Best Cheap LLM in 2026
Models released in the last 12 months, ranked by blended price per million tokens (3:1 input-to-output mix).
Updated automatically as pricing changes. Full model database →
| # | Model | Input /1M |
|---|---|---|
| 1 | ScaleDown: SummarizeScaledown | $0.04 |
| 2 | Decision-1Microsoft | $0.042 |
| 3 | Nex-N2.5-miniNex Agi | $0.025 |
| 4 | Nex-N2-MiniNex Agi | $0.025 |
| 5 | Solar Pro 4Upstage | $0.03 |
| 6 | Qwen3.7 FlashAlibaba / Qwen | $0.03 |
| 7 | Schematron V2 TurboInference Net | $0.03 |
| 8 | Granite 4.1 8BIbm | $0.05 |
| 9 | Mercury 2.5 PreviewInception | $0.04 |
| 10 | Laguna XS 2.1Poolside | $0.06 |
| 11 | Hy-MT2-1.8BTencent | $0.044 |
| 12 | Solar Mini 4Upstage | $0.05 |
| 13 | Ling 3.0 Flash FinInclusionai | $0.06 |
| 14 | Ling 3.0 Flash VLInclusionai | $0.06 |
| 15 | Schematron V2 SmallInference Net | $0.05 |
| 16 | Qwen3.5-9BAlibaba / Qwen | $0.1 |
| 17 | Granite 4.2 8BIbm | $0.1 |
| 18 | Qwen3.5-FlashAlibaba / Qwen | $0.065 |
| 19 | Nex-N2.5-ProNex Agi | $0.075 |
| 20 | GLM-5.3-FlashZhipu AI | $0.075 |
| 21 | DeepSeek V4 FlashDeepSeek | $0.098 |
| 22 | Laguna S 2.1Poolside | $0.1 |
| 23 | Meta: Muse Spark 1.3 ContributorMeta AI | $0.1 |
| 24 | Hy-MT2-7BTencent | $0.074 |
| 25 | Hy-MT2-30B-A3BTencent | $0.074 |
| 26 | Nemotron 3.5 LightningNVIDIA | $0.1 |
| 27 | Step-3.5-FlashStepFun | $0.1 |
| 28 | Step-3.5-Flash-BaseStepFun | $0.1 |
| 29 | Seed-2.0-MiniByteDance | $0.1 |
| 30 | MiMo-V2.5Xiaomi | $0.14 |
Finding the best value LLM
Price alone doesn't tell the whole story. A model that costs twice as much but solves problems in half the calls is actually cheaper. When evaluating cost, consider:
- Input vs output pricing — for chat and RAG, input is usually 80%+ of your tokens. For generation-heavy tasks (writing, summarization), output price matters more.
- Context window — larger contexts let you process more in a single call, reducing round trips and total token usage.
- Open-weight models — if you can self-host, models like Mistral and LLaMA can cost near zero at scale. Check the “open weights” column on the model database.
- Quality vs cost — compare benchmark scores on the benchmark leaderboard to find the best performance per dollar.
Also see: Best Coding LLM, Best Reasoning LLM, Compare any two models.