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 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 5.1 (Z.ai) and Ling-2.6-1T (Ant Group) are two of the models people most often weigh against each other in 2026. GLM 5.1 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. 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 4.7× cheaper on input ($0.3/$2.5 per 1M tokens vs $1.4/$4.4 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.
Specifications
Spec
GLM 5.1
Ling-2.6-1T
Provider
Z.ai (China)
Ant Group (China)
Released
April 7, 2026
April 2026
Context window
200K (~300 pages)
256K (~393 pages)
Price (in/out)
$1.4/$4.4 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
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 — 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 — and it is the newer of the two.
State-of-the-art open-weight coding (topped SWE-Bench Pro at launch): GLM 5.1 — GLM 5.1 lists state-of-the-art open-weight coding (topped SWE-Bench Pro at launch) among its strengths; Ling-2.6-1T does not.
Sustained tool use across thousands of calls: GLM 5.1 — Ling-2.6-1T is comparatively weak here — exact release date is disputed across sources (reports range from mid to late April 2026)
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 ($1.4/$4.4 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 5.1'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 5.1, 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 long-horizon autonomous agentic engineering (up to 8-hour runs): GLM 5.1 — 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 5.1: 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.
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 5.1 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 5.1 for long-horizon autonomous agentic engineering (up to 8-hour runs), 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 5.1 or Ling-2.6-1T 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 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 5.1 or Ling-2.6-1T?
Ling-2.6-1T is cheaper — $1.4/$4.4 per 1M tokens vs $0.3/$2.5 per 1M tokens, roughly 4.7× 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 5.1 and Ling-2.6-1T together?
Yes — a multi-model platform like LumiChats gives you GLM 5.1, 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 5.1 or Ling-2.6-1T?
GLM 5.1 — released April 7, 2026, about 6 days after Ling-2.6-1T.
GLM 5.1 vs Ling-2.6-1T
Z.ai · China | Ant Group · 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 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 5.1 (Z.ai) and Ling-2.6-1T (Ant Group) are two of the models people most often weigh against each other in 2026. GLM 5.1 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. 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 4.7× cheaper on input ($0.3/$2.5 per 1M tokens vs $1.4/$4.4 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.
Side-by-side specs
Spec
GLM 5.1
Ling-2.6-1T
Provider
Z.ai (China)
Ant Group (China)
Released
April 7, 2026
April 2026
Context window
200K (~300 pages)
256K (~393 pages)
Price (in/out)
$1.4/$4.4 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
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
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 — and it is the newer of the two.
State-of-the-art open-weight coding (topped SWE-Bench Pro at launch)
GLM 5.1
GLM 5.1 lists state-of-the-art open-weight coding (topped SWE-Bench Pro at launch) among its strengths; Ling-2.6-1T does not.
Sustained tool use across thousands of calls
GLM 5.1
Ling-2.6-1T is comparatively weak here — exact release date is disputed across sources (reports range from mid to late April 2026)
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 ($1.4/$4.4 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 5.1'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 5.1, 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 long-horizon autonomous agentic engineering (up to 8-hour runs)
→ GLM 5.1
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 5.1: 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.
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 5.1 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 5.1 for long-horizon autonomous agentic engineering (up to 8-hour runs), 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 5.1 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 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 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 5.1 or Ling-2.6-1T?
Ling-2.6-1T is cheaper — $1.4/$4.4 per 1M tokens vs $0.3/$2.5 per 1M tokens, roughly 4.7× 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 5.1 and Ling-2.6-1T together?
Yes — a multi-model platform like LumiChats gives you GLM 5.1, 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 5.1 or Ling-2.6-1T?
GLM 5.1 — released April 7, 2026, about 6 days after Ling-2.6-1T.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.