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: Xiaomi: MiMo-V2-Flash, Tencent: Hunyuan A13B Instruct, Microsoft: Phi 4.
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 | Xiaomi: MiMo-V2-Flash | Xiaomi | $0.09 | $0.29 | 262,144 |
| 2 | Tencent: Hunyuan A13B Instruct | Tencent | $0.14 | $0.57 | 131,072 |
| 3 | Microsoft: Phi 4 | Microsoft | $0.07 | $0.14 | 16,384 |
| 4 | Meta: Llama 3.3 70B Instruct | Meta | $0.12 | $0.38 | 131,072 |
| 5 | Qwen2.5 72B Instruct | Qwen | $0.12 | $0.39 | 32,768 |
| 6 | Google: Gemma 4 31B | $0.13 | $0.38 | 262,144 | |
| 7 | AllenAI: Olmo 3 32B Think | Allen AI | $0.15 | $0.50 | 65,536 |
| 8 | Qwen: Qwen3 32B | Qwen | $0.08 | $0.24 | 40,960 |
| 9 | Meta: Llama 3.1 70B Instruct | Meta | $0.40 | $0.40 | 131,072 |
| 10 | Qwen: Qwen3.5-9B | Qwen | $0.10 | $0.15 | 262,144 |
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 Xiaomi: MiMo-V2-Flash, Tencent: Hunyuan A13B Instruct, Microsoft: Phi 4.
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.