muse-glimmer-30bFireworks AI
- api
- chat
- hosting
- US
- model lab
- Meta
- weights
- open
- added
- August 2026
- model id
- fireworks/muse-glimmer-30b
capabilities 5/8
2 providers serve muse-glimmer-30b. This page is one of them. Compare all endpoints
- input
- $0.35
- output
- $1.50
- cache write
- -
- cache read
- $0.04
worked cost
| volume | cost |
|---|---|
| 100K in + 10K out | $0.0500 |
| 1M in + 100K out | $0.50 |
| 10M in + 1M out | $5.00 |
Requesty charges what the upstream provider charges, with no markup and no per-request fee. Prompt caching and smart routing cut the effective cost further on repeated context. Gateway pricing
no measured traffic for this endpoint yet. pricing above is the provider's own
no published scores for this exact variant. region deployments and highspeed tiers usually share the base model's results
- retention
- none
- trains on prompts
- no
- hosted in
- US
Terms are the provider's, not Requesty's: routing a request here puts it under them. Fireworks Privacy Policy
Need every request to stay in one jurisdiction? EU hosting routes only through EU-hosted providers.
fireworks/muse-glimmer-30bthis id pins the fireworks ai deployment, with no routing and no failover. the managed id on the canonical page routes across all 2 providers instead
from openai import OpenAI client = OpenAI( api_key="YOUR_REQUESTY_API_KEY", base_url="https://router.requesty.ai/v1",) response = client.chat.completions.create( model="fireworks/muse-glimmer-30b", messages=[ {"role": "user", "content": "Explain quantum computing in one paragraph."}, ],) print(response.choices[0].message.content)
Change the base url to https://router.requesty.ai/v1, use your Requesty key, set the model to the id above. Existing OpenAI SDK code needs no other edit, and the same key reaches every other model in the catalog. Browse all models
Muse Glimmer 30B is a dense causal language model distilled from Muse Spark and purpose-built for autonomous agentic work. It combines multi-step reasoning, reliable schema-based tool calling, and failure recovery with multimodal understanding via a ~1.8B ViT-G/14 perception encoder, supporting interleaved text and image input, a 131K+ context window, and selectable reasoning strength (low through xhigh). Trained on data from over 100 languages, Muse Glimmer performs strongly for its size class on agentic benchmarks including MCP Atlas, DeepSearch QA, Gaia2 and SWE-Bench Pro, and is released under Apache 2.0.
questions 5
How much does muse-glimmer-30b cost?
What is the context window of muse-glimmer-30b?
What can muse-glimmer-30b do?
How do I use muse-glimmer-30b with the OpenAI SDK?
Can I run muse-glimmer-30b through Requesty?
more from fireworks ai 8
| endpoint | ctx | in /M |
|---|---|---|
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| deepseek-v4-flash-vision-exp | 1.0M | $0.22 |
| glm-5.3-flash | 1.0M | $0.15 |
| glm-5.3 | 1.0M | $1.40 |
| nemotron-lightning-3.5-30b-a3b | 262K | $0.05 |
| deepseek-v4-pro-0813 | 1M | $1.32 |
| qwen3.8-max | 1M | $2.00 |
| deepseek-v4-flash-0731 | 1M | $0.22 |
call muse-glimmer-30b through one endpoint
One key for this endpoint and 600+ other models. No markup on provider prices, automatic failover when a provider degrades, and prompt caching built in. Methodology