Language Models & Text Processing
From frontier LLMs to specialized NER models. Which model for which task, at what cost, and when an LLM is overkill.
March 2026|8 min read
Quick picks
Best general LLM
Claude Opus 4 (reasoning) / GPT-5 (multimodal)
Best value LLM
Claude Sonnet 4 ($3/1M) / DeepSeek R1 ($0.55/1M)
Best open source
Llama 4 Maverick (MoE) / DeepSeek R1 (reasoning)
Best embeddings
KaLM-Gemma3-12B (open) / OpenAI text-embedding-3 (API)
Best for classification/NER
Fine-tuned DeBERTa v3 (speed) / LLM few-shot (flexibility)
The tradeoff
LLMs: flexible, slow, expensive. Specialized: fast, cheap, rigid.
Frontier LLM comparison
Ranked by reasoning benchmarks. Costs per million input tokens.
| Model | MMLU | HumanEval | Reasoning | Speed | Cost | Best for |
|---|---|---|---|---|---|---|
| Claude Opus 4Anthropic | 92.4 | 95.1 | Best | Medium | $15/1M in | Complex reasoning, analysis, coding |
| GPT-5OpenAI | 91.8 | 93.7 | Excellent | Fast | $5/1M in | General-purpose, multimodal |
| Claude Sonnet 4Anthropic | 90.1 | 93.8 | Excellent | Fast | $3/1M in | Best value frontier, coding |
| Gemini 2.5 ProGoogle | 90.3 | 91.2 | Excellent | Fast | $1.25/1M in | 1M+ context, multimodal |
| Llama 4 MaverickMeta (Open) | 89.2 | 90.5 | Very Good | Variable | Self-host | Open source, MoE, customization |
| DeepSeek R1DeepSeek (Open) | 90.8 | 92.1 | Excellent | Slow (CoT) | $0.55/1M in | Math, reasoning, open weights |
| Claude Haiku 4Anthropic | 84.5 | 88 | Good | Very Fast | $0.25/1M in | High volume, cost-efficient |
| GPT-4o-miniOpenAI | 82 | 87.2 | Good | Very Fast | $0.15/1M in | Cheapest frontier, high throughput |
Text processing tasks
Not everything needs an LLM. Here's what specialized models still win at.
Text Embeddings
Semantic search, RAG, clustering
MTEB
KaLM-Gemma3-12B (72.3%)
Translation
33+ languages, document-level
WMT
HY-MT1.5 (WMT2025 winner)
Question Answering
Extractive, abstractive, multi-hop
SQuAD, TriviaQA
GPT-5 / Claude 4
Named Entity Recognition
People, orgs, locations, custom
CoNLL-2003
Fine-tuned DeBERTa v3
Text Classification
Sentiment, intent, topic
GLUE, SuperGLUE
DeBERTa v3 (GLUE 91.3)
Summarization
News, documents, conversations
CNN/DailyMail
Claude 4 / GPT-5
When to use an LLM vs a specialized model
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 calls)
- Well-defined, stable task
- Training data available
Explore deeper
Verified benchmarks across every text task. Submit new SOTA results or suggest benchmarks.