GPT-4 Turbo vs Ministral 3 3B 2512
GPT-4 Turbo leads Ministral 3 3B 2512 on the Artificial Analysis Intelligence Index, 7.0 to 4.8, and Ministral 3 3B 2512 costs 300.0x less per output token ($0.10 vs $30.00 per million). Updated October 23, 2026; 2 benchmarks have a result for both.
GPT-4 Turbo or Ministral 3 3B 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
- 2
- GPT-4 Turbo only
- 40
- Ministral 3 3B 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-4 Turbo
GPT-4 Turbo leads, 7.0 against 4.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Ministral 3 3B 2512 has a AA Agentic Index result, 0.8.
Directional only
Web design
Design Arena: websites
No call
Only Ministral 3 3B 2512 has a Design Arena: websites result, 1027.
Directional only
Long documents
Listed context window
Ministral 3 3B 2512
Ministral 3 3B 2512 takes 131K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ministral 3 3B 2512
GPT-4 Turbo $0.025, Ministral 3 3B 2512 $0.0001 per turn.
List prices
At a glance
- Coding index
- GPT-4 Turbo not benchmarked, Ministral 3 3B 2512 not benchmarked.
- Widest category
- Coding, 16.7 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Coding Index: GPT-4 Turbo 21.5 against Ministral 3 3B 2512 4.8, 16.7 points apart on a 0 to 100 scale.
- Output price
- GPT-4 Turbo $30.00 against Ministral 3 3B 2512 $0.10 per million output tokens, 300.0x apart.
- Context window
- GPT-4 Turbo takes 128K tokens, Ministral 3 3B 2512 131K.
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-4 Turbo | Bars | Ministral 3 3B 2512 | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| All shared boards | 2 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | GPT-4 Turbo | Ministral 3 3B 2512 | Leader |
|---|---|---|---|
| AA Coding Index | 21.5 | 4.8 | GPT-4 Turbo |
| LiveCodeBench (Artificial Analysis run) | 29.1% | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: coding | 1268 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: software and IT | 1267 | Not measured | Only GPT-4 Turbo 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-4 Turbo | Ministral 3 3B 2512 | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.0 | 4.8 | GPT-4 Turbo |
| Epoch Capabilities Index | 127.3 | Not measured | Only GPT-4 Turbo measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-4 Turbo | Ministral 3 3B 2512 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 0.8 | Only Ministral 3 3B 2512 measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-4 Turbo | Ministral 3 3B 2512 | Leader |
|---|---|---|---|
| Humanity's Last Exam (Artificial Analysis run) | 3.1% | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: hard prompts | 1251 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: expert | 1223 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: hard prompts in English | 1266 | Not measured | Only GPT-4 Turbo measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4 Turbo | Ministral 3 3B 2512 | Leader |
|---|---|---|---|
| LMArena Text: math | 1272 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: mathematical work | 1268 | Not measured | Only GPT-4 Turbo measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-4 Turbo | Ministral 3 3B 2512 | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 80.5% | Not measured | Only GPT-4 Turbo measured |
| MedQA (Vals AI) | 82.0% | Not measured | Only GPT-4 Turbo measured |
| MMLU Pro (Artificial Analysis run) | 69.4% | Not measured | Only GPT-4 Turbo measured |
| MMLU | 81.3% | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: science | 1256 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: medicine and healthcare | 1239 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: legal and government | 1265 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: business and finance | 1222 | Not measured | Only GPT-4 Turbo measured |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-4 Turbo | Ministral 3 3B 2512 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1027 | Only Ministral 3 3B 2512 measured |
| Design Arena: game dev | Not measured | 948 | Only Ministral 3 3B 2512 measured |
| Design Arena: 3D | Not measured | 963 | Only Ministral 3 3B 2512 measured |
| Design Arena: all code categories | Not measured | 1009 | Only Ministral 3 3B 2512 measured |
| Neither model has a result on the other 5 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index22 boards, none shared
| Benchmark | GPT-4 Turbo | Ministral 3 3B 2512 | Leader |
|---|---|---|---|
| DTBench | 61.6% | Not measured | Only GPT-4 Turbo measured |
| LMCA | 9.8% | Not measured | Only GPT-4 Turbo measured |
| LMArena Text | 1272 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: instruction following | 1249 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: creative writing | 1268 | Not measured | Only GPT-4 Turbo measured |
| LMArena Vision | 1090 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: English | 1291 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: non-English | 1245 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: Chinese | 1242 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: German | 1259 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: Japanese | 1194 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: Korean | 1187 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: French | 1276 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: Spanish | 1260 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: Russian | 1259 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: Polish | 1302 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: multi-turn | 1267 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: longer queries | 1253 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: writing and language | 1279 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: entertainment and media | 1258 | Not measured | Only GPT-4 Turbo measured |
| LMArena Vision: English | 1097 | Not measured | Only GPT-4 Turbo measured |
| LMArena Vision: Chinese | 1045 | Not measured | Only GPT-4 Turbo measured |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4 Turbo | Ministral 3 3B 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 3B 2512 costs less
Repository review
50K input + 3K output tokens
Ministral 3 3B 2512 costs less
Agent loop
200K input + 10K output tokens
Ministral 3 3B 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 | OpenAI GPT-4 Turbo | Mistral Ministral 3 3B 2512 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | Open weights |
| Released | April 9, 2024 | December 2, 2025 |
| Context window | 128K | 131K |
| Input $/Mtok | $10.00 | $0.10 |
| Output $/Mtok | $30.00 | $0.10 |
| 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-4 Turbo better than Ministral 3 3B 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, GPT-4 Turbo or Ministral 3 3B 2512?
GPT-4 Turbo costs $10.00 per million input tokens and $30.00 per million output tokens; Ministral 3 3B 2512 costs $0.10 and $0.10. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4 Turbo or Ministral 3 3B 2512?
Ministral 3 3B 2512, with 131K tokens against 128K.
How many benchmarks is the GPT-4 Turbo vs Ministral 3 3B 2512 comparison based on?
2 benchmarks have a result for both models as of October 23, 2026. GPT-4 Turbo's coding index rests on 0 independent benchmarks and Ministral 3 3B 2512's on 0. Where only one model has a result, the page says so instead of naming a winner.