Ling 3.0 Flash VL vs Solar Pro 4
Solar Pro 4 leads Ling 3.0 Flash VL on the Artificial Analysis Intelligence Index, 28.2 to 24.6, and Ling 3.0 Flash VL costs 3.3x less per output token ($0.18 vs $0.60 per million). Updated October 23, 2026; 6 benchmarks have a result for both.
Ling 3.0 Flash VL or Solar Pro 4: 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 3.3x cheaper ($0.18 vs $0.60 per million) and work measured by SciCode or AA Intelligence Index, where it leads.
- Shared results
- 6
- Ling 3.0 Flash VL only
- 1
- Solar Pro 4 only
- 39
- 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
Solar Pro 4
Solar Pro 4 leads, 28.2 against 24.6.
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 Solar Pro 4 has a Design Arena: websites result, 1176.
Directional only
Long documents
Listed context window
Solar Pro 4
Solar Pro 4 takes 524K 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, Solar Pro 4 $0.0004 per turn.
List prices
At a glance
- Coding index
- Ling 3.0 Flash VL not benchmarked, Solar Pro 4 not benchmarked.
- Widest category
- General, 7.7 points apart on average over 1 shared benchmark.
- Widest single gap
- Humanity's Last Exam (Artificial Analysis run): Ling 3.0 Flash VL 22.0% against Solar Pro 4 29.8%, 7.8 points apart on a 0 to 100 scale.
- Output price
- Ling 3.0 Flash VL $0.18 against Solar Pro 4 $0.60 per million output tokens, 3.3x apart.
- Context window
- Ling 3.0 Flash VL takes 131K tokens, Solar Pro 4 524K.
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 | Solar Pro 4 | Ties |
|---|---|---|---|---|
| Coding | 1 | 1 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 1 | 0 | ||
| All shared boards | 2 | 4 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index11
| Benchmark | Ling 3.0 Flash VL | Solar Pro 4 | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1370 | Only Solar Pro 4 measured |
| AA Coding Index | 57.0 | 52.7 | Ling 3.0 Flash VL |
| SciCode | 44.2% | 48.0% | Solar Pro 4 |
| ALE-Bench | Not measured | 179.3 | Only Solar Pro 4 measured |
| LMArena Text: coding | Not measured | 1437 | Only Solar Pro 4 measured |
| LMArena Text: software and IT | Not measured | 1431 | Only Solar Pro 4 measured |
| LMArena WebDev: React | Not measured | 1363 | Only Solar Pro 4 measured |
| LMArena WebDev: HTML | Not measured | 1371 | Only Solar Pro 4 measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Ling 3.0 Flash VL | Solar Pro 4 | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.6 | 28.2 | Solar Pro 4 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Solar Pro 4 | Leader |
|---|---|---|---|
| AA Agentic Index | 28.7 | Not measured | Only Ling 3.0 Flash VL measured |
| Design Arena: agents building web apps | Not measured | 1105 | Only Solar Pro 4 measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Ling 3.0 Flash VL | Solar Pro 4 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 86.2% | 89.1% | Solar Pro 4 |
| Humanity's Last Exam (Artificial Analysis run) | 22.0% | 29.8% | Solar Pro 4 |
| LMArena Text: hard prompts | Not measured | 1400 | Only Solar Pro 4 measured |
| LMArena Text: expert | Not measured | 1428 | Only Solar Pro 4 measured |
| LMArena Text: hard prompts in English | Not measured | 1428 | Only Solar Pro 4 measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Solar Pro 4 | Leader |
|---|---|---|---|
| LMArena Text: math | Not measured | 1417 | Only Solar Pro 4 measured |
| LMArena Text: mathematical work | Not measured | 1423 | Only Solar Pro 4 measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Solar Pro 4 | Leader |
|---|---|---|---|
| LMArena Text: science | Not measured | 1403 | Only Solar Pro 4 measured |
| LMArena Text: medicine and healthcare | Not measured | 1374 | Only Solar Pro 4 measured |
| LMArena Text: legal and government | Not measured | 1384 | Only Solar Pro 4 measured |
| LMArena Text: business and finance | Not measured | 1381 | Only Solar Pro 4 measured |
| Neither model has a result on the other 16 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Solar Pro 4 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1176 | Only Solar Pro 4 measured |
| Design Arena: UI components | Not measured | 1147 | Only Solar Pro 4 measured |
| Design Arena: game dev | Not measured | 1169 | Only Solar Pro 4 measured |
| Design Arena: data viz | Not measured | 1178 | Only Solar Pro 4 measured |
| Design Arena: 3D | Not measured | 1179 | Only Solar Pro 4 measured |
| Design Arena: all code categories | Not measured | 1182 | Only Solar Pro 4 measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Ling 3.0 Flash VL | Solar Pro 4 | Leader |
|---|---|---|---|
| Long context reasoning (AA-LCR) | 78.3% | 70.7% | Ling 3.0 Flash VL |
| LMArena Text | Not measured | 1388 | Only Solar Pro 4 measured |
| LMArena Text: instruction following | Not measured | 1374 | Only Solar Pro 4 measured |
| LMArena Text: creative writing | Not measured | 1318 | Only Solar Pro 4 measured |
| LMArena Text: English | Not measured | 1415 | Only Solar Pro 4 measured |
| LMArena Text: non-English | Not measured | 1363 | Only Solar Pro 4 measured |
| LMArena Text: Chinese | Not measured | 1423 | Only Solar Pro 4 measured |
| LMArena Text: German | Not measured | 1357 | Only Solar Pro 4 measured |
| LMArena Text: Japanese | Not measured | 1320 | Only Solar Pro 4 measured |
| LMArena Text: Korean | Not measured | 1391 | Only Solar Pro 4 measured |
| LMArena Text: French | Not measured | 1403 | Only Solar Pro 4 measured |
| LMArena Text: Spanish | Not measured | 1413 | Only Solar Pro 4 measured |
| LMArena Text: Russian | Not measured | 1354 | Only Solar Pro 4 measured |
| LMArena Text: Polish | Not measured | 1307 | Only Solar Pro 4 measured |
| LMArena Text: multi-turn | Not measured | 1387 | Only Solar Pro 4 measured |
| LMArena Text: longer queries | Not measured | 1382 | Only Solar Pro 4 measured |
| LMArena Text: writing and language | Not measured | 1344 | Only Solar Pro 4 measured |
| LMArena Text: entertainment and media | Not measured | 1333 | Only Solar Pro 4 measured |
| Neither model has a result on the other 30 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Solar Pro 4 | 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 | Upstage Solar Pro 4 |
|---|---|---|
| Maker | inclusionAI | Upstage |
| Weights | Open weights | No public weights listed |
| Released | September 10, 2026 | August 10, 2026 |
| Context window | 131K | 524K |
| Input $/Mtok | $0.06 | $0.15 |
| Output $/Mtok | $0.18 | $0.60 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Ling 3.0 Flash VL better than Solar Pro 4 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 3.3x cheaper ($0.18 vs $0.60 per million) and work measured by SciCode or AA Intelligence Index, where it leads.
Which is cheaper, Ling 3.0 Flash VL or Solar Pro 4?
Ling 3.0 Flash VL costs $0.06 per million input tokens and $0.18 per million output tokens; Solar Pro 4 costs $0.15 and $0.60. Cost per solved task is known for neither of them.
Which has the bigger context window, Ling 3.0 Flash VL or Solar Pro 4?
Solar Pro 4, with 524K tokens against 131K.
How many benchmarks is the Ling 3.0 Flash VL vs Solar Pro 4 comparison based on?
6 benchmarks have a result for both models as of October 23, 2026. Ling 3.0 Flash VL's coding index rests on 0 independent benchmarks and Solar Pro 4's on 1. Where only one model has a result, the page says so instead of naming a winner.