Allocate
Muse Glimmer 30B
Open weightsNew- Price first checked
- Listed on Allocate
Muse Glimmer 30B is a language model from Meta with a 128K-token context window. Provider list price is $0.35 per million input tokens and $1.50 per million output; on Allocate you pay $0.37 and $1.60. The weights are open, so you can fine-tune it and own the result.
What a real workload costs
Take 1,000,000 requests a month at 1,200 input and 350 output tokens each: 1,200M input and 350M output tokens. At list prices that is 1,200 × $0.35 + 350 × $1.50 = $945 a month. Billed on Allocate it is $1,011.
Example usage
curl https://api.allocate.network/v1/chat/completions \
-H "Authorization: Bearer $ALLOCATE_KEY" \
-d '{
"model": "meta/muse-glimmer-30b",
"messages": [{ "role": "user", "content": "Summarise the attached contract." }]
}'Common questions
How much does Muse Glimmer 30B cost per million tokens?
Provider list price is $0.35 per million input tokens and $1.50 per million output tokens. On Allocate you pay $0.37 in and $1.60 out.
What context window does Muse Glimmer 30B have?
131,072 tokens (128K). At roughly 0.75 words per token, that is about 98k words of English text per request.
What does cached input cost on Muse Glimmer 30B?
$0.04 per million tokens at list ($0.043 billed). Repeated prompt prefixes, such as a stable system prompt or tool definitions, bill at this rate instead of the full input price.
Can I fine-tune Muse Glimmer 30B?
Yes. Muse Glimmer 30B is an open-weights model; check the lab’s model card for the exact license terms. Read the license terms before fine-tuning for commercial use. On Allocate the trained weights stay inside your boundary and belong to you.
Has the price of Muse Glimmer 30B changed?
Allocate has checked the provider list price hourly since 12 Sep 2026. It has not changed in that time. The price history chart on this page shows every change.
How do I call Muse Glimmer 30B on Allocate?
Send meta/muse-glimmer-30b in the model field of the OpenAI-compatible endpoint at api.allocate.network/v1, or point a route name (like prod/support-agent) at it so you can swap the model later without a deploy.