DeepSeek V3.1 vs Ministral 3 14B 2512
DeepSeek V3.1 leads Ministral 3 14B 2512 on the Artificial Analysis Intelligence Index, 13.7 to 6.0, and Ministral 3 14B 2512 costs 7.5x less per output token ($0.20 vs $1.50 per million). Updated October 22, 2026; 5 benchmarks have a result for both.
DeepSeek V3.1 or Ministral 3 14B 2512: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 5
- DeepSeek V3.1 only
- 6
- Ministral 3 14B 2512 only
- 2
- 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
DeepSeek V3.1
DeepSeek V3.1 leads, 13.7 against 6.0.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Ministral 3 14B 2512 has a AA Agentic Index result, 1.1.
Directional only
Web design
Design Arena: websites
DeepSeek V3.1
DeepSeek V3.1 leads, 1122 against 1081.
Same board, both measured
Long documents
Listed context window
Ministral 3 14B 2512
Ministral 3 14B 2512 takes 262K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ministral 3 14B 2512
DeepSeek V3.1 $0.0013, Ministral 3 14B 2512 $0.0003 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.1 not benchmarked, Ministral 3 14B 2512 not benchmarked.
- Widest category
- Intelligence, 7.7 points apart on average over 1 shared benchmark.
- Widest single gap
- Design Arena: 3D: DeepSeek V3.1 1077 against Ministral 3 14B 2512 989, 11.0 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.1 $1.50 against Ministral 3 14B 2512 $0.20 per million output tokens, 7.5x apart.
- Context window
- DeepSeek V3.1 takes 164K tokens, Ministral 3 14B 2512 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 | DeepSeek V3.1 | Bars | Ministral 3 14B 2512 | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Web design | 4 | 0 | ||
| All shared boards | 5 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index1 board, none shared
| Benchmark | DeepSeek V3.1 | Ministral 3 14B 2512 | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 14.4 | Only Ministral 3 14B 2512 measured |
| Neither model has a result on the other 42 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3.1 | Ministral 3 14B 2512 | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.7 | 6.0 | DeepSeek V3.1 |
| 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 | DeepSeek V3.1 | Ministral 3 14B 2512 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.1 | Only Ministral 3 14B 2512 measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V3.1 | Ministral 3 14B 2512 | Leader |
|---|---|---|---|
| Kagi LLM Benchmark | 53.2% | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 22 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 | Ministral 3 14B 2512 | 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 | DeepSeek V3.1 | Ministral 3 14B 2512 | Leader |
|---|---|---|---|
| Neither model has a result on 20 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V3.1 | Ministral 3 14B 2512 | Leader |
|---|---|---|---|
| Design Arena: websites | 1122 | 1081 | DeepSeek V3.1 |
| Design Arena: UI components | 1083 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: game dev | 1084 | 1037 | DeepSeek V3.1 |
| Design Arena: data viz | 1096 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: 3D | 1077 | 989 | DeepSeek V3.1 |
| Design Arena: all code categories | 1112 | 1064 | DeepSeek V3.1 |
| Design Arena: SVG | 967 | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 | Ministral 3 14B 2512 | 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
Ministral 3 14B 2512 costs less
Repository review
50K input + 3K output tokens
Ministral 3 14B 2512 costs less
Agent loop
200K input + 10K output tokens
Ministral 3 14B 2512 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 | DeepSeek V3.1 | Mistral Ministral 3 14B 2512 |
|---|---|---|
| Maker | DeepSeek | Mistral AI |
| Weights | Open weights | Open weights |
| Released | August 21, 2025 | December 2, 2025 |
| Context window | 164K | 262K |
| Input $/Mtok | $0.55 | $0.20 |
| Output $/Mtok | $1.50 | $0.20 |
| 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 DeepSeek V3.1 better than Ministral 3 14B 2512 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, DeepSeek V3.1 or Ministral 3 14B 2512?
DeepSeek V3.1 costs $0.55 per million input tokens and $1.50 per million output tokens; Ministral 3 14B 2512 costs $0.20 and $0.20. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3.1 or Ministral 3 14B 2512?
Ministral 3 14B 2512, with 262K tokens against 164K.
How many benchmarks is the DeepSeek V3.1 vs Ministral 3 14B 2512 comparison based on?
5 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.1's coding index rests on 0 independent benchmarks and Ministral 3 14B 2512's on 0. Where only one model has a result, the page says so instead of naming a winner.