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. Pick Qwen3 235B A22B (2507) for deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux) or exceptional multilingual and alignment results (79.2 arena-hard v2, 85.2 writingbench). On a tight budget at scale, Qwen3 235B A22B (2507) is the value pick.
Ling-2.6-1T (Ant Group) and Qwen3 235B A22B (2507) (Alibaba) are two of the models people most often weigh against each other in 2026. 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. Qwen3 235B A22B (2507) is an older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Their biggest split is price, and the breakdown below shows exactly how that plays out for your workload.
Key differences
Context window: both advertise 256K (~393 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Recency: Ling-2.6-1T is the newer model by about 8 months (released April 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Ling-2.6-1T
Qwen3 235B A22B (2507)
Provider
Ant Group (China)
Alibaba (China)
Released
April 2026
July 21, 2025
Context window
256K (~393 pages)
256K (~393 pages)
Price (in/out)
$0.3/$2.5 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
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 — 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 is the newer of the two.
Fully open weights under the permissive MIT license, unusual for a model this large: Ling-2.6-1T — Qwen3 235B A22B (2507) is comparatively weak here — its 235B weights need roughly 438GB in BF16, far beyond consumer hardware
A companion 'Ring' reasoning variant claims gold-medal-level IMO/CMO math scores using a hybrid linear-attention architecture: Ling-2.6-1T — Qwen3 235B A22B (2507) is comparatively weak here — text-only with no vision, and the absence of a thinking mode caps its hardest reasoning
Deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux): Qwen3 235B A22B (2507) — Qwen3 235B A22B (2507) lists deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux) among its strengths; Ling-2.6-1T does not.
Exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench): Qwen3 235B A22B (2507) — Qwen3 235B A22B (2507) lists exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench) among its strengths; Ling-2.6-1T does not.
Outstanding structured logic — 95.0 on ZebraLogic: Qwen3 235B A22B (2507) — Qwen3 235B A22B (2507) lists outstanding structured logic — 95.0 on ZebraLogic among its strengths; Ling-2.6-1T does not.
Lowest cost at scale: Qwen3 235B A22B (2507) — Its weights are open, so at volume you pay for your own hardware instead of Ling-2.6-1T's $0.3/$2.5 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume: Qwen3 235B A22B (2507) — At Open weight (self-host / free) it undercuts Ling-2.6-1T, and on millions of tokens that margin decides the monthly bill.
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 — It is specifically built for that.
Anyone whose priority is deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux): Qwen3 235B A22B (2507) — That is its strongest area.
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 are real: 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.
Qwen3 235B A22B (2507): where it fits
An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Released July 21, 2025 by Alibaba, it is built for deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux), exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench), outstanding structured logic — 95.0 on ZebraLogic, and no thinking mode, which makes latency and token spend entirely predictable.
Its trade-offs: nearly a year old and superseded — Artificial Analysis now steers users to Qwen3.5-397B instead, text-only with no vision, and the absence of a thinking mode caps its hardest reasoning, coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on, and its 235B weights need roughly 438GB in BF16, far beyond consumer hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
Ling-2.6-1T and Qwen3 235B A22B (2507) overlap enough that the right pick depends on your specific job. Qwen3 235B A22B (2507) costs less per token; and each leads in its own area — 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, Qwen3 235B A22B (2507) for deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux). Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is Ling-2.6-1T or Qwen3 235B A22B (2507) 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, 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 while Qwen3 235B A22B (2507) leans toward deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Ling-2.6-1T or Qwen3 235B A22B (2507)?
Qwen3 235B A22B (2507) is cheaper — $0.3/$2.5 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Both advertise 256K (~393 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Ling-2.6-1T and Qwen3 235B A22B (2507) together?
Yes — a multi-model platform like LumiChats gives you Ling-2.6-1T, Qwen3 235B A22B (2507) 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, Ling-2.6-1T or Qwen3 235B A22B (2507)?
Ling-2.6-1T — released April 2026, about 8 months after Qwen3 235B A22B (2507).
Ling-2.6-1T vs Qwen3 235B A22B (2507)
Ant Group · China | Alibaba · China · Updated June 2026
Quick verdict
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. Pick Qwen3 235B A22B (2507) for deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux) or exceptional multilingual and alignment results (79.2 arena-hard v2, 85.2 writingbench). On a tight budget at scale, Qwen3 235B A22B (2507) is the value pick.
Ling-2.6-1T (Ant Group) and Qwen3 235B A22B (2507) (Alibaba) are two of the models people most often weigh against each other in 2026. 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. Qwen3 235B A22B (2507) is an older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Their biggest split is price, and the breakdown below shows exactly how that plays out for your workload.
Key differences at a glance
▸Context window: both advertise 256K (~393 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
▸Recency: Ling-2.6-1T is the newer model by about 8 months (released April 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Ling-2.6-1T
Qwen3 235B A22B (2507)
Provider
Ant Group (China)
Alibaba (China)
Released
April 2026
July 21, 2025
Context window
256K (~393 pages)
256K (~393 pages)
Price (in/out)
$0.3/$2.5 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
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
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 is the newer of the two.
Fully open weights under the permissive MIT license, unusual for a model this large
Ling-2.6-1T
Qwen3 235B A22B (2507) is comparatively weak here — its 235B weights need roughly 438GB in BF16, far beyond consumer hardware
A companion 'Ring' reasoning variant claims gold-medal-level IMO/CMO math scores using a hybrid linear-attention architecture
Ling-2.6-1T
Qwen3 235B A22B (2507) is comparatively weak here — text-only with no vision, and the absence of a thinking mode caps its hardest reasoning
Deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux)
Qwen3 235B A22B (2507)
Qwen3 235B A22B (2507) lists deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux) among its strengths; Ling-2.6-1T does not.
Exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench)
Qwen3 235B A22B (2507)
Qwen3 235B A22B (2507) lists exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench) among its strengths; Ling-2.6-1T does not.
Outstanding structured logic — 95.0 on ZebraLogic
Qwen3 235B A22B (2507)
Qwen3 235B A22B (2507) lists outstanding structured logic — 95.0 on ZebraLogic among its strengths; Ling-2.6-1T does not.
Lowest cost at scale
Qwen3 235B A22B (2507)
Its weights are open, so at volume you pay for your own hardware instead of Ling-2.6-1T's $0.3/$2.5 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Qwen3 235B A22B (2507)
At Open weight (self-host / free) it undercuts Ling-2.6-1T, and on millions of tokens that margin decides the monthly bill.
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
It is specifically built for that.
Anyone whose priority is deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux)
→ Qwen3 235B A22B (2507)
That is its strongest area.
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 are real: 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.
Qwen3 235B A22B (2507): where it fits
An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Released July 21, 2025 by Alibaba, it is built for deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux), exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench), outstanding structured logic — 95.0 on ZebraLogic, and no thinking mode, which makes latency and token spend entirely predictable.
Its trade-offs: nearly a year old and superseded — Artificial Analysis now steers users to Qwen3.5-397B instead, text-only with no vision, and the absence of a thinking mode caps its hardest reasoning, coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on, and its 235B weights need roughly 438GB in BF16, far beyond consumer hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
Ling-2.6-1T and Qwen3 235B A22B (2507) overlap enough that the right pick depends on your specific job. Qwen3 235B A22B (2507) costs less per token; and each leads in its own area — 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, Qwen3 235B A22B (2507) for deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux). Rather than crowning one, run the same hard task through both once and let the results decide.
Want both Ling-2.6-1T and Qwen3 235B A22B (2507) 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.
Is Ling-2.6-1T or Qwen3 235B A22B (2507) 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, 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 while Qwen3 235B A22B (2507) leans toward deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Ling-2.6-1T or Qwen3 235B A22B (2507)?
Qwen3 235B A22B (2507) is cheaper — $0.3/$2.5 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Both advertise 256K (~393 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Ling-2.6-1T and Qwen3 235B A22B (2507) together?
Yes — a multi-model platform like LumiChats gives you Ling-2.6-1T, Qwen3 235B A22B (2507) 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, Ling-2.6-1T or Qwen3 235B A22B (2507)?
Ling-2.6-1T — released April 2026, about 8 months after Qwen3 235B A22B (2507).
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.