Ling 3.0 Flash VL vs Schematron V2 Turbo
Schematron V2 Turbo costs 1.2x less per output token than Ling 3.0 Flash VL ($0.15 vs $0.18 per million). Updated October 23, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.18
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.15
Ling 3.0 Flash VL or Schematron V2 Turbo: 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 a larger context window (131K against 128K tokens).
No benchmark has tested both yet. Ling 3.0 Flash VL has results on AA Coding Index, SciCode and AA Intelligence Index; Schematron V2 Turbo has none yet. Compare them on price and context instead.
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
No call
Only Ling 3.0 Flash VL has a AA Intelligence Index result, 24.6.
Directional only
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
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
Schematron V2 Turbo
Ling 3.0 Flash VL $0.0001, Schematron V2 Turbo $0.0001 per turn.
List prices
At a glance
- Coding index
- Ling 3.0 Flash VL not benchmarked, Schematron V2 Turbo not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Ling 3.0 Flash VL $0.18 against Schematron V2 Turbo $0.15 per million output tokens, 1.2x apart.
- Context window
- Ling 3.0 Flash VL takes 131K tokens, Schematron V2 Turbo 128K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index2 boards measured
| Benchmark | Ling 3.0 Flash VL | Schematron V2 Turbo | Leader |
|---|---|---|---|
| AA Coding Index | 57.0 | Not measured | Only Ling 3.0 Flash VL measured |
| SciCode | 44.2% | Not measured | Only Ling 3.0 Flash VL measured |
| Neither model has a result on the other 41 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Ling 3.0 Flash VL | Schematron V2 Turbo | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.6 | Not measured | Only Ling 3.0 Flash VL measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Ling 3.0 Flash VL | Schematron V2 Turbo | Leader |
|---|---|---|---|
| AA Agentic Index | 28.7 | Not measured | Only Ling 3.0 Flash VL measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Ling 3.0 Flash VL | Schematron V2 Turbo | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 86.2% | Not measured | Only Ling 3.0 Flash VL measured |
| Humanity's Last Exam (Artificial Analysis run) | 22.0% | Not measured | Only Ling 3.0 Flash VL measured |
| Neither model has a result on the other 21 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Ling 3.0 Flash VL | Schematron V2 Turbo | Leader |
|---|---|---|---|
| Long context reasoning (AA-LCR) | 78.3% | Not measured | Only Ling 3.0 Flash VL measured |
| Neither model has a result on the other 47 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
Schematron V2 Turbo costs less
Repository review
50K input + 3K output tokens
Schematron V2 Turbo costs less
Agent loop
200K input + 10K output tokens
Schematron V2 Turbo 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 | Inference.net Schematron V2 Turbo |
|---|---|---|
| Maker | inclusionAI | Inference.net |
| Weights | Open weights | Open weights |
| Released | September 10, 2026 | September 12, 2026 |
| Context window | 131K | 128K |
| Input $/Mtok | $0.06 | $0.03 |
| Output $/Mtok | $0.18 | $0.15 |
| 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 Schematron V2 Turbo for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Ling 3.0 Flash VL for a larger context window (131K against 128K tokens).
Which is cheaper, Ling 3.0 Flash VL or Schematron V2 Turbo?
Ling 3.0 Flash VL costs $0.06 per million input tokens and $0.18 per million output tokens; Schematron V2 Turbo costs $0.03 and $0.15. Cost per solved task is known for neither of them.
Which has the bigger context window, Ling 3.0 Flash VL or Schematron V2 Turbo?
Ling 3.0 Flash VL, with 131K tokens against 128K.
Has any benchmark tested both Ling 3.0 Flash VL and Schematron V2 Turbo?
No benchmark has tested both yet. Ling 3.0 Flash VL has results on AA Coding Index, SciCode and AA Intelligence Index; Schematron V2 Turbo has none yet. Compare them on price and context instead. Data as of October 23, 2026.