GPT-5.2-Codex vs Ling 3.0 Flash VL
GPT-5.2-Codex leads Ling 3.0 Flash VL on the Artificial Analysis Intelligence Index, 28.5 to 24.6, and Ling 3.0 Flash VL costs 77.8x less per output token ($0.18 vs $14.00 per million). Updated October 22, 2026; 4 benchmarks have a result for both.
GPT-5.2-Codex or Ling 3.0 Flash VL: which to pick
Only GPT-5.2-Codex has a coding score, 45.4 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 77.8x cheaper ($0.18 vs $14.00 per million).
- Shared results
- 4
- GPT-5.2-Codex only
- 18
- 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-5.2-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.2-Codex
GPT-5.2-Codex leads, 28.5 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
Long documents
Listed context window
GPT-5.2-Codex
GPT-5.2-Codex takes 400K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash VL
GPT-5.2-Codex $0.0088, 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-5.2-Codex has the number, $0.43.
Directional only
At a glance
- Coding index
- GPT-5.2-Codex 45.4 from 3 benchmarks, Ling 3.0 Flash VL not benchmarked.
- Widest category
- Reasoning, 8.7 points apart on average over 2 shared benchmarks.
- Widest single gap
- Humanity's Last Exam (Artificial Analysis run): GPT-5.2-Codex 35.7% against Ling 3.0 Flash VL 22.0%, 13.7 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2-Codex $14.00 against Ling 3.0 Flash VL $0.18 per million output tokens, 77.8x apart.
- Context window
- GPT-5.2-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-5.2-Codex | Bars | Ling 3.0 Flash VL | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 4 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index13 boards, none shared
| Benchmark | GPT-5.2-Codex | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| SWE-bench Verified | 72.8% | Not measured | Only GPT-5.2-Codex measured |
| SWE-bench Multilingual | 66.3% | Not measured | Only GPT-5.2-Codex measured |
| LMArena WebDev | 1339 | Not measured | Only GPT-5.2-Codex measured |
| AA Coding Index | Not measured | 57.0 | Only Ling 3.0 Flash VL measured |
| LiveCodeBench (Vals AI) | 88.0% | Not measured | Only GPT-5.2-Codex measured |
| SWE-bench Verified (Vals AI) | 72.4% | Not measured | Only GPT-5.2-Codex measured |
| Vibe Code Bench (Vals AI) | 37.9% | Not measured | Only GPT-5.2-Codex measured |
| SciCode | Not measured | 44.2% | Only Ling 3.0 Flash VL measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.1% | Not measured | Only GPT-5.2-Codex measured |
| Terminal-Bench | 66.5% | Not measured | Only GPT-5.2-Codex measured |
| ALE-Bench | 1299.9 | Not measured | Only GPT-5.2-Codex measured |
| LMArena WebDev: React | 1329 | Not measured | Only GPT-5.2-Codex measured |
| LMArena WebDev: HTML | 1348 | Not measured | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2-Codex | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| AA Intelligence Index | 28.5 | 24.6 | GPT-5.2-Codex |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-5.2-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 | 1051 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building full-stack apps | 993 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building mobile apps | 1090 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building Android apps | 1176 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GPT-5.2-Codex measured |
| τ²-Bench Telecom | 92.1% | Not measured | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.2-Codex | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 89.9% | 86.2% | GPT-5.2-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 35.7% | 22.0% | GPT-5.2-Codex |
| 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 | GPT-5.2-Codex | Ling 3.0 Flash VL | 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 | GPT-5.2-Codex | Ling 3.0 Flash VL | 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 | GPT-5.2-Codex | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2-Codex | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| IFBench | 77.6% | Not measured | Only GPT-5.2-Codex measured |
| Long context reasoning (AA-LCR) | 82.3% | 78.3% | GPT-5.2-Codex |
| 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-5.2-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-5.2-Codex | inclusionAI Ling 3.0 Flash VL |
|---|---|---|
| Maker | OpenAI | inclusionAI |
| Weights | No public weights listed | Open weights |
| Released | January 14, 2026 | September 10, 2026 |
| Context window | 400K | 131K |
| Input $/Mtok | $1.75 | $0.06 |
| Output $/Mtok | $14.00 | $0.18 |
| Coding index | 45.4 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $0.43 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2-Codex better than Ling 3.0 Flash VL for coding?
Only GPT-5.2-Codex has a coding score, 45.4 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 77.8x cheaper ($0.18 vs $14.00 per million).
Which is cheaper, GPT-5.2-Codex or Ling 3.0 Flash VL?
GPT-5.2-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Ling 3.0 Flash VL costs $0.06 and $0.18. Cost per solved task is known for GPT-5.2-Codex only.
Which has the bigger context window, GPT-5.2-Codex or Ling 3.0 Flash VL?
GPT-5.2-Codex, with 400K tokens against 131K.
How many benchmarks is the GPT-5.2-Codex vs Ling 3.0 Flash VL comparison based on?
4 benchmarks have a result for both models as of October 22, 2026. GPT-5.2-Codex's coding index rests on 3 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.