Ling 3.0 Flash model profile
Evidence snapshot:
Ling 3.0 Flash is a InclusionAI model profile with a 262,144-token context window. The dated catalog snapshot records text modalities and interfaces for prompt caching, reasoning controls, structured output, text generation, tool calling.
Ling-3.0-flash is a 124B-parameter Mixture-of-Experts (MoE) model, with approximately 5.1B parameters activated per token. The model is designed with token efficiency and production-scale agentic inference as key priorities, enabling developers to complete more useful work within constrained token, latency, and serving-cost budgets.
Ling 3.0 Flash is a reference profile: InclusionAI publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.
Ling 3.0 Flash is an independent knowledge profile. It does not claim that Kendr hosts the model, quote a Kendr customer price, promise API availability, or advertise a Kendr routing receipt.
Ling 3.0 Flash accepts text and returns text. The snapshot records prompt caching, reasoning controls, structured output, text generation, and tool calling as capabilities or interfaces.
The dated reference snapshot lists $0.021 input and $0.063 output per million tokens. Cached input is $0.0042 per million tokens.
The 2026-09-06 snapshot includes 3 Artificial Analysis indexes and AutoExacto results for 4 provider observations. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable.
Ling 3.0 Flash ranked #75 in the cited trailing-7-day third-party catalog usage dataset as of 2026-09-06. Observed within the cited trailing-7-day third-party catalog usage dataset; this is not global AI market share.
Profile data was reviewed for the 2026-09-06 snapshot. Claims retain their source dates, and unavailable fields are shown as unavailable instead of estimated.
Profile facts
- Profile type
- Model research profile
- Provider or publisher
- InclusionAI
- Context window
- 262,144 tokens
- Snapshot date
- 2026-09-06
- Knowledge cutoff
- Not disclosed
- Reference catalog ID
- inclusionai/ling-3.0-flash
When to pick Ling 3.0 Flash
Ling 3.0 Flash is a reference profile: InclusionAI publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.
Each condition below is derived from this model's own values in the 2026-09-06 snapshot, compared only against models publishing the same field.
- Reach for it when cost is the binding constraint. At $0.021 in and $0.063 out per 1M tokens, combined token price sits in the cheapest quarter of the 146 models publishing both rates in this snapshot.
- Reach for it when the model has to call tools. Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose.
- Look elsewhere when you need to call it through Kendr today. This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr.
- Look elsewhere when the task is hard reasoning. Its 27.4 Artificial Analysis intelligence index is below the median of the 32 models carrying that field. Scores from different suites are not interchangeable, so treat this as one signal rather than a ranking.
Context, modalities, and identity
Ling 3.0 Flash accepts text and returns text. The snapshot records prompt caching, reasoning controls, structured output, text generation, and tool calling as capabilities or interfaces.
- Provider or publisher
- InclusionAI
- Context window
- 262,144 tokens
- Maximum output
- 32,768 tokens
- Input modalities
- text
- Output modalities
- text
- Knowledge cutoff
- Not disclosed
Model overview
Ling-3.0-flash is a 124B-parameter Mixture-of-Experts (MoE) model, with approximately 5.1B parameters activated per token. The model is designed with token efficiency and production-scale agentic inference as key priorities, enabling developers to complete more useful work within constrained token, latency, and serving-cost budgets.
- Reference model ID
- inclusionai/ling-3.0-flash
- Canonical version
- inclusionai/ling-3.0-flash-20260723
- Hugging Face ID
- inclusionAI/Ling-3.0-flash
Capabilities and supported controls
Capabilities and parameters are reported from the dated catalog and source records; their presence does not guarantee identical behavior across every provider route.
- Supported parameters
- frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, temperature, tool_choice, tools, top_k, top_logprobs, top_p
- prompt caching
- reasoning controls
- structured output
- text generation
- tool calling
Dated pricing snapshot
The dated reference snapshot lists $0.021 input and $0.063 output per million tokens. Cached input is $0.0042 per million tokens.
These are dated third-party reference prices, not Kendr prices or an availability offer. Routes, tiers, caching, region, and provider policy can change the landed price.
- Price date
- 2026-09-06
- Input
- $0.021 per 1M tokens
- Cached input
- $0.0042 per 1M tokens
- Output
- $0.063 per 1M tokens
Provider routes, performance, and uptime
The snapshot retains 2 provider endpoints. Provider prices, context limits, p50 performance, and uptime can differ by route and are not Kendr guarantees.
- Active provider endpoints
- 2
- Best p50 latency
- 0.74 s
- Best p50 throughput
- 53 tok/s
- Availability with routing
- 99.90% over the sampled window
- Availability without routing
- 66.76% over the sampled window
- Performance date
- 2026-09-06
| Provider | Quantization | Input / 1M | Output / 1M | Cache read / 1M | Context | p50 latency | p50 throughput | Uptime (1d) |
|---|---|---|---|---|---|---|---|---|
| NovitaAI | unknown | $0.021 / 1M | $0.063 / 1M | $0.0042 / 1M | 262,144 tokens | 0.74 s | 44 tok/s | 100.00% |
| DeepInfra | bf16 | $0.06 / 1M | $0.18 / 1M | $0.012 / 1M | 131,072 tokens | 0.93 s | 53 tok/s | 95.82% |
Benchmark evidence and limitations
The 2026-09-06 snapshot includes 3 Artificial Analysis indexes and AutoExacto results for 4 provider observations. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable.
Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.
- Intelligence Index
- 27.4
- Coding Index
- 50.6
- Agentic Index
- 21.1
- AutoExacto coverage
- 4 provider observations over 32 days
AutoExacto provider benchmarks
Rolling provider observations from the cited third-party benchmark view. The lookback is 32 days; GPQA Diamond and TAU-Bench Airline measure different abilities and should not be blended into one score.
| Provider | GPQA Diamond | TAU-Bench Airline | Runs |
|---|---|---|---|
| auto-routing | 72.96% | — | 1 |
| DeepInfra | 65.75% | 57.33% | 2 |
| Novita Fast | 75.42% | 74% | 1 |
| NovitaAI | 76.08% | 74.67% | 2 |
Popularity and market context
Ling 3.0 Flash ranked #75 in the cited trailing-7-day third-party catalog usage dataset as of 2026-09-06. Observed within the cited trailing-7-day third-party catalog usage dataset; this is not global AI market share.
- Third-party catalog rank
- #75
- Global market share
- Not inferred
- Observation date
- 2026-09-06
Frequently asked questions
When should I use Ling 3.0 Flash?
Ling 3.0 Flash is a reference profile: InclusionAI publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call. Reach for it when cost is the binding constraint: At $0.021 in and $0.063 out per 1M tokens, combined token price sits in the cheapest quarter of the 146 models publishing both rates in this snapshot. Reach for it when the model has to call tools: Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose. Look elsewhere when you need to call it through Kendr today: This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr. Look elsewhere when the task is hard reasoning: Its 27.4 Artificial Analysis intelligence index is below the median of the 32 models carrying that field. Scores from different suites are not interchangeable, so treat this as one signal rather than a ranking.
What is Ling 3.0 Flash?
Ling 3.0 Flash is a InclusionAI model profile with a 262,144-token context window. The dated catalog snapshot records text modalities and interfaces for prompt caching, reasoning controls, structured output, text generation, tool calling.
What context window does Ling 3.0 Flash have?
The dated profile lists 262,144 tokens of context and up to 32,768 output tokens. Provider routes, variants, and runtime configuration can impose lower effective limits.
What benchmark evidence is available for Ling 3.0 Flash?
The 2026-09-06 snapshot includes 3 Artificial Analysis indexes and AutoExacto results for 4 provider observations. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable. Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.
Is Ling 3.0 Flash available through Kendr?
This is an independent knowledge profile, not a Kendr-hosted availability claim. Check Kendr's live public model API for currently enabled Kendr aliases.
Does the popularity rank represent Ling 3.0 Flash's global market share?
Ling 3.0 Flash ranked #75 in the cited trailing-7-day third-party catalog usage dataset as of 2026-09-06. Observed within the cited trailing-7-day third-party catalog usage dataset; this is not global AI market share. No global market-share percentage is inferred when the source does not publish one.
Sources and evidence dates
- Ling 3.0 Flash third-party catalog record (catalog, checked 2026-09-06)
- Third-party model catalog methodology (methodology, checked 2026-09-06)
- Artificial Analysis capability indices methodology (benchmark, checked 2026-09-06)
- Ling 3.0 Flash third-party catalog record (benchmark, checked 2026-09-06)