Pick GLM 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. Pick Ling-2.6-1T for a trillion-parameter moe (1t total / ~50-63b active) from ant group - the fintech giant behind alipay - entering ai at frontier scale or fully open weights under the permissive mit license, unusual for a model this large. On a tight budget at scale, Ling-2.6-1T is the value pick.
GLM 5 (Z.ai) and Ling-2.6-1T (Ant Group) 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. Ling-2.6-1T is ant Group's trillion-parameter, MIT-licensed open model - a fintech giant's frontier-scale entry into the Chinese open-weight AI race. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: Ling-2.6-1T is about 3.3× cheaper on input ($0.3/$2.5 per 1M tokens vs $1/$3.2 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: Ling-2.6-1T holds 1.3× more — 256K (~393 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: Ling-2.6-1T is the newer model by about 48 days (released April 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GLM 5
Ling-2.6-1T
Provider
Z.ai (China)
Ant Group (China)
Released
February 12, 2026
April 2026
Context window
200K (~300 pages)
256K (~393 pages)
Price (in/out)
$1/$3.2 per 1M tokens
$0.3/$2.5 per 1M tokens
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; Ling-2.6-1T does not.
Complex systems design and backend reasoning: GLM 5 — GLM 5 lists complex systems design and backend reasoning among its strengths; Ling-2.6-1T does not.
Iterative self-correction on autonomous tasks: GLM 5 — GLM 5 lists iterative self-correction on autonomous tasks among its strengths; Ling-2.6-1T does not.
A trillion-parameter MoE (1T total / ~50-63B active) from Ant Group - the fintech giant behind Alipay - entering AI at frontier scale: Ling-2.6-1T — At $0.3/$2.5 per 1M tokens it undercuts GLM 5 ($1/$3.2 per 1M tokens), and that gap compounds at volume.
Fully open weights under the permissive MIT license, unusual for a model this large: Ling-2.6-1T — Ant Group's trillion-parameter, MIT-licensed open model - a fintech giant's frontier-scale entry into the Chinese open-weight AI race — and it runs cheaper at $0.3/$2.5 per 1M tokens.
A companion 'Ring' reasoning variant claims gold-medal-level IMO/CMO math scores using a hybrid linear-attention architecture: Ling-2.6-1T — Ant Group's trillion-parameter, MIT-licensed open model - a fintech giant's frontier-scale entry into the Chinese open-weight AI race — and it carries the larger 256K context.
Lowest cost at scale: Ling-2.6-1T — At $0.3/$2.5 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Ling-2.6-1T — Its 256K window is about 1.3× larger than GLM 5's 200K, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Ling-2.6-1T — At $0.3/$2.5 per 1M tokens it undercuts GLM 5, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Ling-2.6-1T — Larger 256K 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 a trillion-parameter moe (1t total / ~50-63b active) from ant group - the fintech giant behind alipay - entering ai at frontier scale: Ling-2.6-1T — 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.
Ling-2.6-1T: where it fits
Ant Group's trillion-parameter, MIT-licensed open model - a fintech giant's frontier-scale entry into the Chinese open-weight AI race. Released April 2026 by Ant Group, it is built for a trillion-parameter MoE (1T total / ~50-63B active) from Ant Group - the fintech giant behind Alipay - entering AI at frontier scale, fully open weights under the permissive MIT license, unusual for a model this large, a companion 'Ring' reasoning variant claims gold-medal-level IMO/CMO math scores using a hybrid linear-attention architecture, and a major Chinese lab with real financial backing, distinct from DeepSeek, Alibaba, or Moonshot.
Its trade-offs: pricing shown is third-party hosting, not an official Ant Group rate card, newer entrant to LLMs specifically - less track record than dedicated AI labs, and exact release date is disputed across sources (reports range from mid to late April 2026). At $0.3 in / $2.5 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
GLM 5 and Ling-2.6-1T overlap enough that the right pick depends on your specific job. Ling-2.6-1T costs less per token; Ling-2.6-1T holds the larger context; and each leads in its own area — GLM 5 for agentic planning and long-horizon coding workflows, Ling-2.6-1T for a trillion-parameter moe (1t total / ~50-63b active) from ant group - the fintech giant behind alipay - entering ai at frontier scale. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is GLM 5 or Ling-2.6-1T better for coding?
Public SWE-Bench figures are not available for Ling-2.6-1T, 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 Ling-2.6-1T leans toward a trillion-parameter moe (1t total / ~50-63b active) from ant group - the fintech giant behind alipay - entering ai at frontier scale, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 5 or Ling-2.6-1T?
Ling-2.6-1T is cheaper — $1/$3.2 per 1M tokens vs $0.3/$2.5 per 1M tokens, roughly 3.3× apart on input.
Which has the bigger context window?
Ling-2.6-1T — 256K vs 200K, about 1.3× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5 and Ling-2.6-1T together?
Yes — a multi-model platform like LumiChats gives you GLM 5, Ling-2.6-1T 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 Ling-2.6-1T?
Ling-2.6-1T — released April 2026, about 48 days after GLM 5.
GLM 5 vs Ling-2.6-1T
Z.ai · China | Ant Group · 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 Ling-2.6-1T for a trillion-parameter moe (1t total / ~50-63b active) from ant group - the fintech giant behind alipay - entering ai at frontier scale or fully open weights under the permissive mit license, unusual for a model this large. On a tight budget at scale, Ling-2.6-1T is the value pick.
GLM 5 (Z.ai) and Ling-2.6-1T (Ant Group) 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. Ling-2.6-1T is ant Group's trillion-parameter, MIT-licensed open model - a fintech giant's frontier-scale entry into the Chinese open-weight AI race. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: Ling-2.6-1T is about 3.3× cheaper on input ($0.3/$2.5 per 1M tokens vs $1/$3.2 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: Ling-2.6-1T holds 1.3× more — 256K (~393 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: Ling-2.6-1T is the newer model by about 48 days (released April 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GLM 5
Ling-2.6-1T
Provider
Z.ai (China)
Ant Group (China)
Released
February 12, 2026
April 2026
Context window
200K (~300 pages)
256K (~393 pages)
Price (in/out)
$1/$3.2 per 1M tokens
$0.3/$2.5 per 1M tokens
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; Ling-2.6-1T does not.
Complex systems design and backend reasoning
GLM 5
GLM 5 lists complex systems design and backend reasoning among its strengths; Ling-2.6-1T does not.
Iterative self-correction on autonomous tasks
GLM 5
GLM 5 lists iterative self-correction on autonomous tasks among its strengths; Ling-2.6-1T does not.
A trillion-parameter MoE (1T total / ~50-63B active) from Ant Group - the fintech giant behind Alipay - entering AI at frontier scale
Ling-2.6-1T
At $0.3/$2.5 per 1M tokens it undercuts GLM 5 ($1/$3.2 per 1M tokens), and that gap compounds at volume.
Fully open weights under the permissive MIT license, unusual for a model this large
Ling-2.6-1T
Ant Group's trillion-parameter, MIT-licensed open model - a fintech giant's frontier-scale entry into the Chinese open-weight AI race — and it runs cheaper at $0.3/$2.5 per 1M tokens.
A companion 'Ring' reasoning variant claims gold-medal-level IMO/CMO math scores using a hybrid linear-attention architecture
Ling-2.6-1T
Ant Group's trillion-parameter, MIT-licensed open model - a fintech giant's frontier-scale entry into the Chinese open-weight AI race — and it carries the larger 256K context.
Lowest cost at scale
Ling-2.6-1T
At $0.3/$2.5 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Ling-2.6-1T
Its 256K window is about 1.3× larger than GLM 5's 200K, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Ling-2.6-1T
At $0.3/$2.5 per 1M tokens it undercuts GLM 5, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Ling-2.6-1T
Larger 256K 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 a trillion-parameter moe (1t total / ~50-63b active) from ant group - the fintech giant behind alipay - entering ai at frontier scale
→ Ling-2.6-1T
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.
Ling-2.6-1T: where it fits
Ant Group's trillion-parameter, MIT-licensed open model - a fintech giant's frontier-scale entry into the Chinese open-weight AI race. Released April 2026 by Ant Group, it is built for a trillion-parameter MoE (1T total / ~50-63B active) from Ant Group - the fintech giant behind Alipay - entering AI at frontier scale, fully open weights under the permissive MIT license, unusual for a model this large, a companion 'Ring' reasoning variant claims gold-medal-level IMO/CMO math scores using a hybrid linear-attention architecture, and a major Chinese lab with real financial backing, distinct from DeepSeek, Alibaba, or Moonshot.
Its trade-offs: pricing shown is third-party hosting, not an official Ant Group rate card, newer entrant to LLMs specifically - less track record than dedicated AI labs, and exact release date is disputed across sources (reports range from mid to late April 2026). At $0.3 in / $2.5 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
GLM 5 and Ling-2.6-1T overlap enough that the right pick depends on your specific job. Ling-2.6-1T costs less per token; Ling-2.6-1T holds the larger context; and each leads in its own area — GLM 5 for agentic planning and long-horizon coding workflows, Ling-2.6-1T for a trillion-parameter moe (1t total / ~50-63b active) from ant group - the fintech giant behind alipay - entering ai at frontier scale. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both GLM 5 and Ling-2.6-1T 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 Ling-2.6-1T, 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 Ling-2.6-1T leans toward a trillion-parameter moe (1t total / ~50-63b active) from ant group - the fintech giant behind alipay - entering ai at frontier scale, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 5 or Ling-2.6-1T?
Ling-2.6-1T is cheaper — $1/$3.2 per 1M tokens vs $0.3/$2.5 per 1M tokens, roughly 3.3× apart on input.
Which has the bigger context window?
Ling-2.6-1T — 256K vs 200K, about 1.3× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5 and Ling-2.6-1T together?
Yes — a multi-model platform like LumiChats gives you GLM 5, Ling-2.6-1T 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 Ling-2.6-1T?
Ling-2.6-1T — released April 2026, about 48 days after GLM 5.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.