Pick GLM 5.1 for long-horizon autonomous agentic engineering (up to 8-hour runs) or state-of-the-art open-weight coding (topped swe-bench pro at launch). 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 5.1 (Z.ai) and LongCat-2.0 (Meituan) are two of the models people most often weigh against each other in 2026. GLM 5.1 is an open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. 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
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 3 months (released July 5, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GLM 5.1
LongCat-2.0
Provider
Z.ai (China)
Meituan (China)
Released
April 7, 2026
July 5, 2026
Context window
200K (~300 pages)
1M (~1,500 pages)
Price (in/out)
$1.4/$4.4 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Long-horizon autonomous agentic engineering (up to 8-hour runs): GLM 5.1 — A core design strength of GLM 5.1.
State-of-the-art open-weight coding (topped SWE-Bench Pro at launch): GLM 5.1 — A core design strength of GLM 5.1.
Sustained tool use across thousands of calls: GLM 5.1 — A core design strength of GLM 5.1.
Near-frontier agentic coding — topped OpenRouter anonymously as 'Owl Alpha' for two months: LongCat-2.0 — A core design strength of LongCat-2.0.
Massive native 1M context at near-linear cost via sparse attention: LongCat-2.0 — A core design strength of LongCat-2.0.
Fully MIT-licensed 1.6T-parameter mixture-of-experts (about 48B active): LongCat-2.0 — A core design strength of LongCat-2.0.
Lowest cost at scale: LongCat-2.0 — At Open weight (self-host / free), it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: LongCat-2.0 — Its 1M window is about 5× larger, 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 5.1, 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 long-horizon autonomous agentic engineering (up to 8-hour runs): GLM 5.1 — 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 5.1: where it fits
An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. Released April 7, 2026 by Z.ai, it is built for long-horizon autonomous agentic engineering (up to 8-hour runs), state-of-the-art open-weight coding (topped SWE-Bench Pro at launch), sustained tool use across thousands of calls, and self-hostable under a permissive MIT license.
Its trade-offs are real: text-only, with no image, audio, or video input, and 754B-parameter MoE demands heavy GPU resources to self-host. At $1.4 in / $4.4 out per million tokens, it sits in the mid 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 5.1 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 5.1 for long-horizon autonomous agentic engineering (up to 8-hour runs), 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.
Frequently asked questions
Is GLM 5.1 or LongCat-2.0 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, GLM 5.1 leans toward long-horizon autonomous agentic engineering (up to 8-hour runs) 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 5.1 or LongCat-2.0?
LongCat-2.0 is cheaper — $1.4/$4.4 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 5.1 and LongCat-2.0 together?
Yes — a multi-model platform like LumiChats gives you GLM 5.1, 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 5.1 or LongCat-2.0?
LongCat-2.0 — released July 5, 2026, about 3 months after GLM 5.1.
GLM 5.1 vs LongCat-2.0
Z.ai · China | Meituan · China · Updated June 2026
Quick verdict
Pick GLM 5.1 for long-horizon autonomous agentic engineering (up to 8-hour runs) or state-of-the-art open-weight coding (topped swe-bench pro at launch). 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 5.1 (Z.ai) and LongCat-2.0 (Meituan) are two of the models people most often weigh against each other in 2026. GLM 5.1 is an open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. 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 3 months (released July 5, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GLM 5.1
LongCat-2.0
Provider
Z.ai (China)
Meituan (China)
Released
April 7, 2026
July 5, 2026
Context window
200K (~300 pages)
1M (~1,500 pages)
Price (in/out)
$1.4/$4.4 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Long-horizon autonomous agentic engineering (up to 8-hour runs)
GLM 5.1
A core design strength of GLM 5.1.
State-of-the-art open-weight coding (topped SWE-Bench Pro at launch)
GLM 5.1
A core design strength of GLM 5.1.
Sustained tool use across thousands of calls
GLM 5.1
A core design strength of GLM 5.1.
Near-frontier agentic coding — topped OpenRouter anonymously as 'Owl Alpha' for two months
LongCat-2.0
A core design strength of LongCat-2.0.
Massive native 1M context at near-linear cost via sparse attention
At Open weight (self-host / free), it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
LongCat-2.0
Its 1M window is about 5× larger, 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 5.1, 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 long-horizon autonomous agentic engineering (up to 8-hour runs)
→ GLM 5.1
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 5.1: where it fits
An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. Released April 7, 2026 by Z.ai, it is built for long-horizon autonomous agentic engineering (up to 8-hour runs), state-of-the-art open-weight coding (topped SWE-Bench Pro at launch), sustained tool use across thousands of calls, and self-hostable under a permissive MIT license.
Its trade-offs are real: text-only, with no image, audio, or video input, and 754B-parameter MoE demands heavy GPU resources to self-host. At $1.4 in / $4.4 out per million tokens, it sits in the mid 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 5.1 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 5.1 for long-horizon autonomous agentic engineering (up to 8-hour runs), 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 5.1 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.
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, GLM 5.1 leans toward long-horizon autonomous agentic engineering (up to 8-hour runs) 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 5.1 or LongCat-2.0?
LongCat-2.0 is cheaper — $1.4/$4.4 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 5.1 and LongCat-2.0 together?
Yes — a multi-model platform like LumiChats gives you GLM 5.1, 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 5.1 or LongCat-2.0?
LongCat-2.0 — released July 5, 2026, about 3 months after GLM 5.1.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.