Ling 3.0 Flash VL vs Qwen3 Coder Next
Ling 3.0 Flash VL leads Qwen3 Coder Next on the Artificial Analysis Intelligence Index, 24.6 to 9.2, and Ling 3.0 Flash VL costs 4.4x less per output token ($0.18 vs $0.80 per million). Updated October 22, 2026; 7 benchmarks have a result for both.
Ling 3.0 Flash VL or Qwen3 Coder Next: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Ling 3.0 Flash VL for output tokens 4.4x cheaper ($0.18 vs $0.80 per million).
- Shared results
- 7
- Ling 3.0 Flash VL only
- 0
- Qwen3 Coder Next only
- 4
- Like-for-like categories
- 5 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 9.2.
Same board, both measured
Agentic work
AA Agentic Index
Ling 3.0 Flash VL
Ling 3.0 Flash VL leads, 28.7 against 0.9.
Same board, both measured
Long documents
Listed context window
Qwen3 Coder Next
Qwen3 Coder Next 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 Coder Next $0.0005 per turn.
List prices
At a glance
- Coding index
- Ling 3.0 Flash VL not benchmarked, Qwen3 Coder Next not benchmarked.
- Widest category
- General, 31.3 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Ling 3.0 Flash VL 78.3% against Qwen3 Coder Next 47.0%, 31.3 points apart on a 0 to 100 scale.
- Output price
- Ling 3.0 Flash VL $0.18 against Qwen3 Coder Next $0.80 per million output tokens, 4.4x apart.
- Context window
- Ling 3.0 Flash VL takes 131K tokens, Qwen3 Coder Next 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 Coder Next | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 7 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | Ling 3.0 Flash VL | Qwen3 Coder Next | Leader |
|---|---|---|---|
| AA Coding Index | 57.0 | 36.2 | Ling 3.0 Flash VL |
| SciCode | 44.2% | 36.2% | Ling 3.0 Flash VL |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 18.2% | Only Qwen3 Coder Next measured |
| WeirdML | Not measured | 34.4% | Only Qwen3 Coder Next measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Ling 3.0 Flash VL | Qwen3 Coder Next | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.6 | 9.2 | Ling 3.0 Flash VL |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Ling 3.0 Flash VL | Qwen3 Coder Next | Leader |
|---|---|---|---|
| AA Agentic Index | 28.7 | 0.9 | Ling 3.0 Flash VL |
| τ²-Bench Telecom | Not measured | 79.5% | Only Qwen3 Coder Next measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Ling 3.0 Flash VL | Qwen3 Coder Next | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 86.2% | 73.7% | Ling 3.0 Flash VL |
| Humanity's Last Exam (Artificial Analysis run) | 22.0% | 10.1% | Ling 3.0 Flash VL |
| Neither model has a result on the other 20 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Qwen3 Coder Next | Leader |
|---|---|---|---|
| Neither model has a result on 13 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Qwen3 Coder Next | Leader |
|---|---|---|---|
| Neither model has a result on 18 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Qwen3 Coder Next | 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 Coder Next | Leader |
|---|---|---|---|
| IFBench | Not measured | 35.2% | Only Qwen3 Coder Next measured |
| Long context reasoning (AA-LCR) | 78.3% | 47.0% | Ling 3.0 Flash VL |
| 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 Coder Next | 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 Coder Next |
|---|---|---|
| Maker | inclusionAI | Qwen |
| Weights | Open weights | Open weights |
| Released | September 10, 2026 | February 4, 2026 |
| Context window | 131K | 262K |
| Input $/Mtok | $0.06 | $0.12 |
| Output $/Mtok | $0.18 | $0.80 |
| 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 Coder Next for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Ling 3.0 Flash VL for output tokens 4.4x cheaper ($0.18 vs $0.80 per million).
Which is cheaper, Ling 3.0 Flash VL or Qwen3 Coder Next?
Ling 3.0 Flash VL costs $0.06 per million input tokens and $0.18 per million output tokens; Qwen3 Coder Next costs $0.12 and $0.80. Cost per solved task is known for neither of them.
Which has the bigger context window, Ling 3.0 Flash VL or Qwen3 Coder Next?
Qwen3 Coder Next, with 262K tokens against 131K.
How many benchmarks is the Ling 3.0 Flash VL vs Qwen3 Coder Next comparison based on?
7 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 Coder Next's on 0. Where only one model has a result, the page says so instead of naming a winner.