Ling 3.0 Flash VL vs Llama 3.2 3B Instruct
Ling 3.0 Flash VL leads Llama 3.2 3B Instruct on the Artificial Analysis Intelligence Index, 24.6 to 5.7, and Ling 3.0 Flash VL costs 1.8x less per output token ($0.18 vs $0.3325 per million). Updated October 22, 2026; 4 benchmarks have a result for both.
Ling 3.0 Flash VL or Llama 3.2 3B Instruct: 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 1.8x cheaper ($0.18 vs $0.3325 per million).
- Shared results
- 4
- Ling 3.0 Flash VL only
- 3
- Llama 3.2 3B Instruct only
- 31
- 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 5.7.
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
Long documents
Listed context window
No call
Both take 131K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash VL
Ling 3.0 Flash VL $0.0001, Llama 3.2 3B Instruct $0.0002 per turn.
List prices
At a glance
- Coding index
- Ling 3.0 Flash VL not benchmarked, Llama 3.2 3B Instruct not benchmarked.
- Widest category
- General, 74.0 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Ling 3.0 Flash VL 78.3% against Llama 3.2 3B Instruct 4.3%, 74.0 points apart on a 0 to 100 scale.
- Output price
- Ling 3.0 Flash VL $0.18 against Llama 3.2 3B Instruct $0.3325 per million output tokens, 1.8x apart.
- Context window
- Both take 131K tokens.
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 | Llama 3.2 3B Instruct | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 4 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index5 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| AA Coding Index | 57.0 | Not measured | Only Ling 3.0 Flash VL measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 8.3% | Only Llama 3.2 3B Instruct measured |
| SciCode | 44.2% | Not measured | Only Ling 3.0 Flash VL measured |
| LMArena Text: coding | Not measured | 1098 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: software and IT | Not measured | 1103 | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 38 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Ling 3.0 Flash VL | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.6 | 5.7 | 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 index4 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| AA Agentic Index | 28.7 | Not measured | Only Ling 3.0 Flash VL measured |
| τ²-Bench Telecom | Not measured | 21.1% | Only Llama 3.2 3B Instruct measured |
| BALROG | Not measured | 10.1% | Only Llama 3.2 3B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 21.9% | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Ling 3.0 Flash VL | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 86.2% | 25.5% | Ling 3.0 Flash VL |
| Humanity's Last Exam (Artificial Analysis run) | 22.0% | 5.3% | Ling 3.0 Flash VL |
| LMArena Text: hard prompts | Not measured | 1095 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: expert | Not measured | 1090 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: hard prompts in English | Not measured | 1141 | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 3.3 | Only Llama 3.2 3B Instruct measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 3.3% | Only Llama 3.2 3B Instruct measured |
| LMArena Text: math | Not measured | 1126 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: mathematical work | Not measured | 1128 | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 34.7% | Only Llama 3.2 3B Instruct measured |
| LMArena Text: science | Not measured | 1106 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: medicine and healthcare | Not measured | 1104 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: legal and government | Not measured | 1115 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: business and finance | Not measured | 1074 | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 15 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Ling 3.0 Flash VL | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| IFBench | Not measured | 26.2% | Only Llama 3.2 3B Instruct measured |
| Long context reasoning (AA-LCR) | 78.3% | 4.3% | Ling 3.0 Flash VL |
| LMArena Text | Not measured | 1110 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: instruction following | Not measured | 1089 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: creative writing | Not measured | 1094 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: English | Not measured | 1180 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: non-English | Not measured | 1019 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: Chinese | Not measured | 1017 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: German | Not measured | 1056 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: Russian | Not measured | 949 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: multi-turn | Not measured | 1105 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: longer queries | Not measured | 1100 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: writing and language | Not measured | 1096 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: entertainment and media | Not measured | 1083 | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 34 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Llama 3.2 3B Instruct | 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
Llama 3.2 3B Instruct costs less
Agent loop
200K input + 10K output tokens
Llama 3.2 3B Instruct 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 | Meta Llama 3.2 3B Instruct |
|---|---|---|
| Maker | inclusionAI | Meta Llama |
| Weights | Open weights | Open weights |
| Released | September 10, 2026 | September 25, 2024 |
| Context window | 131K | 131K |
| Input $/Mtok | $0.06 | $0.0505 |
| Output $/Mtok | $0.18 | $0.3325 |
| 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 Ling 3.0 Flash VL better than Llama 3.2 3B Instruct 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 1.8x cheaper ($0.18 vs $0.3325 per million).
Which is cheaper, Ling 3.0 Flash VL or Llama 3.2 3B Instruct?
Ling 3.0 Flash VL costs $0.06 per million input tokens and $0.18 per million output tokens; Llama 3.2 3B Instruct costs $0.0505 and $0.3325. Cost per solved task is known for neither of them.
Which has the bigger context window, Ling 3.0 Flash VL or Llama 3.2 3B Instruct?
Neither: both take 131K tokens.
How many benchmarks is the Ling 3.0 Flash VL vs Llama 3.2 3B Instruct comparison based on?
4 benchmarks have a result for both models as of October 22, 2026. Ling 3.0 Flash VL's coding index rests on 0 independent benchmarks and Llama 3.2 3B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.