Best LLM for Cheap Bulk Workloads
Ranked primarily on input and output $/1M with a benchmark floor so you do not ship junk at volume.
Updated April 2026. Top 3 this month: Gemini 2.0 Flash-Lite, Mistral 7B, llama-3.2-1b-instruct.
How we rank
Some workloads are massive but forgiving — classification, tagging, summarization, PII scrubbing. The question is: what is the cheapest model that still clears the quality floor? We weight price dominantly here but set a benchmark floor so the recommendation is not useless.
Pillars and weights: input price (50%) · output price (30%) · MMLU (20%). Our full methodology is published on the methodology page.
Top ranked models
| Rank | Model | Provider | Input $/1M | Output $/1M | Context |
|---|---|---|---|---|---|
| 1 | Gemini 2.0 Flash-Lite | $0.07 | $0.30 | 1,000,000 | |
| 2 | Mistral 7B | Mistral | $0.20 | $0.20 | 32,768 |
| 3 | llama-3.2-1b-instruct | Meta | $0.20 | $0.20 | 60,000 |
| 4 | qwen2-1.5b-instruct | Alibaba (Qwen) | $0.20 | $0.20 | — |
| 5 | deepseek-chat | DeepSeek | $0.14 | $0.28 | 164,000 |
| 6 | GPT-5 nano | OpenAI | $0.05 | $0.40 | 400,000 |
| 7 | Gemini 2.0 Flash | $0.10 | $0.40 | 1,000,000 | |
| 8 | GPT-4.1 nano | OpenAI | $0.10 | $0.40 | 1,000,000 |
| 9 | mistral-nemo | Mistral | $0.20 | $0.40 | 131,000 |
| 10 | llama-3.1-8b-instruct | Meta | $0.20 | $0.50 | 16,000 |
Tips for cheap bulk workloads
- Use batch pricing aggressively. 50%+ discounts are common.
- Use cached-input pricing for repeating preambles.
- A cheaper model with a short retry loop often beats a more expensive model one-shot.
Frequently asked questions
What is the cheapest usable LLM right now?
As of April 2026, our weighted top 3 cheapest-but-capable are Gemini 2.0 Flash-Lite, Mistral 7B, llama-3.2-1b-instruct.
Does batch really cut cost in half?
Often, yes. Providers offer 30–50% discounts on async batch endpoints in exchange for up-to-24h latency.
When is a small model false economy?
When its lower accuracy causes retries, downstream fixup, or human review. Always measure end-to-end dollars-per-correct-output, not dollars-per-token.
Related tasks
Want to model your own workload? Use the volume and switch-cost calculators on the main tool page. Sign in with Google to unlock compare-my-prompt with real tokenizer counts.
Data refreshed daily via our snapshot cron. See our public JSON API for programmatic access.