2026, about 39 days after LongCat-2.0.

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.