Ling 3.0 Flash VL vs Qwen3.5-9B
Ling 3.0 Flash VL leads Qwen3.5-9B on the Artificial Analysis Intelligence Index, 24.6 to 13.3, and Qwen3.5-9B costs 1.2x less per output token ($0.15 vs $0.18 per million). Updated October 22, 2026; 6 benchmarks have a result for both.
Ling 3.0 Flash VL or Qwen3.5-9B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 6
- Ling 3.0 Flash VL only
- 1
- Qwen3.5-9B only
- 10
- 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Ling 3.0 Flash VL
Ling 3.0 Flash VL leads, 24.6 against 13.3.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Ling 3.0 Flash VL has a AA Agentic Index result, 28.7.
Directional only
Long documents
Listed context window
Qwen3.5-9B
Qwen3.5-9B takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash VL
Ling 3.0 Flash VL $0.0001, Qwen3.5-9B $0.0002 per turn.
List prices
At a glance
- Coding index
- Ling 3.0 Flash VL not benchmarked, Qwen3.5-9B not benchmarked.
- Widest category
- Coding, 24.1 points apart on average over 2 shared benchmarks.
- Widest single gap
- AA Coding Index: Ling 3.0 Flash VL 57.0 against Qwen3.5-9B 23.5, 33.5 points apart on a 0 to 100 scale.
- Output price
- Ling 3.0 Flash VL $0.18 against Qwen3.5-9B $0.15 per million output tokens, 1.2x apart.
- Context window
- Ling 3.0 Flash VL takes 131K tokens, Qwen3.5-9B 262K.
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 VL | Bars | Qwen3.5-9B | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 6 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | Ling 3.0 Flash VL | Qwen3.5-9B | Leader |
|---|---|---|---|
| AA Coding Index | 57.0 | 23.5 | Ling 3.0 Flash VL |
| SciCode | 44.2% | 29.5% | Ling 3.0 Flash VL |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 24.2% | Only Qwen3.5-9B measured |
| Terminal-Bench | Not measured | 9.2% | Only Qwen3.5-9B measured |
| Neither model has a result on the other 39 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Ling 3.0 Flash VL | Qwen3.5-9B | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.6 | 13.3 | Ling 3.0 Flash VL |
| Epoch Capabilities Index | Not measured | 139.3 | Only Qwen3.5-9B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Qwen3.5-9B | Leader |
|---|---|---|---|
| AA Agentic Index | 28.7 | Not measured | Only Ling 3.0 Flash VL measured |
| τ²-Bench Telecom | Not measured | 86.8% | Only Qwen3.5-9B measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Ling 3.0 Flash VL | Qwen3.5-9B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 86.2% | 80.6% | Ling 3.0 Flash VL |
| Humanity's Last Exam (Artificial Analysis run) | 22.0% | 14.9% | Ling 3.0 Flash VL |
| GPQA Diamond (Epoch AI run) | Not measured | 79.0% | Only Qwen3.5-9B measured |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Qwen3.5-9B | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 61.7% | Only Qwen3.5-9B measured |
| MathArena | Not measured | 13.0% | Only Qwen3.5-9B measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Qwen3.5-9B | Leader |
|---|---|---|---|
| Neither model has a result on 20 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Qwen3.5-9B | 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 VL | Qwen3.5-9B | Leader |
|---|---|---|---|
| IFBench | Not measured | 66.7% | Only Qwen3.5-9B measured |
| Long context reasoning (AA-LCR) | 78.3% | 70.0% | Ling 3.0 Flash VL |
| DTBench | Not measured | 71.2% | Only Qwen3.5-9B measured |
| LMCA | Not measured | 24.5% | Only Qwen3.5-9B measured |
| Neither model has a result on the other 44 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Qwen3.5-9B | 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 VL costs less
Repository review
50K input + 3K output tokens
Ling 3.0 Flash VL costs less
Agent loop
200K input + 10K output tokens
Ling 3.0 Flash VL 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 VL | Qwen3.5-9B |
|---|---|---|
| Maker | inclusionAI | Qwen |
| Weights | Open weights | Open weights |
| Released | September 10, 2026 | March 10, 2026 |
| Context window | 131K | 262K |
| Input $/Mtok | $0.06 | $0.10 |
| Output $/Mtok | $0.18 | $0.15 |
| 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 3.0 Flash VL better than Qwen3.5-9B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Ling 3.0 Flash VL or Qwen3.5-9B?
Ling 3.0 Flash VL costs $0.06 per million input tokens and $0.18 per million output tokens; Qwen3.5-9B costs $0.10 and $0.15. Cost per solved task is known for neither of them.
Which has the bigger context window, Ling 3.0 Flash VL or Qwen3.5-9B?
Qwen3.5-9B, with 262K tokens against 131K.
How many benchmarks is the Ling 3.0 Flash VL vs Qwen3.5-9B comparison based on?
6 benchmarks have a result for both models as of October 22, 2026. Ling 3.0 Flash VL's coding index rests on 0 independent benchmarks and Qwen3.5-9B's on 0. Where only one model has a result, the page says so instead of naming a winner.