| Signal | Llama 3.1 405B (base) | Delta | o4 Mini Deep Research |
|---|---|---|---|
Capabilities | 17 | -83 | |
Pricing | 4 | -4 | |
Context window size | 72 | -12 | |
Recency | 25 | -75 | |
Output Capacity | 75 | -8 | |
| Overall Result | 0 wins | of 5 | 5 wins |
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Meta
OpenAI
| Metric | Llama 3.1 405B (base) | o4 Mini Deep Research | Winner |
|---|---|---|---|
| Overall Score | 38 | 94 | o4 Mini Deep Research |
| Rank | #283 | #16 | o4 Mini Deep Research |
| Quality Rank | #283 | #16 | o4 Mini Deep Research |
| Adoption Rank | #283 | #16 | o4 Mini Deep Research |
| Parameters | 405B | -- | -- |
| Context Window | 33K | 200K | o4 Mini Deep Research |
| Pricing | $4.00/$4.00/M | $2.00/$8.00/M | -- |
| Signal Scores | |||
| Capabilities | 17 | 100 | o4 Mini Deep Research |
| Pricing | 4 | 8 | o4 Mini Deep Research |
| Context window size | 72 | 84 | o4 Mini Deep Research |
| Recency | 25 | 100 | o4 Mini Deep Research |
| Output Capacity | 75 | 83 | o4 Mini Deep Research |
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 38/100 (rank #283), placing it in the top 3% of all 290 models tracked.
Scores 94/100 (rank #16), placing it in the top 95% of all 290 models tracked.
o4 Mini Deep Research has a 56-point advantage, which typically translates to noticeably stronger performance on complex reasoning, code generation, and multi-step tasks.
o4 Mini Deep Research offers 20% better value per quality point. At 1M tokens/day, you'd spend $120.00/month with Llama 3.1 405B (base) vs $150.00/month with o4 Mini Deep Research — a $30.00 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. Llama 3.1 405B (base) also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (200K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($4.00/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (94/100) 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
o4 Mini Deep Research clearly outperforms Llama 3.1 405B (base) with a significant 56.099999999999994-point lead. For most general use cases, o4 Mini Deep Research is the stronger choice. However, Llama 3.1 405B (base) may still excel in niche scenarios.
Best for Quality
Llama 3.1 405B (base)
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 3.1 405B (base)
20% lower pricing; better value at scale
Best for Reliability
Llama 3.1 405B (base)
Higher uptime and faster response speeds
Best for Prototyping
Llama 3.1 405B (base)
Stronger community support and better developer experience
Best for Production
Llama 3.1 405B (base)
Wider enterprise adoption and proven at scale
by Meta
| Capability | Llama 3.1 405B (base) | o4 Mini Deep Research |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoningdiffers | ||
| Web Searchdiffers | ||
| Image Output |
Meta
OpenAI
Llama 3.1 405B (base) saves you $1.20/month
That's 9% cheaper than o4 Mini Deep Research 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 3.1 405B (base) | o4 Mini Deep Research |
|---|---|---|
| Context Window | 33K | 200K |
| Max Output Tokens | 32,768 | 100,000 |
| Open Source | Yes | No |
| Created | Aug 2, 2024 | Oct 10, 2025 |
o4 Mini Deep Research scores 94/100 (rank #16) compared to Llama 3.1 405B (base)'s 38/100 (rank #283), giving it a 56-point advantage. o4 Mini Deep Research is the stronger overall choice, though Llama 3.1 405B (base) may excel in specific areas like cost efficiency.
Llama 3.1 405B (base) is ranked #283 and o4 Mini Deep Research is ranked #16 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.
Llama 3.1 405B (base) is cheaper at $4.00/M output tokens vs o4 Mini Deep Research's $8.00/M output tokens — 2.0x more expensive. Input token pricing: Llama 3.1 405B (base) at $4.00/M vs o4 Mini Deep Research at $2.00/M.
o4 Mini Deep Research has a larger context window of 200,000 tokens compared to Llama 3.1 405B (base)'s 32,768 tokens. A larger context window means the model can process longer documents and conversations.