Capability
53.6/100
field median 58
#107 of 224 ranked models
Ling 3.0 Flash
Released Jul 23, 2026 — see all recent releases
Decision reading
Ling 3.0 Flash scores 53.6 out of 100 and ranks #107 of 224. This profile shows 17 source-displayable benchmark rows; its strongest eligible category is Instruction Following at #23. No comparable first-party API price is published in the catalog.
Data as of August 22, 2026 · How the score is built
Strongest published evidence
Instruction Following ranks #23. A well-rounded choice across a range of tasks.
Validate before choosing
17 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.
Decision snapshot
Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.
Price
API rate not published
input median $1
No comparable first-party hosted token rate
Speed
397tok/s
field median 91.5 tok/s
First token 7.77 s
Context
262Ktokens
field median 200,000
Maximum output length is tracked separately
Capability shape
Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.
Ling 3.0 Flash category percentile values
- Agentic52nd percentile
- Coding64th percentile
- ReasoningNot eligible
- KnowledgeNot eligible
- MathNot eligible
- MultilingualNot eligible
- MultimodalNot eligible
- Instruction following45th percentile
The dashed outline is median of 6 nearest peers.
Eligible category ranks
- Agentic#66/137
- Coding#52/141
- ReasoningNot ranked
- KnowledgeNot ranked
- MathNot ranked
- MultilingualNot ranked
- MultimodalNot ranked
- Inst. Following#23/41
Top decileTop quartileMid-fieldNot eligible
How much of this is verified
Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.
- Agentic6/6 verified
- Coding4/4 verified
- ReasoningNot measured
- Knowledge3/3 verified
- Math3/3 verified
- MultilingualNot measured
- MultimodalNot measured
- Inst. Following1/1 verified
Verified sourceProvisionalNot measured
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- Not publishedInclusionAI Ling 3.0 Flash model card
- Context window
- 262KInclusionAI Ling 3.0 Flash model card
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- Not sourced yet
- Output modalities
- Not sourced yet
- Parameters
- Not sourced yet
- Availability
- InclusionAI publishes the BF16 checkpoint under the MIT License on Hugging Face. The model card documents SGLang and vLLM deployment, thinking mode enabled by default, and a 262,144-token serving context.
- Cloud regions
- Not tracked yet
- Lifecycle
- Current
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Not documented in the pricing recordInclusionAI Ling 3.0 Flash model card
- Self-host
- Open weights available; hardware estimate not sourced
- Rate limits
- Not tracked yet
Deployment options
Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.
Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.
Estimate VRAM from known parametersCategory score record
Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticRank #66 of 137Percentile 52ndWeight 22%6 benchmarksVerified | 48.9 | #66 of 137 | 52nd | 22% | 6 benchmarks | Verified |
| CodingRank #52 of 141Percentile 64thWeight 20%4 benchmarksVerified | 53.5 | #52 of 141 | 64th | 20% | 4 benchmarks | Verified |
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | Not ranked | Not available | 17% | 0 benchmarks | Not measured |
| KnowledgeRank Not rankedWeight 12%3 benchmarksVerified | 34.3 | Not ranked | Not available | 12% | 3 benchmarks | Verified |
| MathRank Not rankedWeight 5%3 benchmarksVerified | 73.3 | Not ranked | Not available | 5% | 3 benchmarks | Verified |
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | Not ranked | Not available | 7% | 0 benchmarks | Not measured |
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | Not ranked | Not available | 12% | 0 benchmarks | Not measured |
| Inst. FollowingRank #23 of 41Percentile 45thWeight 5%1 benchmarkVerified | 78.8 | #23 of 41 | 45th | 5% | 1 benchmark | Verified |
Benchmark ledger
Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.
Coding4 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| SciCodeScientific Code Benchmark | Score41.2% | Versus best verified row Best verified: Sakana Fugu · 60.1% | Gap18.9 behind | WeightWeighted 16% | Provider exact |
| SWE-bench Pro | Score56.6% | Versus best verified row Best verified: Claude Mythos 5 · 80.3% | Gap23.7 behind | WeightWeighted 10% | Provider exact |
| SWE Multilingual | Score72.4% | Versus best verified row Best verified: Claude Opus 5 · 89.5% | Gap17.1 behind | WeightDisplay only | Provider exact |
| LiveCodeBench v5 | Score82.8% | Versus best verified row Best verified: Ling 3.0 Flash · 82.8% | GapBest verified | WeightDisplay only | Provider exact |
Agentic6 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| BrowseComp | Score72.2% | Versus best verified row Best verified: GPT-5.6 Sol · 92.2% | Gap20 behind | WeightWeighted 28% | Provider exact |
| MCP Atlas | Score65.5% | Versus best verified row Best verified: Muse Spark 1.1 · 88.1% | Gap22.6 behind | WeightDisplay only | Provider exact |
| skillsBench | Score44.8% | Versus best verified row Best verified: Qwen3.8 Max · 70.2% | Gap25.4 behind | WeightDisplay only | Provider exact |
| BFCL v4Berkeley Function Calling Leaderboard v4 | Score73.0% | Versus best verified row Best verified: Qwen3.7 Max · 75.0% | Gap2 behind | WeightDisplay only | Provider exact |
| WideResearch | Score73.6% | Versus best verified row Best verified: Qwen3.8 Max · 81.9% | Gap8.3 behind | WeightDisplay only | Provider exact |
| DRACOData Research and Analysis with Complex Operations | Score70.4% | Versus best verified row Best verified: Claude Opus 5 · 88.6% | Gap18.2 behind | WeightDisplay only | Provider exact |
Knowledge3 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| HLEHumanity's Last Exam | Score22.7% | Versus best verified row Best verified: Claude Opus 5 · 64.7% | Gap42 behind | WeightWeighted 45% | Provider exact |
| GPQAGraduate-Level Google-Proof Q&A | Score85.0% | Versus best verified row Best verified: Sakana Fugu-Ultra · 95.5% | Gap10.5 behind | WeightWeighted 7% | Provider exact |
| GPQA-DGPQA Diamond | Score85.0% | Versus best verified row Best verified: Sakana Fugu-Ultra · 95.5% | Gap10.5 behind | WeightDisplay only | Provider exact |
Math3 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| AIME26AIME 2026 | Score93.2% | Versus best verified row Best verified: GLM-5.2 · 99.2% | Gap6 behind | WeightWeighted 25% | Provider exact |
| HMMT Feb 2026Harvard-MIT Mathematics Tournament February 2026 | Score87.0% | Versus best verified row Best verified: Qwen3.7 Max · 97.1% | Gap10.1 behind | WeightWeighted 25% | Provider exact |
| IMOAnswerBench | Score83.7% | Versus best verified row Best verified: dots3-note Preview · 90.9% | Gap7.2 behind | WeightDisplay only | Provider exact |
Inst. Following1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| IFBenchInstruction Following Benchmark | Score74.5% | Versus best verified row Best verified: MAI-Thinking-1 · 85% | Gap10.5 behind | WeightWeighted 65% | Provider exact |
Lineage
The sequence follows explicit supersedes links. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Apr 21, 2026
Ling 2.6 FlashNot publicly ranked · Price not listed
Jul 23, 2026 · you are here
Ling 3.0 FlashScore 53.6 · Price not listed
Flash
How to read this profile
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
Ling 3.0 Flash ranks #107 of 224 on the public leaderboard with a score of 53.59/100. It does not yet have enough sourced coverage for a verified position.
Ling 3.0 Flash is a open weight model with a 262K context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.
InclusionAI publishes the BF16 checkpoint under the MIT License on Hugging Face. The model card documents SGLang and vLLM deployment, thinking mode enabled by default, and a 262,144-token serving context.
Official exact-value snapshot from InclusionAI's Ling-3.0-flash Hugging Face model card. The final checkpoint is a 124B-total, 5.1B-active hybrid-linear MoE with a 256K training schedule and 262,144-token serving context. BenchLM keeps Terminal-Bench 2.1 separate from the weighted Terminal-Bench 2.0 lane, stores the card's July 2024-May 2025 LiveCodeBench window on the v5 lane, and maps only named rows supported by the current schema. BrowseComp uses the single-agent with-context result; the separate multi-agent result is not collapsed into it. Internal or currently unsupported rows remain outside the scored fields.
Ling 3.0 Flash sits in the Ling 3.0 family with Ling 3.0 Flash FP8. Its explicit predecessor is Ling 2.6 Flash. 17 of 402 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Instruction Following at #23, while its lowest eligible position is Agentic at #66. a well-rounded choice across a range of tasks.
Radar
Full release historyLing 3.0 Flash release history
Ling 3.0 Flash released
InclusionAI · Model release
Source confirmed
Frequently asked questions
How does Ling 3.0 Flash perform overall in AI benchmarks?
Ling 3.0 Flash ranks #107 out of 224 models on the public BenchAlign leaderboard, with a score of 53.59/100. Its evidence status is Estimated, and this profile shows 17 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.
Is Ling 3.0 Flash good for knowledge and understanding?
Ling 3.0 Flash has source-displayable benchmark coverage for knowledge and understanding, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is Ling 3.0 Flash good for coding and programming?
Ling 3.0 Flash ranks #52 out of 141 eligible models for coding and programming, with a public category score of 53.5/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Ling 3.0 Flash good for mathematics?
Ling 3.0 Flash has source-displayable benchmark coverage for mathematics, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is Ling 3.0 Flash good for agentic tool use and computer tasks?
Ling 3.0 Flash ranks #66 out of 137 eligible models for agentic tool use and computer tasks, with a public category score of 48.9/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Ling 3.0 Flash good for instruction following?
Ling 3.0 Flash ranks #23 out of 41 eligible models for instruction following, with a public category score of 78.8/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Ling 3.0 Flash open source?
Ling 3.0 Flash is an open-weight model from InclusionAI. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.
Which sibling models are related to Ling 3.0 Flash?
Ling 3.0 Flash belongs to the Ling 3.0 family. Related tracked variants include Ling 3.0 Flash FP8. A sibling link indicates shared lineage or a documented configuration relationship; it does not mean the variants have identical pricing, context limits, benchmark evidence, or deployment behavior. Compare before switching.
Does Ling 3.0 Flash have full benchmark coverage on BenchLM?
No. Ling 3.0 Flash currently has 33 source-displayable rows across 402 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.
What is the context window size of Ling 3.0 Flash?
Ling 3.0 Flash has a documented context window of 262K. That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.
Last updated August 22, 2026. Runtime fields remain blank until a sourced snapshot exists.
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