A balanced model in the Ministral 3 family, Ministral 3 8B is a powerful, efficient tiny language model with vision capabilities.
Models
Every model in the catalog with source-linked pricing, context limits, provider availability, and published benchmark results.
A balanced model in the Ministral 3 family, Ministral 3 8B is a powerful, efficient tiny language model with vision capabilities.
Granite 4.1 8B is a dense, decoder-only 8-billion-parameter language model from IBM, part of the Granite 4.1 family. It supports a 131K-token context window and is designed for enterprise tasks...
Qwen3-8B is a dense 8.2B parameter causal language model from the Qwen3 series, designed for both reasoning-heavy tasks and efficient dialogue. It supports seamless switching between "thinking" mode for math,...
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...
LFM2.5-2.6B is a compact reasoning model from Liquid AI. It is suited for agent workflows, data extraction, RAG, and long-context processing. Liquid advises against using it for agentic coding or...
LFM2.5-2.6B is a compact reasoning model from Liquid AI. It is suited for agent workflows, data extraction, RAG, and long-context processing. Liquid advises against using it for agentic coding or...
Open multimodal Gemma instruction model for multilingual text generation and image understanding
Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 8B instruct-tuned version is fast and efficient. It has demonstrated strong performance compared to...
The smallest model in the Ministral 3 family, Ministral 3 3B is a powerful, efficient tiny language model with vision capabilities.
Efficient on-device Gemma 3n model for text, image, and audio understanding
Open multimodal Gemma instruction model for efficient text generation and image understanding
| Model | Creator | Score | Inputs | Context | Input | Output | Released | Compare |
|---|---|---|---|---|---|---|---|---|
| Mistral: Ministral 3 8B 2512mistralai/ministral-8b-2512 | 9.7 | 262.144K | $0.15 | $0.15 | — | |||
| Mistral: Ministral 3 8B 2512 (batch)mistralai/ministral-8b-2512:batch | 9.7 | 262.144K | $0.075 | $0.075 | — | |||
| IBM: Granite 4.1 8Bibm-granite/granite-4.1-8b | 9.5 | 131.072K | $0.05 | $0.1 | — | |||
| Qwen: Qwen3 8Bqwen/qwen3-8b | 9.0 | 131.072K | $0.117 | $0.455 | — | |||
| Meta: Llama 4 Scoutmeta-llama/llama-4-scout | 8.2 | 327.68K | $0.1 | $0.3 | — | |||
| LiquidAI: LFM2.5-2.6Bliquid/lfm-2.5-2.6b | 7.7 | 128K | $0.35 | $0.5 | — | |||
| LiquidAI: LFM2.5-2.6B (free)liquid/lfm-2.5-2.6b:free | 7.7 | 65.536K | Free | Free | — | |||
| Gemma 3 12B ITgoogle/gemma-3-12b-it | 5.8 | 131.072K | $0.04 | $0.12 | 2025-03-12 | |||
| Meta: Llama 3.1 8B Instructmeta-llama/llama-3.1-8b-instruct | 5.4 | 131.072K | $0.05 | $0.08 | — | |||
| Mistral: Ministral 3 3B 2512mistralai/ministral-3b-2512 | 4.8 | 131.072K | $0.1 | $0.1 | — | |||
| Gemma 3n E4B ITgoogle/gemma-3n-e4b-it | 3.2 | 32.768K | — | — | 2025-06-26 | |||
| Gemma 3 4B ITgoogle/gemma-3-4b-it | 2.7 | 131.072K | $0.04 | $0.08 | 2025-03-12 |