DeepSeek V3.1 vs Ling 3.0 Flash VL
Ling 3.0 Flash VL leads DeepSeek V3.1 on the Artificial Analysis Intelligence Index, 24.6 to 13.7, and Ling 3.0 Flash VL costs 8.3x less per output token ($0.18 vs $1.50 per million). Updated October 22, 2026; 1 benchmarks have a result for both.
DeepSeek V3.1 or Ling 3.0 Flash VL: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V3.1 for a larger context window (164K against 131K tokens) and work measured by AA Intelligence Index, where it leads.
- Shared results
- 1
- DeepSeek V3.1 only
- 10
- Ling 3.0 Flash VL only
- 6
- Like-for-like categories
- 1 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 13.7.
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
Web design
Design Arena: websites
No call
Only DeepSeek V3.1 has a Design Arena: websites result, 1122.
Directional only
Long documents
Listed context window
DeepSeek V3.1
DeepSeek V3.1 takes 164K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash VL
DeepSeek V3.1 $0.0013, Ling 3.0 Flash VL $0.0001 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.1 not benchmarked, Ling 3.0 Flash VL not benchmarked.
- Widest category
- Intelligence, 10.9 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Intelligence Index: DeepSeek V3.1 13.7 against Ling 3.0 Flash VL 24.6, 10.9 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.1 $1.50 against Ling 3.0 Flash VL $0.18 per million output tokens, 8.3x apart.
- Context window
- DeepSeek V3.1 takes 164K tokens, Ling 3.0 Flash VL 131K.
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 | DeepSeek V3.1 | Bars | Ling 3.0 Flash VL | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| All shared boards | 0 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index2 boards, none shared
| Benchmark | DeepSeek V3.1 | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 57.0 | Only Ling 3.0 Flash VL measured |
| SciCode | Not measured | 44.2% | Only Ling 3.0 Flash VL measured |
| Neither model has a result on the other 41 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.1 | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.7 | 24.6 | Ling 3.0 Flash VL |
| 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 | DeepSeek V3.1 | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 28.7 | Only Ling 3.0 Flash VL measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | DeepSeek V3.1 | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 86.2% | Only Ling 3.0 Flash VL measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 22.0% | Only Ling 3.0 Flash VL measured |
| Kagi LLM Benchmark | 53.2% | Not measured | Only DeepSeek V3.1 measured |
| 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 | DeepSeek V3.1 | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| Neither model has a result on 14 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| Neither model has a result on 20 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3.1 | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| Design Arena: websites | 1122 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: UI components | 1083 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: game dev | 1084 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: data viz | 1096 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: 3D | 1077 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: all code categories | 1112 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: SVG | 967 | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | DeepSeek V3.1 | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| Long context reasoning (AA-LCR) | Not measured | 78.3% | Only Ling 3.0 Flash VL measured |
| DTBench | 82.7% | Not measured | Only DeepSeek V3.1 measured |
| LMCA | 24.3% | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 45 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 | Ling 3.0 Flash VL | 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 | DeepSeek V3.1 | inclusionAI Ling 3.0 Flash VL |
|---|---|---|
| Maker | DeepSeek | inclusionAI |
| Weights | Open weights | Open weights |
| Released | August 21, 2025 | September 10, 2026 |
| Context window | 164K | 131K |
| Input $/Mtok | $0.55 | $0.06 |
| Output $/Mtok | $1.50 | $0.18 |
| 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 DeepSeek V3.1 better than Ling 3.0 Flash VL for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.1 for a larger context window (164K against 131K tokens) and work measured by AA Intelligence Index, where it leads.
Which is cheaper, DeepSeek V3.1 or Ling 3.0 Flash VL?
DeepSeek V3.1 costs $0.55 per million input tokens and $1.50 per million output tokens; Ling 3.0 Flash VL costs $0.06 and $0.18. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3.1 or Ling 3.0 Flash VL?
DeepSeek V3.1, with 164K tokens against 131K.
How many benchmarks is the DeepSeek V3.1 vs Ling 3.0 Flash VL comparison based on?
1 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.1's coding index rests on 0 independent benchmarks and Ling 3.0 Flash VL's on 0. Where only one model has a result, the page says so instead of naming a winner.