Ling 3.0 Flash vs Qwen3 14B
Ling 3.0 Flash leads Qwen3 14B on the Artificial Analysis Intelligence Index, 20.1 to 6.7, and Ling 3.0 Flash costs 1.9x less per output token ($0.1215 vs $0.23 per million). Updated October 22, 2026; 5 benchmarks have a result for both.
Ling 3.0 Flash or Qwen3 14B: which to pick
Only Ling 3.0 Flash has a coding score, 29.6 on the Vibe Coding Index; Qwen3 14B has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 5
- Ling 3.0 Flash only
- 19
- Qwen3 14B only
- 15
- Like-for-like categories
- 4 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only Ling 3.0 Flash has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Ling 3.0 Flash
Ling 3.0 Flash leads, 20.1 against 6.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Ling 3.0 Flash has a AA Agentic Index result, 19.3.
Directional only
Reasoning
GPQA Diamond
No call
Only Ling 3.0 Flash has a GPQA Diamond result, 84.8%.
Directional only
Knowledge
MMLU Pro
No call
Only Ling 3.0 Flash has a MMLU Pro result, 82.0%.
Directional only
Long documents
Listed context window
Ling 3.0 Flash
Ling 3.0 Flash takes 262K tokens against 41K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash
Ling 3.0 Flash $0.0001, Qwen3 14B $0.0002 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Ling 3.0 Flash has the number, $0.05.
Directional only
At a glance
- Coding index
- Ling 3.0 Flash 29.6 from 2 benchmarks, Qwen3 14B not benchmarked.
- Widest category
- General, 73.0 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Ling 3.0 Flash 73.0% against Qwen3 14B 0.0%, 73.0 points apart on a 0 to 100 scale.
- Output price
- Ling 3.0 Flash $0.1215 against Qwen3 14B $0.23 per million output tokens, 1.9x apart.
- Context window
- Ling 3.0 Flash takes 262K tokens, Qwen3 14B 41K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | Ling 3.0 Flash | Bars | Qwen3 14B | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 5 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | Ling 3.0 Flash | Qwen3 14B | Leader |
|---|---|---|---|
| AA Coding Index | 50.6 | Not measured | Only Ling 3.0 Flash measured |
| LiveCodeBench (Vals AI) | 84.0% | Not measured | Only Ling 3.0 Flash measured |
| SWE-bench Verified (Vals AI) | 65.2% | Not measured | Only Ling 3.0 Flash measured |
| Vibe Code Bench (Vals AI) | 2.9% | Not measured | Only Ling 3.0 Flash measured |
| Code migration (Vals AI) | 0.0% | Not measured | Only Ling 3.0 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 50.2% | Not measured | Only Ling 3.0 Flash measured |
| SkillsBench (Vals AI) | 27.9% | Not measured | Only Ling 3.0 Flash measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 52.3% | Only Qwen3 14B measured |
| SciCode | 42.0% | 30.7% | Ling 3.0 Flash |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 5.3% | Only Qwen3 14B measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Ling 3.0 Flash | Qwen3 14B | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.1 | 6.7 | Ling 3.0 Flash |
| Epoch Capabilities Index | Not measured | 138.2 | Only Qwen3 14B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Ling 3.0 Flash | Qwen3 14B | Leader |
|---|---|---|---|
| AA Agentic Index | 19.3 | Not measured | Only Ling 3.0 Flash measured |
| Finance Agent (Vals AI) | 30.3% | Not measured | Only Ling 3.0 Flash measured |
| Legal Research Bench (Vals AI) | 0.0% | Not measured | Only Ling 3.0 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 25.0% | Not measured | Only Ling 3.0 Flash measured |
| τ²-Bench Telecom | Not measured | 34.5% | Only Qwen3 14B measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 41.0% | Only Qwen3 14B measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Ling 3.0 Flash | Qwen3 14B | Leader |
|---|---|---|---|
| GPQA Diamond | 84.8% | Not measured | Only Ling 3.0 Flash measured |
| GPQA Diamond (Artificial Analysis run) | 85.5% | 60.4% | Ling 3.0 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 23.7% | 4.5% | Ling 3.0 Flash |
| GPQA Diamond (Epoch AI run) | Not measured | 63.8% | Only Qwen3 14B measured |
| Kagi LLM Benchmark | Not measured | 49.1% | Only Qwen3 14B measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Ling 3.0 Flash | Qwen3 14B | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 58.0 | Only Qwen3 14B measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 58.0% | Only Qwen3 14B measured |
| OTIS Mock AIME | Not measured | 66.4% | Only Qwen3 14B measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Ling 3.0 Flash | Qwen3 14B | Leader |
|---|---|---|---|
| MMLU Pro | 82.0% | Not measured | Only Ling 3.0 Flash measured |
| LegalBench (Vals AI) | 79.7% | Not measured | Only Ling 3.0 Flash measured |
| CorpFin (Vals AI) | 61.9% | Not measured | Only Ling 3.0 Flash measured |
| TaxEval (Vals AI) | 70.7% | Not measured | Only Ling 3.0 Flash measured |
| MedCode (Vals AI) | 32.3% | Not measured | Only Ling 3.0 Flash measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 77.4% | Only Qwen3 14B measured |
| Neither model has a result on the other 14 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash | Qwen3 14B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Ling 3.0 Flash | Qwen3 14B | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 50.7% | Not measured | Only Ling 3.0 Flash measured |
| MedScribe (Vals AI) | 80.9% | Not measured | Only Ling 3.0 Flash measured |
| IFBench | Not measured | 40.5% | Only Qwen3 14B measured |
| Long context reasoning (AA-LCR) | 73.0% | 0.0% | Ling 3.0 Flash |
| DTBench | Not measured | 64.0% | Only Qwen3 14B measured |
| LMCA | Not measured | 18.2% | Only Qwen3 14B measured |
| Fiction.LiveBench | Not measured | 62.5% | Only Qwen3 14B measured |
| Neither model has a result on the other 41 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash | Qwen3 14B | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Ling 3.0 Flash costs less
Repository review
50K input + 3K output tokens
Ling 3.0 Flash costs less
Agent loop
200K input + 10K output tokens
Ling 3.0 Flash costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | inclusionAI Ling 3.0 Flash | Qwen3 14B |
|---|---|---|
| Maker | inclusionAI | Qwen |
| Weights | Open weights | Open weights |
| Released | July 23, 2026 | April 28, 2025 |
| Context window | 262K | 41K |
| Input $/Mtok | $0.0405 | $0.11 |
| Output $/Mtok | $0.1215 | $0.23 |
| Coding index | 29.6 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | $0.05 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Ling 3.0 Flash better than Qwen3 14B for coding?
Only Ling 3.0 Flash has a coding score, 29.6 on the Vibe Coding Index; Qwen3 14B has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Ling 3.0 Flash or Qwen3 14B?
Ling 3.0 Flash costs $0.0405 per million input tokens and $0.1215 per million output tokens; Qwen3 14B costs $0.11 and $0.23. Cost per solved task is known for Ling 3.0 Flash only.
Which has the bigger context window, Ling 3.0 Flash or Qwen3 14B?
Ling 3.0 Flash, with 262K tokens against 41K.
How many benchmarks is the Ling 3.0 Flash vs Qwen3 14B comparison based on?
5 benchmarks have a result for both models as of October 22, 2026. Ling 3.0 Flash's coding index rests on 2 independent benchmarks and Qwen3 14B's on 0. Where only one model has a result, the page says so instead of naming a winner.