BenchLeader

Ling 3.0 Flash

Ling 3.0 Flash is an Ant Group open-weights reasoning model, released 4 Aug 2026. Its best configuration ranks #303 of 785 on the BenchLeader Index at 52.8 ±3.6, in the upper half. The ± is the point: 288 other configurations score within that range, so they and this one cannot be told apart on quality alone — price and speed are what separate them. It scores highest in long context (61) and lowest in reasoning (44). At $0.115 per million tokens blended it is among the cheapest fifth of ranked models. Output speed of 320 tokens per second puts it in the fastest quarter, with a first answer in 9.1 s. Last measured 13 Oct 2026.

Blended price
$0.115/M
$0.080 in · $0.220 out
Output speed
320 tok/s
measured by Artificial Analysis
First answer
9.07 s
first token 0.86 s
Context
262k
Full answer
11 s
median, reasoning included
Cost per run
–
Released
4 Aug 2026
How it scores by categoryDashed line = average model (50). One step of 15 = one standard deviation.
  1. Overall index53
  2. Reasoning44
  3. Coding61
  4. Agents & tools50
  5. Knowledge53
  6. Long context61
  7. Composite52

Versions

Ant Group has shipped 3 models under this name. Each is ranked on its own results; a newer version often has fewer results so far, which holds its index nearer the average until more arrive.

ModelReleasedIndexRank
Ling 3.1 Flash1 Oct 2026––
Ling 3.0 Flashthis page4 Aug 202652.8#303
Ling Flash 2.017 Sept 202544.4#538

How its index has moved

9 Sept 2026 to 18 Oct 2026
495458

The index is recomputed from scratch every day, so a line moves when a new benchmark result lands, when a publisher revises a score, or when the models it is normalised against change. Early movement usually means the score is still settling.

Benchmark results

See all LLM benchmarks. One column per reasoning effort. Rank is among every configuration of every model on that benchmark. Hover a score for the run it came from.

Reasoning

Benchmarknot statedSourceTrend
GPQA Diamond (AA)not in index85.5%#125Artificial Analysis
Humanity's Last Exam (AA)not in index23.7%#192Artificial Analysis
GPQA Diamond (Vals)not in index84.8%#48Vals AI
CritPt1.7%#206Artificial Analysis

Coding

Benchmarknot statedSourceTrend
SciCode (AA)not in index42.0%#163Artificial Analysis
LiveCodeBench84.0%#42Vals AI
SWE-bench (Vals)not in index65.2%#70Vals AI
Code Migrationnot in index0.0%#75Vals AI
Vibe Code Bench v1.1not in index2.9%#107Vals AI
Terminal-Bench 4.0 (AA)not in index0.0%#152Artificial Analysis
Terminal-Bench 2.1 (AA)not in index55.4%#107Artificial Analysis

Agents & tools

Benchmarknot statedSourceTrend
Terminal-Bench 2.1 (Vals)50.2%#57Vals AI
τ³-Banking (AA)27.2%#77Artificial Analysis
Finance Agent v2not in index30.3%#69Vals AI
Harvey's Legal Agent Benchmark v1not in index1.3%#52Vals AI
Legal Research Benchnot in index0.0%#80Vals AI
SkillsBenchnot in index27.9%#32Vals AI
AutomationBenchnot in index3.2%#173Artificial Analysis
GDP.pdf (AA)not in index5.4%#159Artificial Analysis

Knowledge

Benchmarknot statedSourceTrend
AA-Omniscience-17.9#236Artificial Analysis
MMLU-Pro82.0%#76Vals AI
LegalBench79.7%#91Vals AI
CorpFin61.9%#50Vals AI
TaxEval70.7%#85Vals AI
Excel Modeling Benchmarknot in index25.0%#66Vals AI
MedCodenot in index32.3%#87Vals AI
MedScribenot in index80.9%#52Vals AI
Public Benefits Bench v1.1not in index50.7%#44Vals AI
AA-Omniscience: accuracynot in index18.2%#344Artificial Analysis
AA-Omniscience: non-hallucinationnot in index56.0%#91Artificial Analysis

Long context

Benchmarknot statedSourceTrend
AA-LCR73.0%#182Artificial Analysis
MLCRnot in index5.0%#144Artificial Analysis

Composite

Benchmarknot statedSourceTrend
AA Intelligence Index v4.3.220.1#235Artificial Analysis

Where to run it

Every provider serving this model through OpenRouter, with throughput and first-token latency measured on live traffic over the last 30 minutes and each provider’s own price. Purple marks the best in each column.

ProviderSpeedFirst tokenInput $/MOutput $/MContextQuantisation
NovitaAI124 tok/s0.80 s$0.021$0.063262k–
DeepInfra36 tok/s1.18 s$0.060$0.180131kbf16

Price history

The price above, per 1M tokens, every day it changed.

$0.00$0.061$0.121$0.182$0.242Sept 26Oct 26
input outputnow $0.080 in · $0.220 out

What a task costs

Estimates from list price, output speed and time to first answer for the best configuration. “With caching” assumes three-quarters of the input is served from the prompt cache at $0.012 per 1M. Reasoning tokens are not modelled.

WorkloadTokens in / outCostWith cachingTime
Chat reply400 / 300$0.0001$0.000110.0 s
Summarize a 30-page report12,000 / 600$0.0011$0.000510.9 s
Code edit6,000 / 1,500$0.0008$0.000513.8 s
Agentic coding session60,000 / 4,000$0.0057$0.002621.6 s
Structured extraction2,000 / 200$0.0002$0.00019.7 s

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See also

Questions people ask

What are Ling 3.0 Flash's benchmark scores?
As of 24 Oct 2026, Ling 3.0 Flash scores 52.8 ±3.6 on the BenchLeader Index, strongest in long context (61) and weakest in reasoning (44). That rests on 11 independent benchmark results, and every score on this page links to its publisher.
How does Ling 3.0 Flash rank?
Ling 3.0 Flash is #303 of 785 ranked configurations on 24 Oct 2026. 288 other configurations score within its margin, so the exact position matters less than the band. The leader is Claude Opus 5.5 (max effort) at 72.6.
How much does Ling 3.0 Flash cost per 1M tokens?
As of 24 Oct 2026, Ling 3.0 Flash costs $0.080 per 1M input tokens and $0.220 per 1M output tokens, $0.115 blended (three input tokens per output token). The list price last changed on 9 Sept 2026.
When was Ling 3.0 Flash released?
Ant Group released Ling 3.0 Flash on 4 Aug 2026.

Data as of 24 Oct 2026. Compare with another model.

Cite as: BenchLeader, “Ling 3.0 Flash: benchmarks, pricing, speed and rank”, data as of 24 Oct 2026, https://www.benchleader.com/models/ling-3-0-flash.