Ling 3.1 Flash vs Qwen3 VL 30B A3B Instruct
Ling 3.1 Flash leads Qwen3 VL 30B A3B Instruct on the Artificial Analysis Intelligence Index, 41.1 to 7.9, and Ling 3.1 Flash costs 3.1x less per output token ($0.225 vs $0.70 per million). Updated October 23, 2026; 2 benchmarks have a result for both.
Ling 3.1 Flash or Qwen3 VL 30B A3B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Ling 3.1 Flash for output tokens 3.1x cheaper ($0.225 vs $0.70 per million).
- Shared results
- 2
- Ling 3.1 Flash only
- 2
- Qwen3 VL 30B A3B Instruct only
- 8
- Like-for-like categories
- 2 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Ling 3.1 Flash
Ling 3.1 Flash leads, 41.1 against 7.9.
Same board, both measured
Long documents
Listed context window
No call
Both take 262K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.1 Flash
Ling 3.1 Flash $0.0002, Qwen3 VL 30B A3B Instruct $0.0006 per turn.
List prices
At a glance
- Coding index
- Ling 3.1 Flash not benchmarked, Qwen3 VL 30B A3B Instruct not benchmarked.
- Widest category
- Intelligence, 33.2 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Intelligence Index: Ling 3.1 Flash 41.1 against Qwen3 VL 30B A3B Instruct 7.9, 33.2 points apart on a 0 to 100 scale.
- Output price
- Ling 3.1 Flash $0.225 against Qwen3 VL 30B A3B Instruct $0.70 per million output tokens, 3.1x apart.
- Context window
- Both take 262K tokens.
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 3.1 Flash | Bars | Qwen3 VL 30B A3B Instruct | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 0 | ||
| All shared boards | 2 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index3 boards, none shared
| Benchmark | Ling 3.1 Flash | Qwen3 VL 30B A3B Instruct | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 69.7% | Only Qwen3 VL 30B A3B Instruct measured |
| SciCode | 54.1% | Not measured | Only Ling 3.1 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 6.1% | Only Qwen3 VL 30B A3B Instruct measured |
| Neither model has a result on the other 40 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Ling 3.1 Flash | Qwen3 VL 30B A3B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 41.1 | 7.9 | Ling 3.1 Flash |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Ling 3.1 Flash | Qwen3 VL 30B A3B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 19.9% | Only Qwen3 VL 30B A3B Instruct measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Ling 3.1 Flash | Qwen3 VL 30B A3B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 72.0% | Only Qwen3 VL 30B A3B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | 39.4% | 8.9% | Ling 3.1 Flash |
| Neither model has a result on the other 21 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Ling 3.1 Flash | Qwen3 VL 30B A3B Instruct | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 82.3 | Only Qwen3 VL 30B A3B Instruct measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 82.3% | Only Qwen3 VL 30B A3B Instruct measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Ling 3.1 Flash | Qwen3 VL 30B A3B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 80.7% | Only Qwen3 VL 30B A3B Instruct measured |
| Neither model has a result on the other 19 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.1 Flash | Qwen3 VL 30B A3B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Ling 3.1 Flash | Qwen3 VL 30B A3B Instruct | Leader |
|---|---|---|---|
| IFBench | Not measured | 45.1% | Only Qwen3 VL 30B A3B Instruct measured |
| Long context reasoning (AA-LCR) | 83.0% | Not measured | Only Ling 3.1 Flash measured |
| Neither model has a result on the other 46 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.1 Flash | Qwen3 VL 30B A3B Instruct | 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 3.1 Flash costs less
Repository review
50K input + 3K output tokens
Ling 3.1 Flash costs less
Agent loop
200K input + 10K output tokens
Ling 3.1 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 3.1 Flash | Qwen3 VL 30B A3B Instruct |
|---|---|---|
| Maker | inclusionAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | October 2, 2026 | October 6, 2025 |
| Context window | 262K | 262K |
| Input $/Mtok | $0.075 | $0.20 |
| Output $/Mtok | $0.225 | $0.70 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Ling 3.1 Flash better than Qwen3 VL 30B A3B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Ling 3.1 Flash for output tokens 3.1x cheaper ($0.225 vs $0.70 per million).
Which is cheaper, Ling 3.1 Flash or Qwen3 VL 30B A3B Instruct?
Ling 3.1 Flash costs $0.075 per million input tokens and $0.225 per million output tokens; Qwen3 VL 30B A3B Instruct costs $0.20 and $0.70. Cost per solved task is known for neither of them.
Which has the bigger context window, Ling 3.1 Flash or Qwen3 VL 30B A3B Instruct?
Neither: both take 262K tokens.
How many benchmarks is the Ling 3.1 Flash vs Qwen3 VL 30B A3B Instruct comparison based on?
2 benchmarks have a result for both models as of October 23, 2026. Ling 3.1 Flash's coding index rests on 0 independent benchmarks and Qwen3 VL 30B A3B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.