Ling 3.0 Flash VL vs Saba
Ling 3.0 Flash VL leads Saba on the Artificial Analysis Intelligence Index, 24.6 to 6.5, and Ling 3.0 Flash VL costs 3.3x less per output token ($0.18 vs $0.60 per million). Updated October 22, 2026; 3 benchmarks have a result for both.
Ling 3.0 Flash VL or Saba: 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 3.3x cheaper ($0.18 vs $0.60 per million) and a larger context window (131K against 33K tokens).
- Shared results
- 3
- Ling 3.0 Flash VL only
- 4
- Saba only
- 1
- Like-for-like categories
- 2 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 6.5.
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
Ling 3.0 Flash VL
Ling 3.0 Flash VL takes 131K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash VL
Ling 3.0 Flash VL $0.0001, Saba $0.0005 per turn.
List prices
At a glance
- Coding index
- Ling 3.0 Flash VL not benchmarked, Saba not benchmarked.
- Widest category
- Reasoning, 30.8 points apart on average over 2 shared benchmarks.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): Ling 3.0 Flash VL 86.2% against Saba 42.4%, 43.8 points apart on a 0 to 100 scale.
- Output price
- Ling 3.0 Flash VL $0.18 against Saba $0.60 per million output tokens, 3.3x apart.
- Context window
- Ling 3.0 Flash VL takes 131K tokens, Saba 33K.
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 | Saba | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| All shared boards | 3 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index2 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Saba | 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 37 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Ling 3.0 Flash VL | Saba | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.6 | 6.5 | 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 index1 board, none shared
| Benchmark | Ling 3.0 Flash VL | Saba | Leader |
|---|---|---|---|
| AA Agentic Index | 28.7 | Not measured | Only Ling 3.0 Flash VL measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Ling 3.0 Flash VL | Saba | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 86.2% | 42.4% | Ling 3.0 Flash VL |
| Humanity's Last Exam (Artificial Analysis run) | 22.0% | 4.3% | Ling 3.0 Flash VL |
| 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 | Ling 3.0 Flash VL | Saba | Leader |
|---|---|---|---|
| Neither model has a result on 13 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Ling 3.0 Flash VL | Saba | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 61.1% | Only Saba measured |
| Neither model has a result on the other 17 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Saba | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Ling 3.0 Flash VL | Saba | 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 45 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Saba | 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 | inclusionAI Ling 3.0 Flash VL | Mistral Saba |
|---|---|---|
| Maker | inclusionAI | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | September 10, 2026 | February 17, 2025 |
| Context window | 131K | 33K |
| Input $/Mtok | $0.06 | $0.20 |
| Output $/Mtok | $0.18 | $0.60 |
| 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 Saba 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 3.3x cheaper ($0.18 vs $0.60 per million) and a larger context window (131K against 33K tokens).
Which is cheaper, Ling 3.0 Flash VL or Saba?
Ling 3.0 Flash VL costs $0.06 per million input tokens and $0.18 per million output tokens; Saba costs $0.20 and $0.60. Cost per solved task is known for neither of them.
Which has the bigger context window, Ling 3.0 Flash VL or Saba?
Ling 3.0 Flash VL, with 131K tokens against 33K.
How many benchmarks is the Ling 3.0 Flash VL vs Saba comparison based on?
3 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 Saba's on 0. Where only one model has a result, the page says so instead of naming a winner.