GPT-5.4 Nano vs Ling 3.1 Flash

OpenAI · US | InclusionAI (Ant Group) · China · Updated June 2026

Quick verdict

Pick GPT-5.4 Nano for cheapest gpt-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work or classification, extraction, ranking and sub-agent execution at scale. 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. On a tight budget at scale, GPT-5.4 Nano is the value pick.

GPT-5.4 Nano (OpenAI, 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. GPT-5.4 Nano is openAI's cheapest GPT-5.4 variant at $0.20/$1.25 with a 400K window — a speed-and-cost tier for high-volume tasks, not deep reasoning. 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 and context window — each quantified below from the models' real specs.

Key differences at a glance

▸Price: GPT-5.4 Nano is about 1.5× cheaper on input ($0.2/$1.25 per 1M tokens vs $0.3/$0.9 per 1M tokens) — modest, but it adds up at steady volume.
▸Context window: GPT-5.4 Nano holds 1.5× more — 400K (~600 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 7 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	GPT-5.4 Nano	Ling 3.1 Flash
Provider	OpenAI (US)	InclusionAI (Ant Group) (China)
Released	March 17, 2026	September 30, 2026
Context window	400K (~600 pages)	262K tokens (~393 pages)
Price (in/out)	$0.2/$1.25 per 1M tokens	$0.3/$0.9 per 1M tokens
Open weight?	No — API only	No — API only
Modalities	text, image, code	text, code
SWE-Bench Verified	Not published	Not published
MRCR v2 @ 1M	Not published	Not published

Who wins what

Cheapest GPT-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work

GPT-5.4 Nano

At $0.2/$1.25 per 1M tokens it undercuts Ling 3.1 Flash ($0.3/$0.9 per 1M tokens), and that gap compounds at volume.

Classification, extraction, ranking and sub-agent execution at scale

GPT-5.4 Nano

OpenAI's cheapest GPT-5.4 variant at $0.20/$1.25 with a 400K window — a speed-and-cost tier for high-volume tasks, not deep reasoning — and it runs cheaper at $0.2/$1.25 per 1M tokens.

A 400K context in the smallest, fastest GPT-5.4 variant

GPT-5.4 Nano

Its 400K window holds about 1.5× more than Ling 3.1 Flash's 262K tokens in a single prompt.

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

Ling 3.1 Flash

GPT-5.4 Nano is comparatively weak here — a nano tier — not built for hard reasoning or frontier coding

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

Ling 3.1 Flash

GPT-5.4 Nano is comparatively weak here — no published SWE-Bench Verified score (OpenAI reported SWE-Bench Pro instead)

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

Ling 3.1 Flash

Lowest cost at scale

GPT-5.4 Nano

At $0.2/$1.25 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.

Largest single-prompt input

GPT-5.4 Nano

Its 400K window is about 1.5× larger than Ling 3.1 Flash's 262K tokens, fitting roughly 600 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

→ GPT-5.4 Nano

At $0.2/$1.25 per 1M tokens it undercuts Ling 3.1 Flash, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ GPT-5.4 Nano

Larger 400K window fits more in one prompt.

Anyone whose priority is cheapest gpt-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work

→ GPT-5.4 Nano

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

→ GPT-5.4 Nano 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.

GPT-5.4 Nano: where it fits

OpenAI's cheapest GPT-5.4 variant at $0.20/$1.25 with a 400K window — a speed-and-cost tier for high-volume tasks, not deep reasoning. Released March 17, 2026 by OpenAI, it is built for cheapest GPT-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work, classification, extraction, ranking and sub-agent execution at scale, a 400K context in the smallest, fastest GPT-5.4 variant, and text and image input for cheap multimodal pipelines.

Its trade-offs are real: a nano tier — not built for hard reasoning or frontier coding, no published SWE-Bench Verified score (OpenAI reported SWE-Bench Pro instead), outclassed by GPT-5.4 and GPT-5.4 Mini whenever a task needs real depth, and image input only — no audio or video. At $0.2 in / $1.25 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

This is less "which is smarter" and more "which ecosystem fits." GPT-5.4 Nano (US) and Ling 3.1 Flash (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. GPT-5.4 Nano is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.

Want both GPT-5.4 Nano 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 GPT-5.4 Nano 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, GPT-5.4 Nano leans toward cheapest gpt-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work 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, GPT-5.4 Nano or Ling 3.1 Flash?

GPT-5.4 Nano is cheaper — $0.2/$1.25 per 1M tokens vs $0.3/$0.9 per 1M tokens, roughly 1.5× apart on input.

Which has the bigger context window?

GPT-5.4 Nano — 400K vs 262K tokens, about 1.5× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both GPT-5.4 Nano and Ling 3.1 Flash together?

Yes — a multi-model platform like LumiChats gives you GPT-5.4 Nano, 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, GPT-5.4 Nano or Ling 3.1 Flash?

Ling 3.1 Flash — released September 30, 2026, about 7 months after GPT-5.4 Nano.

Related comparisons

Claude Sonnet 5.5 vs Ling 3.1 Flash Claude Sonnet 5.5 vs GPT-5.4 Nano GPT-6.1 Sol vs Ling 3.1 Flash GPT-6.1 Sol vs GPT-5.4 Nano Claude Haiku 5.5 vs Ling 3.1 Flash Claude Haiku 5.5 vs GPT-5.4 Nano Mistral Large 4 vs Ling 3.1 Flash Mistral Large 4 vs GPT-5.4 Nano

Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.