Ling 3.1 Flash
Large hybrid reasoning model with 560B total and 25B active parameters, long context, and coding/agentic focus; direct and hosted inference routes are listed.
Score calculation
(2 + 5 + 7 + 1 + 7) ÷ 5 = 4.4
5 of six dimensions rated. Unrated dimensions are excluded from the average.
Full scoring method ↗Setup details
InclusionAI Ling-3.1-Flash; excludes separate Ring, Ming, and earlier Ling releases.
What fits your needs
InclusionAI Ling-3.1-Flash; excludes separate Ring, Ming, and earlier Ling releases.
Good fit
Large hybrid reasoning model with 560B total and 25B active parameters, long context, and coding/agentic focus; direct and hosted inference routes are listed.
Tradeoffs to expect
Tradeoffs
MIT, as stated in the official model repository. Weights are downloadable under MIT and a model-specific training-content summary is published. The system is large, relies on custom code, and the summary is not a complete rights audit or full reproducible training recipe.
How to switch
Local setup
The published checkpoint is about 574 GB in BF16. It is a server-scale deployment; quantization changes memory needs but does not make it a consumer-laptop model.
Practical questions
Weights and license
Fetch the exact checkpoint revision and read its own license; sibling and distilled variants may inherit different terms.
Hardware
The published checkpoint is about 574 GB in BF16. It is a server-scale deployment; quantization changes memory needs but does not make it a consumer-laptop model.
Training data
Treat the weight license and public web/data availability separately from evidence of permission for the training data.
This is a practices and disclosure score for the named release, not a request-level energy or emissions estimate.
See the evidence
The official Ling 3.1 release publishes downloadable weights and a self-hosted serving path, but no measured electricity, emissions, or water for that route. The score credits that runtime boundary alone; absent footprint data is not evidence of high consumption.
1 + 1 reporting + 0 electricity + 0 controls = 2/10. This grades documented practices and transparency.
No per-request electricity, emissions, water, serving mix, or independently reviewed footprint is established in the cited sources.
- Reporting1/3
- The official release materials provide downloadable weights and a local serving path for Ling 3.1 Flash; this establishes an operating boundary, not a measured footprint.
- Electricity sourcing0/3
- No model-specific clean-electricity sourcing or hourly power evidence is published in the reviewed sources; zero means unestablished, not dirty electricity.
- Resource controls0/3
- No model-specific documented control or measured physical resource reduction is established by the reviewed release materials. Public weights or smaller variants alone do not prove reduced resource use.
Water disclosure
Water reporting has not been assessed for this choice.
The release provides some training-process information, but not a complete permission ledger for inherited and later training data.
See the evidence
The card links a training-content summary specific to Ling-3.1-Flash and an index describing covered versions and scope. This improves provenance disclosure but does not replace license terms or establish permission for each source. A public weight license governs use of those weights; it is not proof that every training source was permitted.
1 base + 3 source transparency + 1 documented permission + 0 creator controls = 5/10. Weight licensing is not treated as training data permission.
Complete source lists, source-specific rights, inherited-base provenance, synthetic-data generation permissions, and creator removal or retraining remedies remain unestablished.
- Source transparency3/3
- The card links a training-content summary specific to Ling-3.1-Flash and an index describing covered versions and scope. This improves provenance disclosure but does not replace license terms or establish permission for each source.
- Documented permission1/4
- Some named or permissively licensed material is described, but the sources don’t settle permission for the full inherited and later-stage training data.
- Creator controls0/2
- The reviewed model documentation doesn’t show creator permission covering all training data, opt-out, removal, or retraining remedy for this model family.
Privacy depends on where these released weights are run; the model family has no single prompt-retention or training route.
See the evidence
The official release makes weights available for separate deployments, but it does not define one inference host or retention policy for every user. Local and hosted operators may handle prompts differently, so a single numerical privacy grade would misstate this model-level entry.
The user-selected host, region, telemetry, logging, extensions, and provider training terms are outside the checkpoint release and require a route-specific review.
Weights are downloadable under MIT and a model-specific training-content summary is published. The system is large, relies on custom code, and the summary is not a complete rights audit or full reproducible training recipe.
See the evidence
Weights are downloadable under MIT and a model-specific training-content summary is published. The system is large, relies on custom code, and the summary is not a complete rights audit or full reproducible training recipe. This openness rating concerns artifacts and reproducibility only; it does not assess whether the original training data was authorized.
MIT, as stated in the official model repository. Model weights and documentation are available, but limitations described above reduce source-level reproducibility.
The checked release and license apply to the named checkpoints; sibling variants, future versions, and the underlying training data may have different terms or disclosures.
No model-specific worker safeguards are established for InclusionAI (Ant Group) in the reviewed model sources.
See the evidence
The model release pages describe weights and technical properties, not conditions for employees, data annotators, evaluators, moderators, or infrastructure contractors. This is a source-bounded disclosure gap, not evidence of mistreatment.
1 base + 0 conditions + 0 worker voice/remedy + 0 evidence/follow-through = 1/10; this is a disclosure finding, not proof of abuse.
Worker locations, pay and conditions, supplier coverage, voice mechanisms, labor audits, and remediation outcomes are not established by these model-release sources.
- Pay & conditions0/3
- No worker-level conditions or supply-chain safeguards are established in this model-family source set. This is a disclosure gap, not proof of mistreatment.
- Worker voice & remedy0/3
- The model sources do not document a worker voice or remedy mechanism for data, evaluation, or infrastructure labor.
- Evidence & follow-through0/3
- No independent worker-level audit or remediation outcome is established in the reviewed model sources.
No military relationship or release-specific safeguards are established for InclusionAI (Ant Group) in the reviewed model sources.
See the evidence
The official model card and release materials identify the model and license but do not provide a military customer, contract, deployment, or model-specific enforcement record. A relationship may still exist even if this review found no record of it.
7 base + 0 safeguard adjustment = 7/10 (0 of 9 safeguard points; every 3 adds 1). No military relationship was established in the cited sources. This ranks above known defense ties without claiming a verified clean record.
Undisclosed contracts, customer-specific deployment, end-use restrictions, and independent compliance outcomes are not established by this model-family review.
- Use restrictions0/4
- The cited model license and release documents don’t show a model-specific binding ban on military or intelligence use. This is limited to this source set.
- Enforcement & oversight0/3
- No military-specific monitoring, enforcement, or remedy mechanism is established in the reviewed model materials.
- Relationship disclosure0/2
- The model materials do not disclose a model-specific military relationship or military-use record. A relationship may still exist even if this review found no record of it.
The reviewed model-release sources don’t confirm a defense relationship; absence of disclosure is not a clean-record finding.
Military rubric ↗InclusionAI (Ant Group) is not named in the two official BNC campaign lists checked. This is a limited list check, not BDS approval.
See the evidence
The official BNC consumer boycott/pressure guide and No Tech campaign page do not name InclusionAI (Ant Group) among their current targets. BDS uses selective campaigns, so absence from these lists is neutral and should not be read as a clean record or approval.
This check covers only named targets in the two cited BNC campaigns; it does not cover all BDS campaigns, local groups, investments, suppliers, or downstream hosts. Lists and ownership may change.