| Signal | DeepSeek V3.2 Exp | Delta | Llama 3.1 70B Instruct |
|---|---|---|---|
Capabilities | 67 | +17 | |
Benchmarks | 70 | -2 | |
Pricing | 100 | -- | |
Context window size | 83 | +2 | |
Recency | 92 | +79 | |
Output Capacity | 80 | +10 | |
| Overall Result | 4 wins | of 6 | 1 wins |
Score History
70.2
current score
DeepSeek V3.2 Exp
right now
65.3
current score
DeepSeek
Meta
DeepSeek V3.2 Exp saves you $12.50/month
That's $150.00/year compared to Llama 3.1 70B Instruct at your current usage level of 100K calls/month.
| Metric | DeepSeek V3.2 Exp | Llama 3.1 70B Instruct | Winner |
|---|---|---|---|
| Overall Score | 70 | 65 | DeepSeek V3.2 Exp |
| Rank | #99 | #128 | DeepSeek V3.2 Exp |
| Quality Rank | #99 | #128 | DeepSeek V3.2 Exp |
| Adoption Rank | #99 | #128 | DeepSeek V3.2 Exp |
| Parameters | -- | 70B | -- |
| Context Window | 164K | 131K | DeepSeek V3.2 Exp |
| Pricing | $0.27/$0.41/M | $0.40/$0.40/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 50 | DeepSeek V3.2 Exp |
| Benchmarks | 70 | 72 | Llama 3.1 70B Instruct |
| Pricing | 100 | 100 | DeepSeek V3.2 Exp |
| Context window size | 83 | 81 | DeepSeek V3.2 Exp |
| Recency | 92 | 13 | DeepSeek V3.2 Exp |
| Output Capacity | 80 | 70 | DeepSeek V3.2 Exp |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.
Scores 70/100 (rank #99), placing it in the top 66% of all 290 models tracked.
Scores 65/100 (rank #128), placing it in the top 56% of all 290 models tracked.
With only a 5-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.
DeepSeek V3.2 Exp offers 15% better value per quality point. At 1M tokens/day, you'd spend $10.20/month with DeepSeek V3.2 Exp vs $12.00/month with Llama 3.1 70B Instruct - a $1.80 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
Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Suitable for user-facing chat with competitive response times. Llama 3.1 70B Instruct also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (164K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.40/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (70/100) correlates with better nuance, coherence, and style in long-form content
DeepSeek V3.2 Exp has a moderate advantage with a 4.900000000000006-point lead in composite score. It wins on more signal dimensions, but Llama 3.1 70B Instruct has specific strengths that could make it the better choice for certain workflows.
Best for Quality
DeepSeek V3.2 Exp
Marginally better benchmark scores; both are excellent
Best for Cost
DeepSeek V3.2 Exp
15% lower pricing; better value at scale
Best for Reliability
DeepSeek V3.2 Exp
Higher uptime and faster response speeds
Best for Prototyping
DeepSeek V3.2 Exp
Stronger community support and better developer experience
Best for Production
DeepSeek V3.2 Exp
Wider enterprise adoption and proven at scale
by DeepSeek
| Capability | DeepSeek V3.2 Exp | Llama 3.1 70B Instruct |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
DeepSeek
Meta
DeepSeek V3.2 Exp saves you $0.2220/month
That's 18% cheaper than Llama 3.1 70B Instruct 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 | DeepSeek V3.2 Exp | Llama 3.1 70B Instruct |
|---|---|---|
| Context Window | 164K | 131K |
| Max Output Tokens | 65,536 | 16,384 |
| Open Source | Yes | Yes |
| Created | Sep 29, 2025 | Jul 23, 2024 |