Ling 3.0 Flash VL vs Mistral Small 4
Ling 3.0 Flash VL leads Mistral Small 4 on the Artificial Analysis Intelligence Index, 24.6 to 11.3, and Ling 3.0 Flash VL costs 3.3x less per output token ($0.18 vs $0.60 per million). Updated October 22, 2026; 7 benchmarks have a result for both.
Ling 3.0 Flash VL or Mistral Small 4: 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).
- Shared results
- 7
- Ling 3.0 Flash VL only
- 0
- Mistral Small 4 only
- 8
- Like-for-like categories
- 5 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 11.3.
Same board, both measured
Agentic work
AA Agentic Index
Ling 3.0 Flash VL
Ling 3.0 Flash VL leads, 28.7 against 0.8.
Same board, both measured
Long documents
Listed context window
Mistral Small 4
Mistral Small 4 takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash VL
Ling 3.0 Flash VL $0.0001, Mistral Small 4 $0.0004 per turn.
List prices
At a glance
- Coding index
- Ling 3.0 Flash VL not benchmarked, Mistral Small 4 not benchmarked.
- Widest category
- General, 28.7 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Coding Index: Ling 3.0 Flash VL 57.0 against Mistral Small 4 26.6, 30.4 points apart on a 0 to 100 scale.
- Output price
- Ling 3.0 Flash VL $0.18 against Mistral Small 4 $0.60 per million output tokens, 3.3x apart.
- Context window
- Ling 3.0 Flash VL takes 131K tokens, Mistral Small 4 262K.
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 | Mistral Small 4 | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 7 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | Ling 3.0 Flash VL | Mistral Small 4 | Leader |
|---|---|---|---|
| AA Coding Index | 57.0 | 26.6 | Ling 3.0 Flash VL |
| Vibe Code Bench (Vals AI) | Not measured | 0.0% | Only Mistral Small 4 measured |
| SciCode | 44.2% | 38.8% | Ling 3.0 Flash VL |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 17.4% | Only Mistral Small 4 measured |
| ALE-Bench | Not measured | 497.6 | Only Mistral Small 4 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 | Mistral Small 4 | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.6 | 11.3 | Ling 3.0 Flash VL |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Ling 3.0 Flash VL | Mistral Small 4 | Leader |
|---|---|---|---|
| AA Agentic Index | 28.7 | 0.8 | Ling 3.0 Flash VL |
| τ²-Bench Telecom | Not measured | 41.2% | Only Mistral Small 4 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 | Mistral Small 4 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 86.2% | 76.9% | Ling 3.0 Flash VL |
| Humanity's Last Exam (Artificial Analysis run) | 22.0% | 9.9% | Ling 3.0 Flash VL |
| Kagi LLM Benchmark | Not measured | 40.5% | Only Mistral Small 4 measured |
| 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 | Mistral Small 4 | Leader |
|---|---|---|---|
| Neither model has a result on 14 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Mistral Small 4 | Leader |
|---|---|---|---|
| Neither model has a result on 20 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Mistral Small 4 | 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 | Mistral Small 4 | Leader |
|---|---|---|---|
| IFBench | Not measured | 48.2% | Only Mistral Small 4 measured |
| Long context reasoning (AA-LCR) | 78.3% | 49.7% | Ling 3.0 Flash VL |
| DTBench | Not measured | 70.9% | Only Mistral Small 4 measured |
| LMCA | Not measured | 20.6% | Only Mistral Small 4 measured |
| 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 | Ling 3.0 Flash VL | Mistral Small 4 | 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 Small 4 |
|---|---|---|
| Maker | inclusionAI | Mistral AI |
| Weights | Open weights | Open weights |
| Released | September 10, 2026 | March 16, 2026 |
| Context window | 131K | 262K |
| Input $/Mtok | $0.06 | $0.15 |
| Output $/Mtok | $0.18 | $0.60 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Ling 3.0 Flash VL better than Mistral Small 4 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).
Which is cheaper, Ling 3.0 Flash VL or Mistral Small 4?
Ling 3.0 Flash VL costs $0.06 per million input tokens and $0.18 per million output tokens; Mistral Small 4 costs $0.15 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 Mistral Small 4?
Mistral Small 4, with 262K tokens against 131K.
How many benchmarks is the Ling 3.0 Flash VL vs Mistral Small 4 comparison based on?
7 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 Mistral Small 4's on 1. Where only one model has a result, the page says so instead of naming a winner.