Cost of LLM Sentiment Analysis and Classification
Classification returns only a label, so output is tiny and the whole bill is driven by input volume.
Typical monthly workload
- Input tokens
- 30,000,000
- Output tokens
- 300,000
- Requests / day
- 20,000
Illustrative estimate. Adjust the sliders below to match your own volume.
Recommended setup
A small model usually suffices for coarse sentiment. Sample results against a labeled set and upgrade only if accuracy falls short.
Monthly cost by model for this workload
| Model | Input / 1M | Output / 1M | Est. monthly cost |
|---|---|---|---|
| Gemini 1.5 Flash | $0.075 | $0.30 | $2.34 |
| DeepSeek V3 / V2.5 | $0.140 | $0.28 | $4.28 |
| GPT-4o Mini | $0.150 | $0.60 | $4.68 |
| Llama 3.3 70B (Groq/Together) | $0.590 | $0.79 | $17.94 |
| Claude 3.5 Haiku | $0.800 | $4.00 | $25.20 |
| o1-mini | $1.100 | $4.40 | $34.32 |
| Gemini 1.5 Pro | $1.250 | $5.00 | $39.00 |
| Mistral Large 2 | $2.000 | $6.00 | $61.80 |
| GPT-4o (Omni) | $2.500 | $10.00 | $78.00 |
| Claude 3.5 Sonnet | $3.000 | $15.00 | $94.50 |
| o1 (Reasoning) | $15.000 | $60.00 | $468.00 |
Computed from list prices and the workload above (30,000,000 input and 300,000 output tokens per month), with no caching or batch discounts. Real bills vary.
How to cut this cost
- Return a single token
Constrain output to a label from a fixed list to keep output near zero.
- Batch short texts
Classify many short items per request to amortize the shared instruction tokens.
Simulate your own spend
Quick Workload Presets
Pick a ready-made scenario or customize your token volume below.
Token & Usage Parameters
By switching or routing background tasks from GPT-4o to Gemini 1.5 Flash, your team saves up to $2,270 annually.
Lowest cost per token, high-frequency tasks, real-time audio/video
State-of-the-art open economics, high-performance general intelligence
High-volume text processing, fast chatbots, classification
Open-weights privacy, self-hosting or ultra-cheap hosted inferencing
Rapid code completion, customer service, structured data extraction
Fast STEM reasoning, competitive programming at lower cost
Massive context (2M tokens), full video/audio analysis, large codebases
Multilingual tasks, European compliance, enterprise code generation
Complex multimodal reasoning, coding, and production apps
Software engineering, nuanced writing, complex agentic workflows
Advanced mathematics, algorithmic coding, PhD-level scientific logic
Frequently asked questions
Is an LLM overkill for sentiment analysis?
Sometimes. A fine-tuned small classifier can be cheaper at very high volume, but an LLM needs no training data and handles nuance and new categories flexibly.