GPT-4o-mini vs Ministral 3 14B 2512
GPT-4o-mini leads Ministral 3 14B 2512 on the Artificial Analysis Intelligence Index, 6.7 to 6.0, and Ministral 3 14B 2512 costs 3.0x less per output token ($0.20 vs $0.60 per million). Updated October 22, 2026; 2 benchmarks have a result for both.
GPT-4o-mini or Ministral 3 14B 2512: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-4o-mini for work measured by AA Coding Index, where it leads.
- Shared results
- 2
- GPT-4o-mini only
- 27
- Ministral 3 14B 2512 only
- 5
- 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
GPT-4o-mini
GPT-4o-mini leads, 6.7 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
Reasoning
GPQA Diamond
No call
Only GPT-4o-mini has a GPQA Diamond result, 44.2%.
Directional only
Math
AIME
No call
Only GPT-4o-mini has a AIME result, 11.5%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-4o-mini has a MMLU Pro result, 62.7%.
Directional only
Web design
Design Arena: websites
No call
Only Ministral 3 14B 2512 has a Design Arena: websites result, 1081.
Directional only
Long documents
Listed context window
Ministral 3 14B 2512
Ministral 3 14B 2512 takes 262K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ministral 3 14B 2512
GPT-4o-mini $0.0004, Ministral 3 14B 2512 $0.0003 per turn.
List prices
At a glance
- Coding index
- GPT-4o-mini not benchmarked, Ministral 3 14B 2512 not benchmarked.
- Widest category
- Coding, 3.0 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Coding Index: GPT-4o-mini 11.4 against Ministral 3 14B 2512 14.4, 3.0 points apart on a 0 to 100 scale.
- Output price
- GPT-4o-mini $0.60 against Ministral 3 14B 2512 $0.20 per million output tokens, 3.0x apart.
- Context window
- GPT-4o-mini takes 128K 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 | GPT-4o-mini | Bars | Ministral 3 14B 2512 | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 1 | 0 | ||
| All shared boards | 1 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | GPT-4o-mini | Ministral 3 14B 2512 | Leader |
|---|---|---|---|
| AA Coding Index | 11.4 | 14.4 | Ministral 3 14B 2512 |
| LiveCodeBench (Vals AI) | 26.4% | Not measured | Only GPT-4o-mini measured |
| LiveCodeBench (Artificial Analysis run) | 23.4% | Not measured | Only GPT-4o-mini measured |
| SciCode | 1.5% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 39 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-4o-mini | Ministral 3 14B 2512 | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.7 | 6.0 | GPT-4o-mini |
| Epoch Capabilities Index | 126.5 | Not measured | Only GPT-4o-mini measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-4o-mini | 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 index6 boards, none shared
| Benchmark | GPT-4o-mini | Ministral 3 14B 2512 | Leader |
|---|---|---|---|
| GPQA Diamond | 44.2% | Not measured | Only GPT-4o-mini measured |
| MMMU Pro (Vals AI) | 56.6% | Not measured | Only GPT-4o-mini measured |
| GPQA Diamond (Artificial Analysis run) | 42.6% | Not measured | Only GPT-4o-mini measured |
| Humanity's Last Exam (Artificial Analysis run) | 4.2% | Not measured | Only GPT-4o-mini measured |
| ARC-AGI-2 | 0.0% | Not measured | Only GPT-4o-mini measured |
| Kagi LLM Benchmark | 28.8% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-4o-mini | Ministral 3 14B 2512 | Leader |
|---|---|---|---|
| AIME | 11.5% | Not measured | Only GPT-4o-mini measured |
| MATH 500 (Vals AI) | 72.6% | Not measured | Only GPT-4o-mini measured |
| MGSM (Vals AI) | 86.2% | Not measured | Only GPT-4o-mini measured |
| AA Math Index | 14.7 | Not measured | Only GPT-4o-mini measured |
| AIME 2025 (Artificial Analysis run) | 14.7% | Not measured | Only GPT-4o-mini measured |
| GSM8K | 91.3% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-4o-mini | Ministral 3 14B 2512 | Leader |
|---|---|---|---|
| MMLU Pro | 62.7% | Not measured | Only GPT-4o-mini measured |
| CorpFin (Vals AI) | 45.5% | Not measured | Only GPT-4o-mini measured |
| TaxEval (Vals AI) | 60.5% | Not measured | Only GPT-4o-mini measured |
| MortgageTax (Vals AI) | 54.5% | Not measured | Only GPT-4o-mini measured |
| MedQA (Vals AI) | 72.4% | Not measured | Only GPT-4o-mini measured |
| MMLU Pro (Artificial Analysis run) | 64.8% | Not measured | Only GPT-4o-mini measured |
| MMLU | 81.8% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-4o-mini | Ministral 3 14B 2512 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1081 | Only Ministral 3 14B 2512 measured |
| Design Arena: game dev | Not measured | 1037 | Only Ministral 3 14B 2512 measured |
| Design Arena: 3D | Not measured | 989 | Only Ministral 3 14B 2512 measured |
| Design Arena: all code categories | Not measured | 1064 | Only Ministral 3 14B 2512 measured |
| Neither model has a result on the other 5 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-4o-mini | Ministral 3 14B 2512 | Leader |
|---|---|---|---|
| IFBench | 31.0% | Not measured | Only GPT-4o-mini measured |
| DTBench | 54.4% | Not measured | Only GPT-4o-mini measured |
| LMCA | 10.4% | Not measured | Only GPT-4o-mini measured |
| PIQA | 88.7% | Not measured | Only GPT-4o-mini 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 | GPT-4o-mini | 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
GPT-4o-mini costs less
Agent loop
200K input + 10K output tokens
GPT-4o-mini 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 | OpenAI GPT-4o-mini | Mistral Ministral 3 14B 2512 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | Open weights |
| Released | July 18, 2024 | December 2, 2025 |
| Context window | 128K | 262K |
| Input $/Mtok | $0.15 | $0.20 |
| Output $/Mtok | $0.60 | $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 GPT-4o-mini better than Ministral 3 14B 2512 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4o-mini for work measured by AA Coding Index, where it leads.
Which is cheaper, GPT-4o-mini or Ministral 3 14B 2512?
GPT-4o-mini costs $0.15 per million input tokens and $0.60 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, GPT-4o-mini or Ministral 3 14B 2512?
Ministral 3 14B 2512, with 262K tokens against 128K.
How many benchmarks is the GPT-4o-mini vs Ministral 3 14B 2512 comparison based on?
2 benchmarks have a result for both models as of October 22, 2026. GPT-4o-mini'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.