GPT-5.1-Codex vs Ling 3.0 Flash VL
Ling 3.0 Flash VL leads GPT-5.1-Codex on the Artificial Analysis Intelligence Index, 24.6 to 23.7, and Ling 3.0 Flash VL costs 55.6x less per output token ($0.18 vs $10.00 per million). Updated October 22, 2026; 4 benchmarks have a result for both.
GPT-5.1-Codex or Ling 3.0 Flash VL: which to pick
Only GPT-5.1-Codex has a coding score, 15.3 on the Vibe Coding Index; Ling 3.0 Flash VL has none, so the two cannot be ranked on coding.
Pick Ling 3.0 Flash VL for output tokens 55.6x cheaper ($0.18 vs $10.00 per million) and work measured by AA Intelligence Index or GPQA Diamond (Artificial Analysis run), where it leads.
- Shared results
- 4
- GPT-5.1-Codex only
- 22
- 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
Only GPT-5.1-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Ling 3.0 Flash VL
Ling 3.0 Flash VL leads, 24.6 against 23.7.
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 GPT-5.1-Codex has a Design Arena: websites result, 1161.
Directional only
Long documents
Listed context window
GPT-5.1-Codex
GPT-5.1-Codex takes 400K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash VL
GPT-5.1-Codex $0.0063, Ling 3.0 Flash VL $0.0001 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.1-Codex has the number, $0.892.
Directional only
At a glance
- Coding index
- GPT-5.1-Codex 15.3 from 2 benchmarks, Ling 3.0 Flash VL not benchmarked.
- Widest category
- General, 9.0 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): GPT-5.1-Codex 69.3% against Ling 3.0 Flash VL 78.3%, 9.0 points apart on a 0 to 100 scale.
- Output price
- GPT-5.1-Codex $10.00 against Ling 3.0 Flash VL $0.18 per million output tokens, 55.6x apart.
- Context window
- GPT-5.1-Codex takes 400K 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-5.1-Codex | Bars | Ling 3.0 Flash VL | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 1 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 1 | 3 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index11 boards, none shared
| Benchmark | GPT-5.1-Codex | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| SWE-bench Verified | 66.0% | Not measured | Only GPT-5.1-Codex measured |
| LMArena WebDev | 1337 | Not measured | Only GPT-5.1-Codex measured |
| AA Coding Index | Not measured | 57.0 | Only Ling 3.0 Flash VL measured |
| LiveCodeBench (Vals AI) | 85.5% | Not measured | Only GPT-5.1-Codex measured |
| Vibe Code Bench (Vals AI) | 13.1% | Not measured | Only GPT-5.1-Codex measured |
| LiveCodeBench (Artificial Analysis run) | 84.9% | Not measured | Only GPT-5.1-Codex measured |
| SciCode | Not measured | 44.2% | Only Ling 3.0 Flash VL measured |
| Terminal-Bench Hard (Artificial Analysis run) | 34.8% | Not measured | Only GPT-5.1-Codex measured |
| Terminal-Bench | 57.8% | Not measured | Only GPT-5.1-Codex measured |
| ALE-Bench | 1244.9 | Not measured | Only GPT-5.1-Codex measured |
| LMArena WebDev: HTML | 1346 | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 28 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.1-Codex | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| AA Intelligence Index | 23.7 | 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 index5 boards, none shared
| Benchmark | GPT-5.1-Codex | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 28.7 | Only Ling 3.0 Flash VL measured |
| Design Arena: agents building web apps | 1018 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: agents building full-stack apps | 1086 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: agents building mobile apps | 1191 | Not measured | Only GPT-5.1-Codex measured |
| τ²-Bench Telecom | 83.0% | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GPT-5.1-Codex | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 86.0% | 86.2% | Ling 3.0 Flash VL |
| Humanity's Last Exam (Artificial Analysis run) | 25.7% | 22.0% | GPT-5.1-Codex |
| 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 | GPT-5.1-Codex | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| AA Math Index | 95.7 | Not measured | Only GPT-5.1-Codex measured |
| AIME 2025 (Artificial Analysis run) | 95.7% | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.1-Codex | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 86.0% | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 17 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-5.1-Codex | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| Design Arena: websites | 1161 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: UI components | 1234 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: game dev | 1137 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: data viz | 1213 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: all code categories | 1152 | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 4 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.1-Codex | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| IFBench | 70.0% | Not measured | Only GPT-5.1-Codex measured |
| Long context reasoning (AA-LCR) | 69.3% | 78.3% | 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 | GPT-5.1-Codex | 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-5.1-Codex | inclusionAI Ling 3.0 Flash VL |
|---|---|---|
| Maker | OpenAI | inclusionAI |
| Weights | No public weights listed | Open weights |
| Released | November 13, 2025 | September 10, 2026 |
| Context window | 400K | 131K |
| Input $/Mtok | $1.25 | $0.06 |
| Output $/Mtok | $10.00 | $0.18 |
| Coding index | 15.3 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | $0.892 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1-Codex better than Ling 3.0 Flash VL for coding?
Only GPT-5.1-Codex has a coding score, 15.3 on the Vibe Coding Index; Ling 3.0 Flash VL has none, so the two cannot be ranked on coding. Pick Ling 3.0 Flash VL for output tokens 55.6x cheaper ($0.18 vs $10.00 per million) and work measured by AA Intelligence Index or GPQA Diamond (Artificial Analysis run), where it leads.
Which is cheaper, GPT-5.1-Codex or Ling 3.0 Flash VL?
GPT-5.1-Codex costs $1.25 per million input tokens and $10.00 per million output tokens; Ling 3.0 Flash VL costs $0.06 and $0.18. Cost per solved task is known for GPT-5.1-Codex only.
Which has the bigger context window, GPT-5.1-Codex or Ling 3.0 Flash VL?
GPT-5.1-Codex, with 400K tokens against 131K.
How many benchmarks is the GPT-5.1-Codex vs Ling 3.0 Flash VL comparison based on?
4 benchmarks have a result for both models as of October 22, 2026. GPT-5.1-Codex's coding index rests on 2 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.