| Signal | Mistral Small 3.2 24B | Delta | R1 Distill Llama 70B |
|---|---|---|---|
Capabilities | 67 | +17 | |
Pricing | 0 | -1 | |
Context window size | 81 | 0 | |
Recency | 83 | +27 | |
Output Capacity | 20 | -50 | |
| Overall Result | 2 wins | of 5 | 3 wins |
30
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Mistral AI
DeepSeek
Mistral Small 3.2 24B saves you $92.50/month
That's $1110.00/year compared to R1 Distill Llama 70B at your current usage level of 100K calls/month.
| Metric | Mistral Small 3.2 24B | R1 Distill Llama 70B | Winner |
|---|---|---|---|
| Overall Score | 67 | 61 | Mistral Small 3.2 24B |
| Rank | #171 | #214 | Mistral Small 3.2 24B |
| Quality Rank | #171 | #214 | Mistral Small 3.2 24B |
| Adoption Rank | #171 | #214 | Mistral Small 3.2 24B |
| Parameters | 24B | 70B | -- |
| Context Window | 128K | 131K | R1 Distill Llama 70B |
| Pricing | $0.07/$0.20/M | $0.70/$0.80/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 50 | Mistral Small 3.2 24B |
| Pricing | 0 | 1 | R1 Distill Llama 70B |
| Context window size | 81 | 81 | R1 Distill Llama 70B |
| Recency | 83 | 56 | Mistral Small 3.2 24B |
| Output Capacity | 20 | 70 | R1 Distill Llama 70B |
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 67/100 (rank #171), placing it in the top 41% of all 290 models tracked.
Scores 61/100 (rank #214), placing it in the top 27% of all 290 models tracked.
Mistral Small 3.2 24B has a 6-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.
Mistral Small 3.2 24B offers 82% better value per quality point. At 1M tokens/day, you'd spend $4.13/month with Mistral Small 3.2 24B vs $22.50/month with R1 Distill Llama 70B - a $18.38 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 Small 3.2 24B 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 ($0.20/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (67/100) 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
Mistral Small 3.2 24B has a moderate advantage with a 6.299999999999997-point lead in composite score. It wins on more signal dimensions, but R1 Distill Llama 70B has specific strengths that could make it the better choice for certain workflows.
Best for Quality
Mistral Small 3.2 24B
Marginally better benchmark scores; both are excellent
Best for Cost
Mistral Small 3.2 24B
82% lower pricing; better value at scale
Best for Reliability
Mistral Small 3.2 24B
Higher uptime and faster response speeds
Best for Prototyping
Mistral Small 3.2 24B
Stronger community support and better developer experience
Best for Production
Mistral Small 3.2 24B
Wider enterprise adoption and proven at scale
by Mistral AI
| Capability | Mistral Small 3.2 24B | R1 Distill Llama 70B |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Mistral AI
DeepSeek
Mistral Small 3.2 24B saves you $1.84/month
That's 83% cheaper than R1 Distill Llama 70B 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 | Mistral Small 3.2 24B | R1 Distill Llama 70B |
|---|---|---|
| Context Window | 128K | 131K |
| Max Output Tokens | -- | 16,384 |
| Open Source | Yes | Yes |
| Created | Jun 20, 2025 | Jan 23, 2025 |
Mistral Small 3.2 24B scores 67/100 (rank #171) compared to R1 Distill Llama 70B's 61/100 (rank #214), giving it a 6-point advantage. Mistral Small 3.2 24B is the stronger overall choice, though R1 Distill Llama 70B may excel in specific areas like certain benchmarks.
Mistral Small 3.2 24B is ranked #171 and R1 Distill Llama 70B is ranked #214 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 Small 3.2 24B is cheaper at $0.20/M output tokens vs R1 Distill Llama 70B's $0.80/M output tokens - 4.0x more expensive. Input token pricing: Mistral Small 3.2 24B at $0.07/M vs R1 Distill Llama 70B at $0.70/M.
R1 Distill Llama 70B has a larger context window of 131,072 tokens compared to Mistral Small 3.2 24B's 128,000 tokens. A larger context window means the model can process longer documents and conversations.