GLM 5 vs Ling 3.1 Flash

Z.ai · China  |  InclusionAI (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 3.1 Flash for hybrid-reasoning moe — 560b total parameters, ~25b active per token, aimed at agents, tool use, and long-document work or artificial analysis intelligence index score of 41 — above average for its price tier. Choose GLM 5 if you need self-hosting or data privacy; Ling 3.1 Flash if you want a managed API.

GLM 5 (Z.ai) and Ling 3.1 Flash (InclusionAI (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 3.1 Flash is inclusionAI's (Ant Group) September 30, 2026 hybrid-reasoning MoE model — 560B parameters aimed at agents and long documents, launched as a free trial with weights still unreleased. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecGLM 5Ling 3.1 Flash
ProviderZ.ai (China) InclusionAI (Ant Group) (China)
ReleasedFebruary 12, 2026 September 30, 2026
Context window200K (~300 pages) 262K tokens (~393 pages)
Price (in/out)$1/$3.2 per 1M tokens $0.3/$0.9 per 1M tokens
Open weight?Yes — self-hostable No — API only
Modalitiestext, code text, code
SWE-Bench Verified77.8% Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Agentic planning and long-horizon coding workflows

GLM 5

Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding — and its weights are open while Ling 3.1 Flash is API-only.

Complex systems design and backend reasoning

GLM 5

GLM 5 lists complex systems design and backend reasoning among its strengths; Ling 3.1 Flash does not.

Iterative self-correction on autonomous tasks

GLM 5

GLM 5 lists iterative self-correction on autonomous tasks among its strengths; Ling 3.1 Flash does not.

Hybrid-reasoning MoE — 560B total parameters, ~25B active per token, aimed at agents, tool use, and long-document work

Ling 3.1 Flash

Its 262K tokens window holds about 1.3× more than GLM 5's 200K in a single prompt.

Artificial Analysis Intelligence Index score of 41 — above average for its price tier

Ling 3.1 Flash

At $0.3/$0.9 per 1M tokens it undercuts GLM 5 ($1/$3.2 per 1M tokens), and that gap compounds at volume.

First-party pricing of $0.30/$0.90 per million tokens, with an 80% cache-read discount ($0.06/MTok)

Ling 3.1 Flash

InclusionAI's (Ant Group) September 30, 2026 hybrid-reasoning MoE model — 560B parameters aimed at agents and long documents, launched as a free trial with weights still unreleased — and it runs cheaper at $0.3/$0.9 per 1M tokens.

Lowest cost at scale

Ling 3.1 Flash

At $0.3/$0.9 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.

Largest single-prompt input

Ling 3.1 Flash

Its 262K tokens 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 3.1 Flash

At $0.3/$0.9 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 3.1 Flash

Larger 262K tokens window fits more in one prompt.

A team with data-privacy or self-hosting needs

→ GLM 5

Open weights let you run it on your own hardware; Ling 3.1 Flash is API-only.

Anyone whose priority is agentic planning and long-horizon coding workflows

→ GLM 5

It is specifically built for that.

Anyone whose priority is hybrid-reasoning moe — 560b total parameters, ~25b active per token, aimed at agents, tool use, and long-document work

→ Ling 3.1 Flash

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 3.1 Flash: where it fits

InclusionAI's (Ant Group) September 30, 2026 hybrid-reasoning MoE model — 560B parameters aimed at agents and long documents, launched as a free trial with weights still unreleased. Released September 30, 2026 by InclusionAI (Ant Group), it is built for hybrid-reasoning MoE — 560B total parameters, ~25B active per token, aimed at agents, tool use, and long-document work, artificial Analysis Intelligence Index score of 41 — above average for its price tier, first-party pricing of $0.30/$0.90 per million tokens, with an 80% cache-read discount ($0.06/MTok), and predecessor Ling 3.0 Flash shipped MIT-licensed open weights, suggesting a similar path once 3.1 clears review.

Its trade-offs: shipped as an announcement plus a two-week free trial rather than a full release — no weights, no model card, and limited independent verification at launch, context window currently served at 262K tokens even though InclusionAI's stated target is 1M — the larger window isn't live yet, not yet open-weight despite InclusionAI saying it plans to open-source the model 'soon', and announced via WeChat rather than a formal English-language technical report, so some specs are vendor-reported only. At $0.3 in / $0.9 out per million tokens, it sits in the budget price band.

The bottom line for this matchup

The defining split here is open vs. closed. GLM 5 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Ling 3.1 Flash gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.

Want both GLM 5 and Ling 3.1 Flash 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 pricing

Frequently asked questions

Is GLM 5 or Ling 3.1 Flash better for coding?

Public SWE-Bench figures are not available for Ling 3.1 Flash, 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 3.1 Flash leans toward hybrid-reasoning moe — 560b total parameters, ~25b active per token, aimed at agents, tool use, and long-document work, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, GLM 5 or Ling 3.1 Flash?

GLM 5 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Ling 3.1 Flash is API-metered at $0.3/$0.9 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.

Which has the bigger context window?

Ling 3.1 Flash — 262K tokens 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 3.1 Flash together?

Yes — a multi-model platform like LumiChats gives you GLM 5, Ling 3.1 Flash 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 3.1 Flash?

Ling 3.1 Flash — released September 30, 2026, about 8 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.