| Signal | Llama 3.1 405B (base) | Delta | Mistral Large 2411 |
|---|---|---|---|
Capabilities | 17 | -33 | |
Pricing | 4 | -2 | |
Context window size | 72 | -9 | |
Recency | 25 | -20 | |
Output Capacity | 75 | +55 | |
| Overall Result | 1 wins | of 5 | 4 wins |
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Meta
Mistral AI
Mistral Large 2411 saves you $100.00/month
That's $1200.00/year compared to Llama 3.1 405B (base) at your current usage level of 100K calls/month.
| Metric | Llama 3.1 405B (base) | Mistral Large 2411 | Winner |
|---|---|---|---|
| Overall Score | 38 | 51 | Mistral Large 2411 |
| Rank | #283 | #249 | Mistral Large 2411 |
| Quality Rank | #283 | #249 | Mistral Large 2411 |
| Adoption Rank | #283 | #249 | Mistral Large 2411 |
| Parameters | 405B | -- | -- |
| Context Window | 33K | 131K | Mistral Large 2411 |
| Pricing | $4.00/$4.00/M | $2.00/$6.00/M | -- |
| Signal Scores | |||
| Capabilities | 17 | 50 | Mistral Large 2411 |
| Pricing | 4 | 6 | Mistral Large 2411 |
| Context window size | 72 | 81 | Mistral Large 2411 |
| Recency | 25 | 45 | Mistral Large 2411 |
| Output Capacity | 75 | 20 | 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 38/100 (rank #283), placing it in the top 3% of all 290 models tracked.
Scores 51/100 (rank #249), placing it in the top 14% of all 290 models tracked.
Mistral Large 2411 has a 13-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.
Both models are priced similarly, so the decision comes down to quality and features rather than cost.
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 (131K 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 (51/100) correlates with better nuance, coherence, and style in long-form content
Mistral Large 2411 clearly outperforms Llama 3.1 405B (base) with a significant 12.599999999999994-point lead. For most general use cases, Mistral Large 2411 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)
0% 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 Large 2411 |
|---|---|---|
| Vision (Image Input) | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Meta
Mistral AI
Mistral Large 2411 saves you $1.20/month
That's 10% 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 Large 2411 |
|---|---|---|
| Context Window | 33K | 131K |
| Max Output Tokens | 32,768 | -- |
| Open Source | Yes | No |
| Created | Aug 2, 2024 | Nov 19, 2024 |
Mistral Large 2411 scores 51/100 (rank #249) compared to Llama 3.1 405B (base)'s 38/100 (rank #283), giving it a 13-point advantage. Mistral Large 2411 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 Mistral Large 2411 is ranked #249 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 Mistral Large 2411's $6.00/M output tokens — 1.5x more expensive. Input token pricing: Llama 3.1 405B (base) at $4.00/M vs Mistral Large 2411 at $2.00/M.
Mistral Large 2411 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.