Quick Answer: NLP in 2025
LLMs dominate, but specialized models still win on speed and cost.
- Best general LLM:
- GPT-4o (balanced) / Claude 3.5 Opus (reasoning) / o1 (math)
- Best open-source LLM:
- Llama 3.1 405B (best) / Qwen 2.5 72B (efficient)
- Best embeddings:
- Voyage AI voyage-3 (API) / BGE-M3 (open source)
- Best for classification/NER:
- Fine-tuned DeBERTa (speed) / LLM few-shot (flexibility)
- Best for translation:
- GPT-4o (quality) / NLLB-200 (200 languages, free)
- The tradeoff:
- LLMs: flexible, slow, expensive. Specialized: fast, cheap, rigid.
NLP Benchmarks 2025
From academic benchmarks to production pipelines. Every NLP task maps to a building block with code you can deploy today.
Updated December 2025|15 min read
LLM Comparison
Current frontier models ranked by reasoning benchmarks. Costs are per million input tokens.
| Model | MMLU | HumanEval | Reasoning | Speed | Cost | Best For |
|---|---|---|---|---|---|---|
| GPT-4oOpenAI | 88.7% | 90.2% | Excellent | Fast | $2.50/1M in | General-purpose, multimodal |
| Claude 3.5 SonnetAnthropic | 88.3% | 92% | Excellent | Fast | $3/1M in | Coding, long context, safety |
| Claude 3.5 OpusAnthropic | 89.1% | 89.5% | Best | Medium | $15/1M in | Complex reasoning, analysis |
| Gemini 1.5 ProGoogle | 85.9% | 84.1% | Very Good | Fast | $1.25/1M in | 1M+ context, multimodal |
| Llama 3.1 405BMeta (Open) | 88.6% | 89% | Excellent | Variable | Self-host | Open source, customization |
| Qwen 2.5 72BAlibaba (Open) | 86.1% | 86.4% | Very Good | Variable | Self-host | Multilingual, open source |
| GPT-4o-miniOpenAI | 82% | 87.2% | Good | Very Fast | $0.15/1M in | Cost-efficient, high volume |
| Claude 3.5 HaikuAnthropic | 80.5% | 84% | Good | Very Fast | $0.25/1M in | Fast responses, cost-efficient |
Best Overall
GPT-4o / Claude 3.5 Sonnet
Best balance of quality, speed, and cost
Best Value
GPT-4o-mini / Claude Haiku
90% of quality at 10% of cost
Best Open Source
Llama 3.1 405B
GPT-4 class, fully open weights
LLM vs Specialized Model: Decision Guide
Use LLMs when:
- +Few examples available (few-shot learning)
- +Complex, nuanced task definitions
- +Need to explain reasoning
- +Task evolves frequently
- +Low volume (<10K requests/day)
Use Specialized Models when:
- +High volume (>100K requests/day)
- +Latency critical (<100ms)
- +Cost sensitive (pennies per 1K)
- +Well-defined, stable task
- +Training data available
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