GPT-4o vs Ministral 3 8B 2512
Ministral 3 8B 2512 costs 66.7x less per output token than GPT-4o ($0.15 vs $10.00 per million). Updated October 22, 2026; 4 benchmarks have a result for both.
GPT-4o or Ministral 3 8B 2512: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-4o for work measured by Design Arena: websites or Design Arena: game dev, where it leads.
- Shared results
- 4
- GPT-4o only
- 23
- Ministral 3 8B 2512 only
- 3
- Like-for-like categories
- 1 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
No call
Only Ministral 3 8B 2512 has a AA Intelligence Index result, 5.5.
Directional only
Agentic work
AA Agentic Index
No call
Only Ministral 3 8B 2512 has a AA Agentic Index result, 0.6.
Directional only
Web design
Design Arena: websites
Ministral 3 8B 2512
Ministral 3 8B 2512 leads, 1065 against 831.
Same board, both measured
Long documents
Listed context window
Ministral 3 8B 2512
Ministral 3 8B 2512 takes 262K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ministral 3 8B 2512
GPT-4o $0.0075, Ministral 3 8B 2512 $0.0002 per turn.
List prices
At a glance
- Coding index
- GPT-4o not benchmarked, Ministral 3 8B 2512 not benchmarked.
- Widest category
- Web design, 21.1 points apart on average over 4 shared benchmarks.
- Widest single gap
- Design Arena: websites: GPT-4o 831 against Ministral 3 8B 2512 1065, 29.3 points apart on a 0 to 100 scale.
- Output price
- GPT-4o $10.00 against Ministral 3 8B 2512 $0.15 per million output tokens, 66.7x apart.
- Context window
- GPT-4o takes 128K tokens, Ministral 3 8B 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 | Bars | Ministral 3 8B 2512 | Ties |
|---|---|---|---|---|
| Web design | 0 | 4 | ||
| All shared boards | 0 | 4 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index8 boards, none shared
| Benchmark | GPT-4o | Ministral 3 8B 2512 | Leader |
|---|---|---|---|
| SWE-bench Verified | 38.8% | Not measured | Only GPT-4o measured |
| SWE-bench Lite | 38.0% | Not measured | Only GPT-4o measured |
| SWE-bench (full test set) | 18.8% | Not measured | Only GPT-4o measured |
| SWE-bench Multimodal | 30.4% | Not measured | Only GPT-4o measured |
| AA Coding Index | Not measured | 9.7 | Only Ministral 3 8B 2512 measured |
| LiveCodeBench (Artificial Analysis run) | 30.9% | Not measured | Only GPT-4o measured |
| Terminal-Bench Hard (Artificial Analysis run) | 8.3% | Not measured | Only GPT-4o measured |
| SciCode | 1.5% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-4o | Ministral 3 8B 2512 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 5.5 | Only Ministral 3 8B 2512 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-4o | Ministral 3 8B 2512 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 0.6 | Only Ministral 3 8B 2512 measured |
| τ²-Bench Telecom | 25.1% | Not measured | Only GPT-4o measured |
| GDPval | 9.9% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-4o | Ministral 3 8B 2512 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 54.3% | Not measured | Only GPT-4o measured |
| Humanity's Last Exam (Artificial Analysis run) | 2.4% | Not measured | Only GPT-4o measured |
| ARC-AGI | 4.5% | Not measured | Only GPT-4o measured |
| ARC-AGI-2 | 0.0% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o | Ministral 3 8B 2512 | Leader |
|---|---|---|---|
| AA Math Index | 6.0 | Not measured | Only GPT-4o measured |
| AIME 2025 (Artificial Analysis run) | 6.0% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o | Ministral 3 8B 2512 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 74.8% | Not measured | Only GPT-4o measured |
| MMLU | 88.1% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 16 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GPT-4o | Ministral 3 8B 2512 | Leader |
|---|---|---|---|
| Design Arena: websites | 831 | 1065 | Ministral 3 8B 2512 |
| Design Arena: UI components | 882 | Not measured | Only GPT-4o measured |
| Design Arena: game dev | 906 | 990 | Ministral 3 8B 2512 |
| Design Arena: data viz | 858 | Not measured | Only GPT-4o measured |
| Design Arena: 3D | 868 | 1031 | Ministral 3 8B 2512 |
| Design Arena: all code categories | 860 | 1053 | Ministral 3 8B 2512 |
| Neither model has a result on the other 3 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-4o | Ministral 3 8B 2512 | Leader |
|---|---|---|---|
| IFBench | 34.3% | Not measured | Only GPT-4o measured |
| Long context reasoning (AA-LCR) | 49.3% | Not measured | Only GPT-4o measured |
| DTBench | 64.5% | Not measured | Only GPT-4o measured |
| LMCA | 16.6% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 42 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o | Ministral 3 8B 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 8B 2512 costs less
Repository review
50K input + 3K output tokens
Ministral 3 8B 2512 costs less
Agent loop
200K input + 10K output tokens
Ministral 3 8B 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-4o | Mistral Ministral 3 8B 2512 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | Open weights |
| Released | May 13, 2024 | December 2, 2025 |
| Context window | 128K | 262K |
| Input $/Mtok | $2.50 | $0.15 |
| Output $/Mtok | $10.00 | $0.15 |
| 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 better than Ministral 3 8B 2512 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4o for work measured by Design Arena: websites or Design Arena: game dev, where it leads.
Which is cheaper, GPT-4o or Ministral 3 8B 2512?
GPT-4o costs $2.50 per million input tokens and $10.00 per million output tokens; Ministral 3 8B 2512 costs $0.15 and $0.15. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4o or Ministral 3 8B 2512?
Ministral 3 8B 2512, with 262K tokens against 128K.
How many benchmarks is the GPT-4o vs Ministral 3 8B 2512 comparison based on?
4 benchmarks have a result for both models as of October 22, 2026. GPT-4o's coding index rests on 0 independent benchmarks and Ministral 3 8B 2512's on 0. Where only one model has a result, the page says so instead of naming a winner.