| Signal | Llama 3.1 405B (base) | Delta | Mistral Nemo |
|---|---|---|---|
Capabilities | 17 | -33 | |
Pricing | 4 | +4 | |
Context window size | 72 | -9 | |
Recency | 24 | +3 | |
Output Capacity | 75 | +5 | |
| Overall Result | 3 wins | of 5 | 2 wins |
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Meta
Mistral AI
Mistral Nemo saves you $596.00/month
That's $7152.00/year compared to Llama 3.1 405B (base) at your current usage level of 100K calls/month.
| Metric | Llama 3.1 405B (base) | Mistral Nemo | Winner |
|---|---|---|---|
| Overall Score | 39 | 51 | Mistral Nemo |
| Rank | #288 | #261 | Mistral Nemo |
| Quality Rank | #288 | #261 | Mistral Nemo |
| Adoption Rank | #288 | #261 | Mistral Nemo |
| Parameters | 405B | -- | -- |
| Context Window | 33K | 131K | Mistral Nemo |
| Pricing | $4.00/$4.00/M | $0.02/$0.04/M | -- |
| Signal Scores | |||
| Capabilities | 17 | 50 | Mistral Nemo |
| Pricing | 4 | 0 | Llama 3.1 405B (base) |
| Context window size | 72 | 81 | Mistral Nemo |
| Recency | 24 | 21 | Llama 3.1 405B (base) |
| Output Capacity | 75 | 70 | Llama 3.1 405B (base) |
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 39/100 (rank #288), placing it in the top 1% of all 290 models tracked.
Scores 51/100 (rank #261), placing it in the top 10% of all 290 models tracked.
Mistral Nemo has a 12-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.
Mistral Nemo offers 99% better value per quality point. At 1M tokens/day, you'd spend $0.90/month with Mistral Nemo vs $120.00/month with Llama 3.1 405B (base) - a $119.10 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. Mistral Nemo also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (131K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.04/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (51/100) correlates with better nuance, coherence, and style in long-form content
Mistral Nemo clearly outperforms Llama 3.1 405B (base) with a significant 12.100000000000001-point lead. For most general use cases, Mistral Nemo 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
Mistral Nemo
99% 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) | Mistral Nemo |
|---|---|---|
| Vision (Image Input) | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Meta
Mistral AI
Mistral Nemo saves you $11.92/month
That's 99% cheaper than Llama 3.1 405B (base) 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) | Mistral Nemo |
|---|---|---|
| Context Window | 33K | 131K |
| Max Output Tokens | 32,768 | 16,384 |
| Open Source | Yes | Yes |
| Created | Aug 2, 2024 | Jul 19, 2024 |
Mistral Nemo scores 51/100 (rank #261) compared to Llama 3.1 405B (base)'s 39/100 (rank #288), giving it a 12-point advantage. Mistral Nemo is the stronger overall choice, though Llama 3.1 405B (base) may excel in specific areas like certain benchmarks.
Llama 3.1 405B (base) is ranked #288 and Mistral Nemo is ranked #261 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.
Mistral Nemo is cheaper at $0.04/M output tokens vs Llama 3.1 405B (base)'s $4.00/M output tokens - 100.0x more expensive. Input token pricing: Llama 3.1 405B (base) at $4.00/M vs Mistral Nemo at $0.02/M.
Mistral Nemo has a larger context window of 131,072 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.