Every model in the catalog with source-linked pricing, context limits, provider availability, and published benchmark results.
Muse Spark 1.3 is a multimodal reasoning model from Meta for long-running agentic, multi-agent, and coding workflows. It improves long-horizon agent collaboration, instruction following, and coding efficiency relative to Muse Spark 1.2.
Muse Spark 1.2 is a coding-focused update to Muse Spark 1.1 with improvements in code generation, complex debugging, codebase understanding, and end-to-end developer workflows.
Muse Spark is a natively multimodal reasoning model with support for tool-use, visual chain of thought, and multi-agent orchestration.
Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...
Muse Spark 1.2 contributor tier is a reasoning model from Meta designed for developers who want to start building at an even lower cost. It’s meaningfully cheaper than Muse Spark...
No provider description is available for this model yet.
Muse Spark 1.3 Contributor is the cost-efficient contributor tier of Meta’s multimodal reasoning model for experimentation, learning, and early-stage agentic, multi-agent, and coding workflows. It is designed to track information...
No provider description is available for this model yet.
No provider description is available for this model yet.
No provider description is available for this model yet.
Muse Glimmer 30B is a dense, open-weight multimodal model from Meta Superintelligence Labs, distilled from Muse Spark and optimized for autonomous agents on consumer hardware. It is suited for long-horizon...
Llama 3.2 11B Vision is a multimodal model with 11 billion parameters, designed to handle tasks combining visual and textual data. It excels in tasks such as image captioning and...
No provider description is available for this model yet.
Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward...
Llama Guard 4 is a Llama 4 Scout-derived multimodal pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM...
| Model | Creator | Inputs | Context | Input | Output | Released | Compare |
|---|---|---|---|---|---|---|---|
| Muse Spark 1.3meta/muse-spark-1.3 | Meta |
1.04858M | $1.25 | $4.25 | 2026-09-02 | ||
| Muse Spark 1.2meta/muse-spark-1.2 | Meta |
1.04858M | $1.25 | $4.25 | 2026-08-05 | ||
| Muse Spark 1.1meta/muse-spark-1.1 | Meta |
1.04858M | $1.25 | $4.25 | 2026-04-08 | ||
| Meta: Llama 4 Scoutmeta-llama/llama-4-scout | Meta |
327.68K | $0.1 | $0.3 | — | ||
| Meta: Muse Spark 1.2 Contributormeta/muse-spark-1.2-contributor | Meta |
1.04858M | $0.1 | $0.2 | — | ||
| meta-llama/Llama-4-Maverick-17B-128E-Instructmeta-llama/Llama-4-Maverick-17B-128E-Instruct | Meta |
Not documented | — | — | — | ||
| Meta: Muse Spark 1.3 Contributormeta/muse-spark-1.3-contributor | Meta |
1.04858M | $0.1 | $0.2 | — | ||
| meta-llama/Llama-Guard-4-12Bmeta-llama/Llama-Guard-4-12B | Meta |
Not documented | — | — | — | ||
| meta-llama/Llama-4-Maverick-17B-128Emeta-llama/Llama-4-Maverick-17B-128E | Meta |
Not documented | — | — | — | ||
| meta-llama/Llama-4-Scout-17B-16Emeta-llama/Llama-4-Scout-17B-16E | Meta |
Not documented | — | — | — | ||
| Meta: Muse Glimmer 30B (batch)meta/muse-glimmer-30b:batch | Meta |
131.072K | $0.175 | $0.75 | — | ||
| Meta: Llama 3.2 11B Vision Instructmeta-llama/llama-3.2-11b-vision-instruct | Meta |
131.072K | $0.345 | $0.345 | — | ||
| meta-llama/Llama-4-Scout-17B-16E-Instructmeta-llama/Llama-4-Scout-17B-16E-Instruct | Meta |
Not documented | — | — | — | ||
| Meta: Llama 4 Maverickmeta-llama/llama-4-maverick | Meta |
128K | $0.2 | $0.696 | — | ||
| Meta: Llama Guard 4 12Bmeta-llama/llama-guard-4-12b | Meta |
163.84K | $0.18 | $0.18 | — |