GLM 5 vs LongCat-2.0
Z.ai · China | Meituan · China · Updated June 2026
Quick verdict
Pick GLM 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. 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. On a tight budget at scale, LongCat-2.0 is the value pick.
GLM 5 (Z.ai) and LongCat-2.0 (Meituan) are two of the models people most often weigh against each other in 2026. GLM 5 is z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. LongCat-2.0 is a trillion-parameter, MIT-licensed open MoE delivering near-frontier agentic coding at 1M context — trained entirely on Chinese chips. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
- ▸Context window: LongCat-2.0 holds 5× more — 1M (~1,500 pages) vs 200K (~300 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: LongCat-2.0 is the newer model by about 5 months (released July 5, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | GLM 5 | LongCat-2.0 |
|---|---|---|
| Provider | Z.ai (China) | Meituan (China) |
| Released | February 12, 2026 | July 5, 2026 |
| Context window | 200K (~300 pages) | 1M (~1,500 pages) |
| Price (in/out) | $1/$3.2 per 1M tokens | Open weight (self-host / free) |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text, code | text, code |
| SWE-Bench Verified | 77.8% | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Agentic planning and long-horizon coding workflows
GLM 5
GLM 5 lists agentic planning and long-horizon coding workflows among its strengths; LongCat-2.0 does not.
Complex systems design and backend reasoning
GLM 5
GLM 5 lists complex systems design and backend reasoning among its strengths; LongCat-2.0 does not.
Iterative self-correction on autonomous tasks
GLM 5
GLM 5 lists iterative self-correction on autonomous tasks among its strengths; LongCat-2.0 does not.
Near-frontier agentic coding — topped OpenRouter anonymously as 'Owl Alpha' for two months
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.
Massive native 1M context at near-linear cost via sparse attention
LongCat-2.0
Its 1M window holds about 5× more than GLM 5's 200K 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 is the newer of the two.
Lowest cost at scale
LongCat-2.0
Its weights are open, so at volume you pay for your own hardware instead of GLM 5's $1/$3.2 per 1M tokens.
Largest single-prompt input
LongCat-2.0
Its 1M window is about 5× larger than GLM 5's 200K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ LongCat-2.0
At Open weight (self-host / free) it undercuts GLM 5, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ LongCat-2.0
Larger 1M window fits more in one prompt.
Anyone whose priority is agentic planning and long-horizon coding workflows
→ GLM 5
It is specifically built for that.
Anyone whose priority is near-frontier agentic coding — topped openrouter anonymously as 'owl alpha' for two months
→ LongCat-2.0
That is its strongest area.
GLM 5: where it fits
Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. Released February 12, 2026 by Z.ai, it is built for agentic planning and long-horizon coding workflows, complex systems design and backend reasoning, iterative self-correction on autonomous tasks, and open weights under the permissive MIT license.
Its trade-offs are real: 200K context trails 1M-context rivals, and quickly superseded by GLM-5.1 and GLM-5.2. At $1 in / $3.2 out per million tokens, it sits in the budget price band.
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: 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.
The bottom line for this matchup
GLM 5 and LongCat-2.0 overlap enough that the right pick depends on your specific job. LongCat-2.0 costs less per token; LongCat-2.0 holds the larger context; and each leads in its own area — GLM 5 for agentic planning and long-horizon coding workflows, LongCat-2.0 for near-frontier agentic coding — topped openrouter anonymously as 'owl alpha' for two months. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both GLM 5 and LongCat-2.0 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 pricingFrequently asked questions
Is GLM 5 or LongCat-2.0 better for coding?
Public SWE-Bench figures are not available for LongCat-2.0, so the honest test is your own repository — run an identical real bug through both. By design, GLM 5 leans toward agentic planning and long-horizon coding workflows while LongCat-2.0 leans toward near-frontier agentic coding — topped openrouter anonymously as 'owl alpha' for two months, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 5 or LongCat-2.0?
LongCat-2.0 is cheaper — $1/$3.2 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
LongCat-2.0 — 1M vs 200K, about 5× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5 and LongCat-2.0 together?
Yes — a multi-model platform like LumiChats gives you GLM 5, LongCat-2.0 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, GLM 5 or LongCat-2.0?
LongCat-2.0 — released July 5, 2026, about 5 months after GLM 5.
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.