AI model comparison
Ministral 3 14B 2512 vs Llama 4 Scout
Pricing, context window, benchmarks, and features compared side by side. Or skip the guesswork and send one prompt to both.
Mistral
Ministral 3 14B 2512
The largest model in the Ministral 3 family, Ministral 3 14B offers frontier capabilities and performance comparable to its larger Mistral Small 3.2 24B counterpart.
- Input / 1M
- $0.20
- Output / 1M
- $0.20
- Context
- 262K
Meta
Llama 4 Scout
Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B.
- Input / 1M
- $0.10
- Output / 1M
- $0.30
- Context
- 1.31M
At a glance
Quick verdict
How the two models stack up on the things people ask about most.
Lower price
Llama 4 Scout
1.4x cheaper for a typical chat
Higher intelligence score
Llama 4 Scout
6.5 vs 6 on Artificial Analysis
Larger context window
Llama 4 Scout
1.31M vs 262K tokens
Newer release
Ministral 3 14B 2512
Released December 2, 2025
Benchmarks
Benchmark scores
Independent scores from Artificial Analysis. Higher is better.
Intelligence index
Ministral 3 14B 25126Coding index
Ministral 3 14B 251214.4Agentic index
Ministral 3 14B 25121.1Pricing
Ministral 3 14B 2512 vs Llama 4 Scout API pricing
Per-token API rates. Cheaper option highlighted.
| Metric | Ministral 3 14B 2512 | |
|---|---|---|
| Input tokensPer 1M tokens | $0.20 | $0.10 |
| Output tokensPer 1M tokens | $0.20 | $0.30 |
| Cached input (read)Per 1M tokens | $0.02 | Not available |
| Cache writePer 1M tokens | Not available | Not available |
What it costs in practice
Estimated cost per 1,000 requests at standard rates. Coding and long-document figures also show the cost when the input is already cached.
Chat message
2K in, 500 out
Ministral 3 14B 2512$0.50Coding task
30K in, 4K out
Ministral 3 14B 2512$6.80$1.40 cachedLong document summary
150K in, 2K out
Ministral 3 14B 2512$30.40$3.40 cachedSpecs
Context window and capabilities
How much each model can read, how much it can write, and what it accepts as input.
| Metric | Ministral 3 14B 2512 | |
|---|---|---|
| Context window | 262K tokens | 1.31M tokens |
| Max output | 210K tokens | 16K tokens |
| Input types | Image, Text | Image, Text |
| Output types | Text | Text |
| Reasoning effort levels | Not available | Not available |
| Default reasoning effort | Not available | Not available |
| Release date | December 2, 2025 | April 5, 2025 |
Features
Supported features
API features available for each model.
| Metric | Ministral 3 14B 2512 | |
|---|---|---|
| Tool calling | Supported | Supported |
| Structured outputs | Supported | Supported |
| JSON mode | Supported | Supported |
| Reasoning | Not supported | Not supported |
| Temperature | Supported | Supported |
| Stop sequences | Supported | Supported |
| Deterministic seed | Supported | Supported |
| Verbosity control | Not supported | Not supported |
Keep exploring
More on these models
Other head-to-heads, the full model page, and the alternatives list for each model.
Ministral 3 14B 2512View full model details- Ministral 3 14B 2512 vs Gemma 3 12B
- Ministral 3 14B 2512 vs Nova Micro 1.0
- Ministral 3 14B 2512 vs Mistral Small 3
- Ministral 3 14B 2512 vs Qwen3 14B
- Ministral 3 14B 2512 vs Nova Lite 1.0
- Ministral 3 14B 2512 vs Ministral 3 8B 2512
- Ministral 3 14B 2512 vs Saba
- Ministral 3 14B 2512 vs Reka Flash 3
- Ministral 3 14B 2512 vs Phi 4
- Ministral 3 14B 2512 vs Ministral 3 3B 2512
- Ministral 3 14B 2512 vs Llama 3.2 3B Instruct
- Ministral 3 14B 2512 vs Granite 4.0 Micro
- Ministral 3 14B 2512 vs Llama 3.2 1B Instruct
- Llama 4 Scout vs GPT-4o-mini
- Llama 4 Scout vs Gemma 3 27B
- Llama 4 Scout vs Llama 3.1 8B Instruct
- Llama 4 Scout vs Llama 3.3 70B Instruct
- Llama 4 Scout vs Qwen3 30B A3B Instruct 2507
- Llama 4 Scout vs Llama 4 Maverick
- Llama 4 Scout vs Nova Micro 1.0
- Llama 4 Scout vs Qwen3 32B
- Llama 4 Scout vs Qwen3 14B
- Llama 4 Scout vs Llama 3.1 70B Instruct
- Llama 4 Scout vs Phi 4
- Llama 4 Scout vs Nova Lite 1.0
- Llama 4 Scout vs Saba
- Llama 4 Scout vs Reka Flash 3
- Llama 4 Scout vs Llama 3.2 3B Instruct
- Llama 4 Scout vs Llama 3.2 1B Instruct
Our take
Llama 4 Scout is 1.4x cheaper for a typical chat. Llama 4 Scout scores higher on the Artificial Analysis Intelligence Index (6.5 vs 6). Llama 4 Scout has the larger context window (1.31M vs 262K tokens). The right pick depends on your workload, so the quickest way to settle it is to send the same prompt to both and compare.
FAQ
Frequently asked questions
Is Ministral 3 14B 2512 or Llama 4 Scout cheaper?
Llama 4 Scout is cheaper for a typical chat (2K input tokens and 500 output tokens). Llama 4 Scout costs $0.10 per 1M input tokens and $0.30 per 1M output tokens, while Ministral 3 14B 2512 costs $0.20 per 1M input tokens and $0.20 per 1M output tokens.
Which has a bigger context window, Ministral 3 14B 2512 or Llama 4 Scout?
Llama 4 Scout supports up to 1.31M tokens of context, compared with 262K for Ministral 3 14B 2512.
Is Ministral 3 14B 2512 smarter than Llama 4 Scout?
On the Artificial Analysis Intelligence Index, Ministral 3 14B 2512 scores 6 and Llama 4 Scout scores 6.5, putting Llama 4 Scout ahead. Benchmarks don't capture everything, so the best test is running your own prompts through both.
Can I use Ministral 3 14B 2512 and Llama 4 Scout at the same time?
Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see Ministral 3 14B 2512 and Llama 4 Scout answer side by side and keep the better response.
Why choose? Ask both.
Send one prompt to Ministral 3 14B 2512 and Llama 4 Scout at the same time. Compare the answers side by side and keep the best one.