Llama Models

53 modelsGeneral models from $0.2/M inputUp to 1.05M context

Usage

Last 30 days · 2026-08-26 to 2026-09-24

Tokens

424M

Requests

36.3K

Models in use

28 of 53

Tokens per day, stacked by model

080.4M161M08-2609-0209-0909-1609-232026-08-26 — 33,960 tokens llama-4-maverick: 32,540 llama-4-scout: 1,4202026-08-27 — 67,570 tokens llama-4-scout: 43,605 llama-3.3-70b: 23,9652026-08-28 — 19,550 tokens llama-4-scout: 18,890 llama-4-maverick: 470 llama-3.1-70b: 1902026-08-29 — 2,161,830 tokens llama-4-scout: 2,140,210 llama-3.3-70b: 21,235 2 more models: 3852026-08-30 — 35,247,495 tokens llama-4-scout: 35,159,930 deepinfra-llama-4-maverick-17b-128e-instruct: 44,715 deepinfra-llama-4-scout-17b-16e-instruct: 42,605 llama-4-maverick: 2452026-08-31 — 113,945,485 tokens llama-3.3-70b: 74,103,345 llama-4-scout: 39,719,140 llama-4-maverick: 122,010 2 more models: 310 deepinfra-llama-3.3-70b-instant-turbo: 225 llama-3.1-70b: 190 deepinfra-llama-4-scout-17b-16e-instruct: 135 deepinfra-llama-4-maverick-17b-128e-instruct: 1302026-09-01 — 135,135 tokens llama-4-maverick: 110,085 llama-4-scout: 22,980 llama-3.1-70b: 1,040 2 more models: 395 llama-3.3-70b: 190 deepinfra-llama-3.3-70b-instant-turbo: 190 deepinfra-llama-4-scout-17b-16e-instruct: 135 deepinfra-llama-4-maverick-17b-128e-instruct: 1202026-09-02 — 168,575 tokens llama-4-scout: 168,405 llama-4-maverick: 1702026-09-03 — 5,571,285 tokens llama-3.3-70b: 5,557,400 llama-4-scout: 13,585 llama-4-maverick: 3002026-09-04 — 13,639,210 tokens llama-4-scout: 13,639,020 llama-4-maverick: 1902026-09-05 — 345 tokens llama-4-scout: 220 llama-3.3-70b-instruct: 65 llama-4-maverick: 602026-09-06 — 19,971,790 tokens llama-4-scout: 19,776,165 llama-3.3-70b-instruct: 193,020 llama-4-maverick: 1,215 2 more models: 390 deepinfra-llama-3.3-70b-instant-turbo: 290 deepinfra-llama-4-scout-17b-16e-instruct: 240 llama-3.3-70b: 195 llama-3.1-70b: 145 deepinfra-llama-4-maverick-17b-128e-instruct: 1302026-09-07 — 132,025 tokens llama-4-maverick: 93,765 llama-4-scout: 33,200 llama-3.3-70b: 4,915 llama-3.3-70b-instruct: 1452026-09-08 — 1,713,535 tokens llama-3.3-70b-instruct: 1,401,415 llama-4-scout: 171,625 llama-4-maverick: 140,190 llama-3.3-70b: 160 deepinfra-llama-4-maverick-17b-128e-instruct: 1452026-09-09 — 458,030 tokens llama-4-maverick: 344,320 llama-4-scout: 95,820 llama-3.1-70b: 17,670 deepinfra-llama-4-maverick-17b-128e-instruct: 145 llama-3.3-70b-instruct: 752026-09-10 — 389,030 tokens llama-4-maverick: 386,350 2 more models: 500 llama-3.3-70b-instruct: 435 llama-4-scout: 355 deepinfra-llama-3.3-70b-instant-turbo: 355 deepinfra-llama-4-maverick-17b-128e-instruct: 265 llama-3.1-70b: 260 llama-3.3-70b: 255 deepinfra-llama-4-scout-17b-16e-instruct: 2552026-09-11 — 633,060 tokens llama-4-maverick: 620,860 llama-4-scout: 12,2002026-09-12 — 306,575 tokens llama-4-scout: 306,150 llama-4-maverick: 285 llama-3.3-70b-instruct: 1402026-09-13 — 13,113,900 tokens llama-3.3-70b: 6,813,240 deepinfra-llama-3.3-70b-instant-turbo: 5,866,120 llama-3.3-70b-instruct: 434,125 llama-4-scout: 180 llama-4-maverick: 165 deepinfra-llama-4-scout-17b-16e-instruct: 702026-09-14 — 14,668,985 tokens llama-4-scout: 6,570,835 llama-4-maverick: 5,299,845 llama-3.3-70b: 1,744,025 deepinfra-llama-3.3-70b-instant-turbo: 1,054,2802026-09-15 — 0 tokens2026-09-16 — 150,335 tokens llama-4-scout: 82,930 llama-4-maverick: 66,710 llama-3.3-70b-instruct: 6952026-09-17 — 160,895,190 tokens llama-3.3-70b: 146,980,255 llama-4-scout: 13,907,380 llama-4-maverick: 7,5552026-09-18 — 16,050 tokens llama-4-maverick: 8,050 llama-4-scout: 8,0002026-09-19 — 0 tokens2026-09-20 — 25,514,860 tokens llama-4-scout: 25,514,015 llama-4-maverick: 770 deepinfra-llama-4-scout-17b-16e-instruct: 752026-09-21 — 330,240 tokens llama-3.3-70b: 283,360 llama-4-scout: 46,8802026-09-22 — 2,076,995 tokens llama-3.3-70b: 1,926,550 llama-4-scout: 142,680 llama-4-maverick: 7,7652026-09-23 — 6,148,140 tokens llama-3.3-70b: 5,156,490 llama-4-scout: 742,780 llama-4-maverick: 248,590 deepinfra-llama-4-maverick-17b-128e-instruct: 145 deepinfra-llama-4-scout-17b-16e-instruct: 1352026-09-24 — 6,791,085 tokens llama-4-scout: 5,483,535 llama-3.3-70b-instruct: 1,307,265 llama-4-maverick: 285
  • llama-3.3-70b
  • llama-4-scout
  • llama-4-maverick
  • deepinfra-llama-3.3-70b-instant-turbo
  • llama-3.3-70b-instruct
  • deepinfra-llama-4-maverick-17b-128e-instruct
  • deepinfra-llama-4-scout-17b-16e-instruct
  • llama-3.1-70b
  • 2 more models

Which models that traffic went to

  1. Llama 3.3 70B57.2%243M
  2. Llama 4 Scout38.6%164M
  3. Llama 4 Maverick1.8%7.5M
  4. Deepinfra Llama 3.3 70B Instant Turbo1.6%6.9M
  5. Llama 3.3 70B Instruct0.8%3.3M
  6. Deepinfra Llama 4 Maverick 17B 128e Instruct<0.1%45.8K
  7. Deepinfra Llama 4 Scout 17B 16e Instruct<0.1%43.6K
  8. Llama 3.1 70B<0.1%19.5K
  9. 2 more models<0.1%2K

Share of 424M tokens. 18 models with traffic report no token counts and cannot be ranked here, including groq-llama-3.3-70b-versatile and aihubmix-Llama-3-1-8B-Instruct — they are in the request view.

The two views disagree on purpose: a model can take a large share of the calls and a small share of the tokens — many short requests — or the reverse. Which one matters depends on whether your cost is driven by call volume or by prompt length. Measured on AIHubMix over the last 30 days, counting the 53 model IDs listed on this page; traffic routed through upstream-specific IDs that are not in the public catalog is not included.

All 53 Llama Models

Open in model list
Llama models on AIHubMix with input and output modalities, context length, maximum output, price per million tokens including cache read and cache write rates, and measured throughput and latency.
Modalities
llama-4-maverickTakes text, vision, returns text.1.05M32K$0.2$0.2/M98 tok/s0.23 s
llama-4-scoutTakes text, vision, returns text.131K131K$0.2$0.2/M148 tok/s0.89 s
llama-3.3-70b-instructTakes text, returns text.131K—$0.6$1.2/M17 tok/s0.41 s
llama-3.3-70b66K8K$0.6$0.6/M3250 tok/s0.25 s
llama3-groq-8b-8192-tool-use-preview——$0.00019$0.00019/M——
llama3-groq-70b-8192-tool-use-preview——$0.00089$0.00089/M——
meta-llama/llama-3.1-405b-instruct:free——$0.02$0.02/M——
meta-llama/llama-3.1-70b-instruct:free——$0.02$0.02/M——
meta-llama/llama-3.1-8b-instruct:free——$0.02$0.02/M——
meta-llama/llama-3.2-11b-vision-instruct:free——$0.02$0.02/M——
meta-llama/llama-3.2-3b-instruct:free——$0.02$0.02/M——
deepinfra-llama-3.1-8b-instant——$0.033$0.055/M——
groq-llama-3.1-8b-instant——$0.055$0.088/M——
llama3-8b-8192——$0.06$0.12/M——
deepinfra-llama-4-scout-17b-16e-instruct——$0.088$0.33/M18 tok/s1.11 s
llama2-7b-2048——$0.1$0.1/M——
deepinfra-llama-3.3-70b-instant-turbo——$0.11$0.352/M13 tok/s3.57 s
groq-llama-4-scout-17b-16e-instruct——$0.122$0.366/M——
deepseek-r1-distill-qianfan-llama-8b——$0.137$0.548/M——
llama-3.2-11b-vision-preview——$0.2$0.2/M——
llama-3.2-1b-preview——$0.2$0.2/M——
llama-3.2-3b-preview——$0.2$0.2/M——
groq-llama-4-maverick-17b-128e-instruct——$0.22$0.66/M——
qianfan-llama-vl-8b——$0.274$0.685/M——
aihubmix-Llama-3-1-8B-Instruct——$0.3$0.6/M——
llama-3.1-8b-instant——$0.3$0.6/M——
llama3-8b-8192(33)——$0.3$0.3/M——
llama3.1-8b——$0.3$0.6/M——
meta/llama3-8B-chat——$0.3$0.3/M——
deepinfra-llama-4-maverick-17b-128e-instruct——$0.33$1.32/M11 tok/s0.27 s
aihubmix-Llama-3-2-11B-Vision——$0.4$0.4/M——
Gryphe/MythoMax-L2-13b——$0.4$0.4/M——
llama-3.1-70b——$0.44$0.44/M——
llama2-70b-4096Takes , returns text.——$0.5$0.5/M——
llama2-70b-40960Takes , returns text.——$0.5$0.5/M——
meta-llama/Llama-3.2-90B-Vision-Instruct——$0.5$0.5/M——
meta-llama-3-8b——$0.548$0.548/M——
aihubmix-Llama-3-1-70B-Instruct——$0.6$0.78/M——
cerebras-llama-3.3-70b——$0.6$0.6/M——
llama-3.1-70b-versatile——$0.6$0.6/M——
groq-llama-3.3-70b-versatile——$0.649$0.869/M——
aihubmix-Llama-3-70B-Instruct——$0.7$0.7/M——
llama3-70b-8192——$0.7$0.9373/M——
qianfan-chinese-llama-2-13b——$0.822$0.822/M——
WizardLM/WizardCoder-Python-34B-V1.0——$0.9$0.9/M——
aihubmix-Llama-3-2-90B-Vision——$2.4$2.4/M——
llama-3.2-90b-vision-preview——$2.4$2.4/M——
llama3-70b-8192(33)——$2.65$2.65/M——
llama-3.1-405b-instruct——$4$4/M——
llama-3.1-405b-reasoning——$4$4/M——
meta-llama-3-70b——$4.795$4.795/M——
aihubmix-Llama-3-1-405B-Instruct——$5$15/M——
meta/llama-3.1-405b-instruct——$5$5/M——

Prices are USD per million tokens; cache read and cache write are the rates for prompt-cache hits and for writing a prompt into the cache. Throughput and latency are measured on AIHubMix — the same figures the model detail page shows — not vendor claims. A dash means the catalog does not publish that field for that model, which is not the same as the model not supporting it.

Common Comparisons

llama-3.3-70b-instruct vs llama-4-maverickllama-3.3-70b-instruct vs llama-4-scoutllama-3.3-70b-instruct vs llama2-70b-4096llama-3.3-70b-instruct vs qwen3.8-max-previewkimi-k3 vs llama-3.3-70b-instruct

Llama on AIHubMix

Which Llama model should I start with?

llama-4-maverick at $0.2/M input — the cheapest entry here that declares tool calling, and it carries a 1.05M context. Move up to aihubmix-Llama-3-1-405B-Instruct when answer quality matters more than cost.

Why are there several entries for the same model?

Because each row is a route you can call, not a model release. Some IDs name an upstream (azure-, alicloud-, cc-), some are the open-weight repository form (meta-llama/…), and some differ only in capitalisation, kept so older integrations keep working.

The catalog does not carry a field saying which of those a given row is, so this page does not sort them into buckets it would have to invent. Every row shows that route’s own price, context and speed — compare those directly, and open a model to see the upstreams that serve it.

Do I need a separate Llama account?

No. One AIHubMix key covers every model on this page, and switching between them is a change to the model string — billing, rate limits, and logs stay in one place.

Other Model Authors

See all model authors →
Qwen
148 modelsOpenAI
141 modelsGoogle
87 modelsZ.AI
71 modelsByteDance
47 models

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