modelbenchmark.io

Price one workload

LLM cost calculator

One workload, every listed price. Cheapest first. Full price table.

… models with a listed input and output price

RankModelCostIn $/MOut $/MCache rd
1MiMo-V2.5 (Free)$0freefreefree
2GPT-5 Nano$0.09$0.05$0.40$0.005
3GLM-4.7-Flash (Deep Infra)$0.1$0.06$0.40$0.01
4Qwen3.5 35B-A3B$0.103$0.057$0.459$0.057
5Qwen3.5 9B$0.103$0.09$0.13$0.045
6GPT OSS 20B (Groq)$0.105$0.075$0.30$0.0375
7MiniMax-M3.1-Flash$0.105$0.075$0.30$0.0075
8Qwen3.6 35B A3B$0.112$0.07$0.42$0.035
9Gemma 3 27B IT (Nebius)$0.13$0.10$0.30$0.10
10Gemini 2.5 Flash-Lite$0.14$0.10$0.40$0.01
11GPT-4.1 nano (Azure)$0.14$0.10$0.40$0.025
12Claude Haiku 5.5$0.15$0.10$0.50$0.01
13GPT-6 Luna$0.15$0.10$0.50$0.01
14Qwen3.5 27B$0.155$0.086$0.688$0.086
15DeepSeek V4 Flash 0731 (Nebius)$0.168$0.14$0.28$0.14
16MiMo V2.6 Flash$0.168$0.14$0.28$0.14
17Hy3 (Deep Infra)$0.183$0.13$0.53$0.033
18Qwen3.8 Flash$0.197$0.15$0.47$0.016
19GLM-5.3-Flash$0.2$0.15$0.50$0.03
20Qwen3-Next 80B-A3B Instruct (Deep Infra)$0.2$0.09$1.10$0.015
21Gemma 4 26B A4B IT$0.201$0.144$0.575$0.05
22Qwen3.5 122B-A10B$0.207$0.115$0.917$0.115
23GLM-4.5-Air$0.209$0.1195$0.8964$0.0239
24Qwen/Qwen3-32B$0.209$0.1494$0.5978$0.05
25DeepSeek V4 Flash Vision Exp$0.21$0.15$0.60$0.003
26GPT-4o mini$0.21$0.15$0.60$0.075
27Mistral Small (latest)$0.21$0.15$0.60$0.015
28Qwen3-30B-A3B$0.218$0.11$1.08$0.05
29GPT OSS 120B (IONOS)$0.24$0.1676$0.7262$0.15
30Qwen3 Coder Next (IONOS)$0.257$0.1676$0.8938$0.07
31Qwen3 235B-A22B Instruct 2507 (Nebius)$0.26$0.20$0.60$0.20
32Qwen3.5 397B-A17B$0.275$0.172$1.032$0.172
33Qwen3 VL 235B A22B Instruct (Deep Infra)$0.288$0.20$0.88$0.11
34Qwen3-Coder 30B-A3B Instruct (Scaleway)$0.314$0.2245$0.898$0.014
35Step 3.7 Flash (Deep Infra)$0.315$0.20$1.15$0.04
36GPT-5.6 Luna$0.32$0.20$1.20$0.02
37GPT-5.4 nano$0.325$0.20$1.25$0.02
38DeepSeek V3 0324$0.385$0.275$1.10$0.0299
39GLM-4.5V$0.388$0.2988$0.8964$0.0598
40Codestral 25.08$0.39$0.30$0.90$0.03
41GLM-4.6V$0.39$0.30$0.90$0.05
42Gemini 3.1 Flash Lite (Vertex AI)$0.4$0.25$1.50$0.025
43Qwen3.6 Flash$0.4$0.25$1.50$0.25
44DeepSeek-V3 (Deep Infra)$0.409$0.32$0.89$0.135
45DeepSeek V4.1 Flash (Nebius)$0.42$0.30$1.20$0.30
46MiniMax M2.7 Highspeed$0.42$0.30$1.20$0.03
47MiniMax-M2$0.42$0.30$1.20$0.03
48MiniMax-M2.1$0.42$0.30$1.20$0.03
49MiniMax-M2.5$0.42$0.30$1.20$0.03
50MiniMax-M2.7$0.42$0.30$1.20$0.06
51MiniMax-M3$0.42$0.30$1.20$0.06
52Muse Glimmer 30B (Deep Infra)$0.42$0.30$1.20$0.04
53GPT-5 Mini$0.45$0.25$2.00$0.025
54GPT-5.1 Codex mini (Azure)$0.45$0.25$2.00$0.025
55Gemma 4 31B IT$0.495$0.38$1.15$0.05
56MiMo V2.6 Pro$0.522$0.435$0.87$0.435
57Gemini 2.5 Flash$0.55$0.30$2.50$0.03
58Gemini 3.5 Flash Lite (Vertex AI)$0.55$0.30$2.50$0.03
59GPT-4.1 mini$0.56$0.40$1.60$0.10
60Qwen3.7 Plus$0.56$0.40$1.60$0.40
61Qwen3-235B-A22B$0.576$0.29$2.86$0.018
62Qwen3.5 Plus$0.581$0.36$2.21$0.36
63MiMo-V2.5-Pro$0.623$0.52$1.0286free
64Mistral Large 3$0.65$0.50$1.50$0.05
65Qwen3.6 27B$0.66$0.4126$2.4754$0.4126
66Kimi K2.5 (TensorX)$0.78$0.50$2.80$0.125
67Llama-3.3-70B-Instruct (IONOS)$0.799$0.7262$0.7262$0.20
68Gemini 3 Flash Preview (Vertex AI)$0.8$0.50$3.00$0.05
69Qwen3.6 Plus$0.8$0.50$3.00$0.50
70Qwen3.8 27B$0.8$0.50$3.00$0.10
71GLM-4.5$0.82$0.60$2.20$0.11
72GLM-4.6$0.82$0.60$2.20$0.11
73GLM-4.7$0.82$0.60$2.20$0.11
74Kimi K2 Thinking (Amazon Bedrock)$0.85$0.60$2.50$0.30
75DeepSeek R1 0528 (TensorX)$0.92$0.66$2.60$0.165
76Hy4 preview (Deep Infra)$1.084$0.834$2.501$0.042
77Gemini 3.6 Flash (Vertex AI)$1.125$0.75$3.75$0.075
78GPT-5.4 mini$1.2$0.75$4.50$0.075
79Grok Build 0.1$1.2$1.00$2.00$0.20
80Step 5 Preview (Deep Infra)$1.27$1.00$2.70$0.10

Common questions

How do I estimate the cost of an LLM workload?
Set input tokens, output tokens and optional cache-read tokens. The table multiplies those by each model's listed rates. Cache-read uses the cache price when a host publishes one. It uses the input price if not.
What token mix should I use?
The default is 1 million input tokens and 100 thousand output tokens. That matches a long document plus a short answer. Change the fields. The URL keeps the mix so you can share it.