| Signal | Llama 4 Scout | Delta | Ministral 3 3B 2512 |
|---|---|---|---|
Capabilities | 67 | -- | |
Pricing | 0 | +0 | |
Context window size | 88 | +6 | |
Recency | 69 | -31 | |
Output Capacity | 70 | +50 | |
| Overall Result | 3 wins | of 5 | 1 wins |
6
days ranked higher
4
days
20
days ranked higher
Meta
Mistral AI
Ministral 3 3B 2512 saves you $8.00/month
That's $96.00/year compared to Llama 4 Scout at your current usage level of 100K calls/month.
| Metric | Llama 4 Scout | Ministral 3 3B 2512 | Winner |
|---|---|---|---|
| Overall Score | 72 | 73 | Ministral 3 3B 2512 |
| Rank | #143 | #135 | Ministral 3 3B 2512 |
| Quality Rank | #143 | #135 | Ministral 3 3B 2512 |
| Adoption Rank | #143 | #135 | Ministral 3 3B 2512 |
| Parameters | -- | 3B | -- |
| Context Window | 328K | 131K | Llama 4 Scout |
| Pricing | $0.08/$0.30/M | $0.10/$0.10/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 67 | Llama 4 Scout |
| Pricing | 0 | 0 | Llama 4 Scout |
| Context window size | 88 | 81 | Llama 4 Scout |
| Recency | 69 | 100 | Ministral 3 3B 2512 |
| Output Capacity | 70 | 20 | Llama 4 Scout |
Our composite score (0–100) combines six weighted signals: benchmark performance (25%), pricing efficiency (25%), context window size (15%), model recency (15%), output capacity (10%), and capability versatility (10%). Here's what the scores mean for these two models:
Scores 72/100 (rank #143), placing it in the top 51% of all 290 models tracked.
Scores 73/100 (rank #135), placing it in the top 54% of all 290 models tracked.
With only a 1-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.
Ministral 3 3B 2512 offers 47% better value per quality point. At 1M tokens/day, you'd spend $3.00/month with Ministral 3 3B 2512 vs $5.70/month with Llama 4 Scout - a $2.70 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Higher benchmark score (0/100) indicates stronger performance on coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Faster response time (speed score 0/100) is critical for user-facing chat. Ministral 3 3B 2512 also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (328K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.10/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (73/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
Llama 4 Scout and Ministral 3 3B 2512 are extremely close in overall performance (only 0.5999999999999943 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Llama 4 Scout
Marginally better benchmark scores; both are excellent
Best for Cost
Ministral 3 3B 2512
47% lower pricing; better value at scale
Best for Reliability
Llama 4 Scout
Higher uptime and faster response speeds
Best for Prototyping
Llama 4 Scout
Stronger community support and better developer experience
Best for Production
Llama 4 Scout
Wider enterprise adoption and proven at scale
by Meta
by Mistral AI
| Capability | Llama 4 Scout | Ministral 3 3B 2512 |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Meta
Mistral AI
Ministral 3 3B 2512 saves you $0.2040/month
That's 40% cheaper than Llama 4 Scout at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | Llama 4 Scout | Ministral 3 3B 2512 |
|---|---|---|
| Context Window | 328K | 131K |
| Max Output Tokens | 16,384 | -- |
| Open Source | Yes | Yes |
| Created | Apr 5, 2025 | Dec 2, 2025 |
Ministral 3 3B 2512 scores 73/100 (rank #135) compared to Llama 4 Scout's 72/100 (rank #143), giving it a 1-point advantage. Ministral 3 3B 2512 is the stronger overall choice, though Llama 4 Scout may excel in specific areas like certain benchmarks.
Llama 4 Scout is ranked #143 and Ministral 3 3B 2512 is ranked #135 out of 290+ AI models. Rankings use a composite score combining benchmark performance (25%), pricing (25%), context window (15%), recency (15%), output capacity (10%), and versatility (10%). Scores update hourly.
Ministral 3 3B 2512 is cheaper at $0.10/M output tokens vs Llama 4 Scout's $0.30/M output tokens - 3.0x more expensive. Input token pricing: Llama 4 Scout at $0.08/M vs Ministral 3 3B 2512 at $0.10/M.
Llama 4 Scout has a larger context window of 327,680 tokens compared to Ministral 3 3B 2512's 131,072 tokens. A larger context window means the model can process longer documents and conversations.