GPT-3.5 Turbo vs Ministral 3 3B 2512
GPT-3.5 Turbo leads Ministral 3 3B 2512 on the Artificial Analysis Intelligence Index, 5.5 to 4.8, and Ministral 3 3B 2512 costs 15.0x less per output token ($0.10 vs $1.50 per million). Updated October 22, 2026; 2 benchmarks have a result for both.
GPT-3.5 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-3.5 Turbo only
- 11
- 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-3.5 Turbo
GPT-3.5 Turbo leads, 5.5 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
Reasoning
GPQA Diamond
No call
Only GPT-3.5 Turbo has a GPQA Diamond result, 30.6%.
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 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ministral 3 3B 2512
GPT-3.5 Turbo $0.0013, Ministral 3 3B 2512 $0.0001 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo not benchmarked, Ministral 3 3B 2512 not benchmarked.
- Widest category
- Coding, 5.9 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Coding Index: GPT-3.5 Turbo 10.7 against Ministral 3 3B 2512 4.8, 5.9 points apart on a 0 to 100 scale.
- Output price
- GPT-3.5 Turbo $1.50 against Ministral 3 3B 2512 $0.10 per million output tokens, 15.0x apart.
- Context window
- GPT-3.5 Turbo takes 16K 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-3.5 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-3.5 Turbo | Ministral 3 3B 2512 | Leader |
|---|---|---|---|
| AA Coding Index | 10.7 | 4.8 | GPT-3.5 Turbo |
| SciCode | 0.0% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 41 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-3.5 Turbo | Ministral 3 3B 2512 | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.5 | 4.8 | GPT-3.5 Turbo |
| 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 | GPT-3.5 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 index3 boards, none shared
| Benchmark | GPT-3.5 Turbo | Ministral 3 3B 2512 | Leader |
|---|---|---|---|
| GPQA Diamond | 30.6% | Not measured | Only GPT-3.5 Turbo measured |
| GPQA Diamond (Artificial Analysis run) | 29.7% | Not measured | Only GPT-3.5 Turbo measured |
| BIG-Bench Hard | 61.6% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-3.5 Turbo | Ministral 3 3B 2512 | Leader |
|---|---|---|---|
| GSM8K | 57.8% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 13 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-3.5 Turbo | Ministral 3 3B 2512 | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 64.4% | Not measured | Only GPT-3.5 Turbo measured |
| MedQA (Vals AI) | 58.5% | Not measured | Only GPT-3.5 Turbo measured |
| MMLU Pro (Artificial Analysis run) | 46.2% | Not measured | Only GPT-3.5 Turbo measured |
| MMLU | 68.5% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 16 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-3.5 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 | 962 | 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 index2 boards, none shared
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 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-3.5 Turbo | Mistral Ministral 3 3B 2512 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | Open weights |
| Released | May 28, 2023 | December 2, 2025 |
| Context window | 16K | 131K |
| Input $/Mtok | $0.50 | $0.10 |
| Output $/Mtok | $1.50 | $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-3.5 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-3.5 Turbo or Ministral 3 3B 2512?
GPT-3.5 Turbo costs $0.50 per million input tokens and $1.50 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-3.5 Turbo or Ministral 3 3B 2512?
Ministral 3 3B 2512, with 131K tokens against 16K.
How many benchmarks is the GPT-3.5 Turbo vs Ministral 3 3B 2512 comparison based on?
2 benchmarks have a result for both models as of October 22, 2026. GPT-3.5 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.