GPT-4o (2024-05-13) vs Ling 3.0 Flash VL
Ling 3.0 Flash VL leads GPT-4o (2024-05-13) on the Artificial Analysis Intelligence Index, 24.6 to 7.3, and Ling 3.0 Flash VL costs 83.3x less per output token ($0.18 vs $15.00 per million). Updated October 23, 2026; 4 benchmarks have a result for both.
GPT-4o (2024-05-13) 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 GPT-4o (2024-05-13) for work measured by AA Coding Index or AA Intelligence Index, where it leads.
- Shared results
- 4
- GPT-4o (2024-05-13) only
- 40
- Ling 3.0 Flash VL only
- 3
- Like-for-like categories
- 3 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 7.3.
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
Reasoning
GPQA Diamond
No call
Only GPT-4o (2024-05-13) has a GPQA Diamond result, 50.2%.
Directional only
Long documents
Listed context window
Ling 3.0 Flash VL
Ling 3.0 Flash VL takes 131K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash VL
GPT-4o (2024-05-13) $0.013, Ling 3.0 Flash VL $0.0001 per turn.
List prices
At a glance
- Coding index
- GPT-4o (2024-05-13) not benchmarked, Ling 3.0 Flash VL not benchmarked.
- Widest category
- Coding, 32.8 points apart on average over 1 shared benchmark.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): GPT-4o (2024-05-13) 52.6% against Ling 3.0 Flash VL 86.2%, 33.6 points apart on a 0 to 100 scale.
- Output price
- GPT-4o (2024-05-13) $15.00 against Ling 3.0 Flash VL $0.18 per million output tokens, 83.3x apart.
- Context window
- GPT-4o (2024-05-13) takes 128K 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 | GPT-4o (2024-05-13) | Bars | Ling 3.0 Flash VL | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| All shared boards | 0 | 4 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | GPT-4o (2024-05-13) | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| AA Coding Index | 24.2 | 57.0 | Ling 3.0 Flash VL |
| LiveCodeBench (Artificial Analysis run) | 33.4% | Not measured | Only GPT-4o (2024-05-13) measured |
| SciCode | Not measured | 44.2% | Only Ling 3.0 Flash VL measured |
| LMArena Text: coding | 1297 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: software and IT | 1297 | Not measured | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 38 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o (2024-05-13) | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.3 | 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 index3 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 28.7 | Only Ling 3.0 Flash VL measured |
| METR time horizons | 40.8% | Not measured | Only GPT-4o (2024-05-13) measured |
| BALROG | 32.3% | Not measured | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4o (2024-05-13) | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| GPQA Diamond | 50.2% | Not measured | Only GPT-4o (2024-05-13) measured |
| GPQA Diamond (Artificial Analysis run) | 52.6% | 86.2% | Ling 3.0 Flash VL |
| Humanity's Last Exam (Artificial Analysis run) | 1.8% | 22.0% | Ling 3.0 Flash VL |
| GPQA Diamond (Epoch AI run) | 48.9% | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: hard prompts | 1281 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: expert | 1250 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: hard prompts in English | 1293 | Not measured | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| OTIS Mock AIME | 6.3% | Not measured | Only GPT-4o (2024-05-13) measured |
| MATH level 5 | 51.0% | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: math | 1284 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: mathematical work | 1284 | Not measured | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 74.0% | Not measured | Only GPT-4o (2024-05-13) measured |
| MMLU | 84.2% | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: science | 1295 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: medicine and healthcare | 1278 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: legal and government | 1314 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: business and finance | 1261 | Not measured | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 14 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index21 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| Long context reasoning (AA-LCR) | Not measured | 78.3% | Only Ling 3.0 Flash VL measured |
| LMArena Text | 1300 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: instruction following | 1278 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: creative writing | 1291 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Vision | 1137 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: English | 1312 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: non-English | 1283 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Chinese | 1277 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: German | 1282 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Japanese | 1257 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Korean | 1234 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: French | 1304 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Spanish | 1292 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Russian | 1285 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Polish | 1365 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: multi-turn | 1302 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: longer queries | 1288 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: writing and language | 1298 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: entertainment and media | 1289 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Vision: English | 1147 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Vision: Chinese | 1132 | Not measured | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | 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 | OpenAI GPT-4o (2024-05-13) | inclusionAI Ling 3.0 Flash VL |
|---|---|---|
| Maker | OpenAI | inclusionAI |
| Weights | No public weights listed | Open weights |
| Released | May 13, 2024 | September 10, 2026 |
| Context window | 128K | 131K |
| Input $/Mtok | $5.00 | $0.06 |
| Output $/Mtok | $15.00 | $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 GPT-4o (2024-05-13) 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 GPT-4o (2024-05-13) for work measured by AA Coding Index or AA Intelligence Index, where it leads.
Which is cheaper, GPT-4o (2024-05-13) or Ling 3.0 Flash VL?
GPT-4o (2024-05-13) costs $5.00 per million input tokens and $15.00 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, GPT-4o (2024-05-13) or Ling 3.0 Flash VL?
Ling 3.0 Flash VL, with 131K tokens against 128K.
How many benchmarks is the GPT-4o (2024-05-13) vs Ling 3.0 Flash VL comparison based on?
4 benchmarks have a result for both models as of October 23, 2026. GPT-4o (2024-05-13)'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.