Data story · cheapest

Cheapest LLM APIs in 2026: full ranking

All 600 models are ranked by blended 3:1 price, lowest first, using input and output prices from the index.

What the data shows

The spread runs from about $0.073 to $43.66 per million tokens on a blended 3:1 basis. The median across the set is $7.65.

The top of the list is dominated by small and open-weight models. That is expected: price and capability trade off, and the cheapest model is rarely the strongest on benchmarks.

How to read the ranking

Blended price weights input and output at 3:1. If your workload is output-heavy, the ranking can change, so check the output price column as well.

  • Filter by context window before comparing price.
  • Check whether the model is open weights or API-only.
  • Test the cheapest candidate on your own task before switching.
  • Keep a fallback model for quality-sensitive requests.

Blended 3:1 price, lowest first

#ModelProviderInput / 1MOutput / 1MContextAvg score
1SmolLM3 1.5BHugging Face$0.040$0.178,19236.8
2GPT-NeoX 1.5BEleutherAI$0.060$0.3232,76836.4
3Pythia 7BEleutherAI$0.10$0.368,19240.7
4Nemotron Nano 1.5BNVIDIA$0.10$0.488,19241.5
5Zephyr 1.5BHugging Face$0.10$0.5132,76838.0
6Pythia 1.5BEleutherAI$0.10$0.544,09635.8
7StableLM 2 1.5BStability AI$0.13$0.4616,38438.3
8Arctic 1.5BSnowflake$0.15$0.8232,76843.2
9Pythia 8BEleutherAI$0.16$0.7932,76841.4
10SmolLM3 8BHugging Face$0.21$0.7632,76844.0
11GPT-NeoX 7BEleutherAI$0.20$0.898,19242.4
12Zephyr 8BHugging Face$0.22$0.848,19243.0
13Molmo 1.5BAllen Institute for AI$0.21$0.938,19242.8
14SmolLM3 7BHugging Face$0.19$1.0316,38444.3
15Zephyr 7BHugging Face$0.23$0.9332,76842.5
16GPT-NeoX 8BEleutherAI$0.25$1.0416,38443.7
17Phi-3.5 1.5BMicrosoft$0.25$1.0532,76844.5
18Mixtral Turbo 1.5BTogether AI$0.31$1.194,09647.6
19DBRX 1.5BDatabricks$0.32$1.2216,38447.0
20OLMo 2 1.5BAllen Institute for AI$0.28$1.4116,38444.0
21Mixtral Groq 1.5BGroq$0.34$1.244,09646.1
22StableLM 2 7BStability AI$0.28$1.478,19244.3
23Nemotron Nano 7BNVIDIA$0.35$1.308,19247.5
24Mixtral 1.5BMistral AI$0.36$1.5016,38447.7
25Mistral Small 3 1.5BMistral AI$0.42$1.5832,76849.2
26StableLM 2 8BStability AI$0.38$1.7832,76847.0
27Nemotron Nano 8BNVIDIA$0.46$1.648,19248.1
28Gemma 3 1.5BGoogle DeepMind$0.40$1.8232,76848.9
29Llama 3 Hyperbolic 1.5BHyperbolic$0.45$1.7032,76848.4
30Llama 3 Modal 1.5BModal$0.42$1.804,09649.7
31Phi-4 1.5BMicrosoft$0.42$1.908,19248.1
32Llama 3.3 Groq 1.5BGroq$0.46$1.804,09650.0
33Llama 3 Baseten 1.5BBaseten$0.42$2.014,09648.2
34Llama 3 Lambda 1.5BLambda$0.46$1.9116,38448.4
35Llama 3 Novita 1.5BNovita AI$0.41$2.094,09649.1
36Llama 3 DeepInfra 1.5BDeepInfra$0.48$1.9132,76848.8
37Yi-Large 1.5B01.AI$0.42$2.138,19246.8
38Molmo 7BAllen Institute for AI$0.42$2.2232,76849.3
39Llama 3 Anyscale 1.5BAnyscale$0.43$2.2716,38448.9
40Codestral 1.5BMistral AI$0.52$2.038,19249.9
41Molmo 8BAllen Institute for AI$0.44$2.2932,76849.9
42Llama 3.1 Turbo 1.5BTogether AI$0.46$2.2416,38449.8
43Seed-OSS 1.5BByteDance$0.45$2.364,09649.8
44Llama 3.1 1.5BMeta AI$0.53$2.144,09650.7
45Llama 3 Replicate 1.5BReplicate$0.51$2.284,09649.3
46Arctic 7BSnowflake$0.49$2.388,19250.3
47Command R+ 1.5BCohere$0.51$2.3532,76850.0
48Llama 3.1 Cerebras 1.5BCerebras$0.48$2.464,09650.2
49Nemotron 4 1.5BNVIDIA$0.49$2.6732,76850.8
50Yi-Lightning 1.5B01.AI$0.54$2.524,09650.0

Methodology

This story uses the AI & LLM Index dataset: 600 models, 40 providers and 12 benchmarks. Prices are listed per million tokens. Scores are modelled estimates on a 0–100 scale and are not official benchmark results. Figures are rounded for readability.

Related

FAQ

What does the Cheapest LLM APIs in 2026: full ranking data show?

All 600 models are ranked by blended 3:1 price, lowest first, using input and output prices from the index.

Are the numbers official?

No. Prices and scores are compiled for this demo index. Benchmark values are modelled estimates, not official results. Verify figures with the provider or benchmark owner before making decisions.

How should I use this ranking?

Use it to form a shortlist. Check the specific model pages, compare price and context, and test the shortlist on your own examples before committing.