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 →

#ModelInput /1M
1ScaleDown: SummarizeScaledown$0.04
2Decision-1Microsoft$0.042
3Nex-N2.5-miniNex Agi$0.025
4Nex-N2-MiniNex Agi$0.025
5Solar Pro 4Upstage$0.03
6Qwen3.7 FlashAlibaba / Qwen$0.03
7Schematron V2 TurboInference Net$0.03
8Granite 4.1 8BIbm$0.05
9Mercury 2.5 PreviewInception$0.04
10Laguna XS 2.1Poolside$0.06
11Hy-MT2-1.8BTencent$0.044
12Solar Mini 4Upstage$0.05
13Ling 3.0 Flash FinInclusionai$0.06
14Ling 3.0 Flash VLInclusionai$0.06
15Schematron V2 SmallInference Net$0.05
16Qwen3.5-9BAlibaba / Qwen$0.1
17Granite 4.2 8BIbm$0.1
18Qwen3.5-FlashAlibaba / Qwen$0.065
19Nex-N2.5-ProNex Agi$0.075
20GLM-5.3-FlashZhipu AI$0.075
21DeepSeek V4 FlashDeepSeek$0.098
22Laguna S 2.1Poolside$0.1
23Meta: Muse Spark 1.3 ContributorMeta AI$0.1
24Hy-MT2-7BTencent$0.074
25Hy-MT2-30B-A3BTencent$0.074
26Nemotron 3.5 LightningNVIDIA$0.1
27Step-3.5-FlashStepFun$0.1
28Step-3.5-Flash-BaseStepFun$0.1
29Seed-2.0-MiniByteDance$0.1
30MiMo-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.