inclusionAI releases Ling 3.1 Flash: 560B MoE, 25B active, 262K context, free on OpenRouter

inclusionAI has released Ling 3.1 Flash, a hybrid reasoning mixture-of-experts (MoE) model with 560B total parameters and 25B active per token. It has a 262K-token context window. According to its OpenRouter listing, the model was released on October 2, 2026 and is currently priced at $0 per 1M input tokens and $0 per 1M output tokens.

Key specifications

Spec	Detail
Developer	inclusionAI
Architecture	Mixture-of-experts, hybrid reasoning
Total parameters	560B
Active parameters	25B
Context window	262K tokens
Release date	Oct 2, 2026 (per OpenRouter listing)
Pricing	Free (input and output)
Training cutoff	Not disclosed
Benchmark scores	Not published on the listing

Availability and performance

The model is available through OpenRouter, with NovitaAI as the sole listed provider. OpenRouter's listing shows these figures:

• Latency: 1.12s P50
• Throughput: 66 tokens per second P50
• Provider uptime: 100.00% (NovitaAI)
• OpenRouter availability: 98.93% over the last 24 hours

The listing does not say whether the free pricing is promotional or how long it will last. Paid rates have not been disclosed. The listing also does not itemize supported modalities. Its description refers to a language model, and inclusionAI sells vision capability separately in the Ling 3.0 Flash VL variant.

Position within the Ling family

Ling 3.1 Flash is much larger than earlier "Flash" models in the lineup, according to OpenRouter's catalog:

• Ling 3.0 Flash: 124B total, about 5.1B active
• Ling-2.6-flash: 104B total, 7.4B active
• Ling 3.1 Flash: 560B total, 25B active

That is roughly 4.5x the total parameters and about 5x the active parameters of Ling 3.0 Flash. It sits below the trillion-parameter Ling-2.6-1T and Ring-2.6-1T (63B active) models. Unlike the Ling-2.6 instruct models, it combines instant and reasoning behavior in one hybrid model, as Ling 3.0 Tiny and Ling 3.0 Flash VL also do.

inclusionAI has not published benchmark results for 3.1 Flash on the listing, so its capability claims cannot yet be verified against peers.

What this means

The jump from 5B to 25B active parameters under the "Flash" name suggests inclusionAI is moving that tier toward higher capability, at the expense of the very low serving cost that defined Ling 3.0 Flash ($0.021 input / $0.063 output per 1M tokens). Active parameter count drives inference cost, so paid pricing for 3.1 Flash will probably be well above the 3.0 tier once it is set.

The free listing makes it easy to test now. A 66 tokens-per-second throughput on a single provider is moderate for a model with 25B active parameters. Builders should treat the free tier as an evaluation window rather than a production commitment until pricing and rate limits are published.

The main gap is evidence. Without benchmark scores, a technical report, or a stated training cutoff, the model's standing against other open MoE models at this scale is unknown. Independent evaluations on coding, tool use and long-context tasks will determine whether the larger active footprint is justified.