Inkling vs Ling 3.1 Flash
Thinking Machines Lab · US | InclusionAI (Ant Group) · China · Updated June 2026
Quick verdict
Pick Inkling for the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai or a 975b-parameter moe (41b active) with native text, image, and audio reasoning in one model. 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 Inkling if you need self-hosting or data privacy; Ling 3.1 Flash if you want a managed API.
Inkling (Thinking Machines Lab, US) and Ling 3.1 Flash (InclusionAI (Ant Group), China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Inkling is mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control. 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
- ▸Cost model: Inkling ships open weights you can self-host (hardware cost only, no per-token fee), while Ling 3.1 Flash is API-metered at $0.3/$0.9 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
- ▸Context window: Inkling holds 3.8× more — 1M (~1,500 pages) vs 262K tokens (~393 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 3.1 Flash is the newer model by about 3 months (released September 30, 2026), usually meaning fresher training data and capabilities.
- ▸Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | Inkling | Ling 3.1 Flash |
|---|---|---|
| Provider | Thinking Machines Lab (US) | InclusionAI (Ant Group) (China) |
| Released | July 15, 2026 | September 30, 2026 |
| Context window | 1M (~1,500 pages) | 262K tokens (~393 pages) |
| Price (in/out) | Open weight (self-host / free) | $0.3/$0.9 per 1M tokens |
| Open weight? | Yes — self-hostable | No — API only |
| Modalities | text, image, audio, code | text, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
The first model release from Mira Murati's Thinking Machines Lab (ex-OpenAI CTO) - one of the most closely-watched new labs in AI
Inkling
Open weights make this possible at all — Ling 3.1 Flash is API-only, so it cannot leave the vendor's servers.
A 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model
Inkling
Ling 3.1 Flash is comparatively weak here — 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
A dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request
Inkling
Mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control — and it carries the larger 1M context.
Hybrid-reasoning MoE — 560B total parameters, ~25B active per token, aimed at agents, tool use, and long-document work
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 is the newer of the two.
Artificial Analysis Intelligence Index score of 41 — above average for its price tier
Ling 3.1 Flash
Inkling is comparatively weak here — no official hosted API price - available via third-party hosts only at launch
First-party pricing of $0.30/$0.90 per million tokens, with an 80% cache-read discount ($0.06/MTok)
Ling 3.1 Flash
Inkling is comparatively weak here — a brand-new lab's first release - no multi-generation track record yet
Lowest cost at scale
Inkling
Its weights are open, so at volume you pay for your own hardware instead of Ling 3.1 Flash's $0.3/$0.9 per 1M tokens.
Largest single-prompt input
Inkling
Its 1M window is about 3.8× larger than Ling 3.1 Flash's 262K tokens, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Inkling
At Open weight (self-host / free) it undercuts Ling 3.1 Flash, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Inkling
Larger 1M window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Inkling
Open weights let you run it on your own hardware; Ling 3.1 Flash is API-only.
Anyone whose priority is the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai
→ Inkling
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.
An enterprise with regional data-residency rules
→ Inkling or Ling 3.1 Flash
Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Inkling: where it fits
Mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control. Released July 15, 2026 by Thinking Machines Lab, it is built for the first model release from Mira Murati's Thinking Machines Lab (ex-OpenAI CTO) - one of the most closely-watched new labs in AI, a 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model, a dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request, and fully open weights (Apache 2.0) at frontier scale - unusual for a model this large and this new.
Its trade-offs are real: a brand-new lab's first release - no multi-generation track record yet, no official hosted API price - available via third-party hosts only at launch, and independent third-party benchmark verification is still limited given how recently it shipped. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
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. Inkling 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 Inkling 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 pricingFrequently asked questions
Is Inkling or Ling 3.1 Flash 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, Inkling leans toward the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai 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, Inkling or Ling 3.1 Flash?
Inkling 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?
Inkling — 1M vs 262K tokens, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Inkling and Ling 3.1 Flash together?
Yes — a multi-model platform like LumiChats gives you Inkling, 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, Inkling or Ling 3.1 Flash?
Ling 3.1 Flash — released September 30, 2026, about 3 months after Inkling.
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.