Ling 3.1 Flash vs Mistral Small 4
Ling 3.1 Flash leads Mistral Small 4 on the Artificial Analysis Intelligence Index, 41.1 to 11.3, and Ling 3.1 Flash costs 2.7x less per output token ($0.225 vs $0.60 per million). Updated October 22, 2026; 4 benchmarks have a result for both.
Ling 3.1 Flash or Mistral Small 4: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Ling 3.1 Flash for output tokens 2.7x cheaper ($0.225 vs $0.60 per million).
- Shared results
- 4
- Ling 3.1 Flash only
- 0
- Mistral Small 4 only
- 11
- Like-for-like categories
- 4 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 3.1 Flash
Ling 3.1 Flash leads, 41.1 against 11.3.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mistral Small 4 has a AA Agentic Index result, 0.8.
Directional only
Long documents
Listed context window
No call
Both take 262K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.1 Flash
Ling 3.1 Flash $0.0002, Mistral Small 4 $0.0004 per turn.
List prices
At a glance
- Coding index
- Ling 3.1 Flash not benchmarked, Mistral Small 4 not benchmarked.
- Widest category
- General, 33.3 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Ling 3.1 Flash 83.0% against Mistral Small 4 49.7%, 33.3 points apart on a 0 to 100 scale.
- Output price
- Ling 3.1 Flash $0.225 against Mistral Small 4 $0.60 per million output tokens, 2.7x apart.
- Context window
- Both take 262K 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 3.1 Flash | Bars | Mistral Small 4 | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 4 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | Ling 3.1 Flash | Mistral Small 4 | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 26.6 | Only Mistral Small 4 measured |
| Vibe Code Bench (Vals AI) | Not measured | 0.0% | Only Mistral Small 4 measured |
| SciCode | 54.1% | 38.8% | Ling 3.1 Flash |
| 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 3.1 Flash | Mistral Small 4 | Leader |
|---|---|---|---|
| AA Intelligence Index | 41.1 | 11.3 | Ling 3.1 Flash |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Ling 3.1 Flash | Mistral Small 4 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 0.8 | Only Mistral Small 4 measured |
| 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 index10
| Benchmark | Ling 3.1 Flash | Mistral Small 4 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 76.9% | Only Mistral Small 4 measured |
| Humanity's Last Exam (Artificial Analysis run) | 39.4% | 9.9% | Ling 3.1 Flash |
| 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 3.1 Flash | 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 3.1 Flash | 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 3.1 Flash | 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 3.1 Flash | Mistral Small 4 | Leader |
|---|---|---|---|
| IFBench | Not measured | 48.2% | Only Mistral Small 4 measured |
| Long context reasoning (AA-LCR) | 83.0% | 49.7% | Ling 3.1 Flash |
| 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 3.1 Flash | 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 3.1 Flash costs less
Repository review
50K input + 3K output tokens
Ling 3.1 Flash costs less
Agent loop
200K input + 10K output tokens
Ling 3.1 Flash 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 3.1 Flash | Mistral Small 4 |
|---|---|---|
| Maker | inclusionAI | Mistral AI |
| Weights | No public weights listed | Open weights |
| Released | October 2, 2026 | March 16, 2026 |
| Context window | 262K | 262K |
| Input $/Mtok | $0.075 | $0.15 |
| Output $/Mtok | $0.225 | $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 3.1 Flash better than Mistral Small 4 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Ling 3.1 Flash for output tokens 2.7x cheaper ($0.225 vs $0.60 per million).
Which is cheaper, Ling 3.1 Flash or Mistral Small 4?
Ling 3.1 Flash costs $0.075 per million input tokens and $0.225 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 3.1 Flash or Mistral Small 4?
Neither: both take 262K tokens.
How many benchmarks is the Ling 3.1 Flash vs Mistral Small 4 comparison based on?
4 benchmarks have a result for both models as of October 22, 2026. Ling 3.1 Flash'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.