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Meta Models

3 modelsGeneral models from $1.375/M inputUp to 1.05M context

Usage

Last 29 days · 2026-08-10 to 2026-09-07

Tokens

2.2B

Requests

17.8K

Models in use

3 of 3

Tokens per day, stacked by model

0423M846M08-1008-1708-2408-3109-072026-08-10 — 7,403,175 tokens muse-spark-1.2: 4,707,045 muse-spark-1.1: 2,696,1302026-08-11 — 6,849,170 tokens muse-spark-1.2: 6,416,825 muse-spark-1.1: 432,3452026-08-12 — 1,839,035 tokens muse-spark-1.1: 1,444,405 muse-spark-1.2: 394,6302026-08-13 — 10,303,790 tokens muse-spark-1.2: 10,303,7902026-08-14 — 14,358,095 tokens muse-spark-1.2: 14,305,805 muse-spark-1.1: 52,2902026-08-15 — 4,433,050 tokens muse-spark-1.2: 4,427,780 muse-spark-1.1: 5,2702026-08-16 — 58,483,615 tokens muse-spark-1.2: 58,483,6152026-08-17 — 14,373,540 tokens muse-spark-1.2: 14,373,230 muse-spark-1.1: 3102026-08-18 — 45,814,345 tokens muse-spark-1.2: 45,797,170 muse-spark-1.1: 17,1752026-08-19 — 10,158,850 tokens muse-spark-1.2: 10,158,300 muse-spark-1.1: 5502026-08-20 — 22,450,370 tokens muse-spark-1.2: 22,445,830 muse-spark-1.1: 4,5402026-08-21 — 10,496,610 tokens muse-spark-1.2: 10,496,6102026-08-22 — 3,290,405 tokens muse-spark-1.2: 3,285,630 muse-spark-1.1: 4,7752026-08-23 — 5,393,590 tokens muse-spark-1.2: 5,298,190 muse-spark-1.1: 95,4002026-08-24 — 846,446,380 tokens muse-spark-1.1: 843,889,815 muse-spark-1.2: 2,556,5652026-08-25 — 3,688,100 tokens muse-spark-1.2: 3,687,145 muse-spark-1.1: 9552026-08-26 — 3,793,265 tokens muse-spark-1.2: 3,792,405 muse-spark-1.1: 8602026-08-27 — 139,344,680 tokens muse-spark-1.1: 134,368,115 muse-spark-1.2: 4,976,5652026-08-28 — 2,007,600 tokens muse-spark-1.2: 2,006,570 muse-spark-1.1: 1,0302026-08-29 — 8,362,755 tokens muse-spark-1.2: 8,362,7552026-08-30 — 2,225,805 tokens muse-spark-1.2: 2,225,375 muse-spark-1.1: 4302026-08-31 — 824,660 tokens muse-spark-1.2: 822,650 muse-spark-1.1: 2,0102026-09-01 — 2,267,250 tokens muse-spark-1.2: 2,265,035 muse-spark-1.1: 2,2152026-09-02 — 18,048,650 tokens muse-spark-1.2: 18,009,990 muse-spark-1.1: 38,6602026-09-03 — 684,062,080 tokens muse-spark-1.3: 682,894,540 muse-spark-1.2: 1,167,5402026-09-04 — 181,872,030 tokens muse-spark-1.3: 181,758,820 muse-spark-1.2: 113,2102026-09-05 — 34,848,820 tokens muse-spark-1.3: 34,848,8202026-09-06 — 7,291,715 tokens muse-spark-1.3: 7,291,585 muse-spark-1.1: 65 muse-spark-1.2: 652026-09-07 — 21,119,655 tokens muse-spark-1.3: 21,116,860 muse-spark-1.1: 1,665 muse-spark-1.2: 1,130
  • muse-spark-1.1
  • muse-spark-1.3
  • muse-spark-1.2

Which models that traffic went to

  1. Muse Spark 1.145.3%983M
  2. Muse Spark 1.342.7%928M
  3. Muse Spark 1.212.0%261M

Share of 2.2B tokens.

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 29 days, counting the 3 model IDs listed on this page; traffic routed through upstream-specific IDs that are not in the public catalog is not included.

All 3 Meta Models

Open in model list
Meta 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
muse-spark-1.1Takes text, vision, audio, video, returns text.1.05M$1.375$4.675/M—125 tok/s1.95 s
muse-spark-1.2Takes text, vision, audio, video, returns text.1.05M$1.375$4.675/M—108 tok/s14.25 s
muse-spark-1.3Takes text, vision, audio, video, returns text.1.05M$1.375$4.675/M$0.165/M83 tok/s4.54 s

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

muse-spark-1.1 vs muse-spark-1.2muse-spark-1.1 vs muse-spark-1.3muse-spark-1.1 vs qwen3.8-max-previewkimi-k3 vs muse-spark-1.1glm-5.2 vs muse-spark-1.1

Meta on AIHubMix

Which Meta model should I start with?

muse-spark-1.1 at $1.375/M input — the cheapest entry here that declares tool calling, and it carries a 1.05M context. Move up to muse-spark-1.2 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-), 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.

How is cached input billed?

The Cache read column is the rate for input tokens served from the prompt cache — for example muse-spark-1.3 bills cache hits at 12% of the input rate. Cache write is the surcharge for putting a prompt into the cache in the first place, and only a few upstreams bill it separately. A dash in either column means the catalog carries no cache rate for that model, so plan on paying the full input rate.

Do I need a separate Meta 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
144 modelsOpenAI
134 modelsGoogle
85 modelsZ.AI
71 modelsLlama
53 models

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One key, one endpoint, 880 models across 38 model authors.

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