SmolLM3 3B

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released July 202566K context≈ 49,152 words3B params

SmolLM3 3B punches above its weight class for a 3-billion parameter model, handling reasoning, instruction-following, and multi-turn conversation with surprising coherence. It's the compact colleague who gets things done without demanding expensive hardware — running comfortably on consumer devices and edge deployments. The trade-off is the ceiling: complex multi-step reasoning or deep domain expertise will eventually expose the limits of its size.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Instruction Following

Moderate

Multimodal

SmolLM3 3B

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released July 202566K context≈ 49,152 words3B params

SmolLM3 3B punches above its weight class for a 3-billion parameter model, handling reasoning, instruction-following, and multi-turn conversation with surprising coherence. It's the compact colleague who gets things done without demanding expensive hardware — running comfortably on consumer devices and edge deployments. The trade-off is the ceiling: complex multi-step reasoning or deep domain expertise will eventually expose the limits of its size.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Instruction Following

Moderate

Multimodal

Glossary

CoherenceThe quality of maintaining consistent meaning and logical flow across multiple sentences or exchanges in a conversation.Domain ExpertiseDeep knowledge and experience in a specific field or function that helps users leverage AI tools effectively.Instruction-FollowingThe ability of a model to understand and execute specific tasks or commands given in natural language prompts.Multi-Step ReasoningThe ability to break down complex problems into smaller steps and solve them sequentially, rather than jumping directly to an answer.Multi-Turn ConversationThe ability to maintain context and coherence across multiple back-and-forth exchanges with a user, remembering earlier messages in the conversation.Parameter ModelA neural network described by the number of learnable weights it contains; more parameters generally mean greater capacity to learn complex patterns, but also require more computational resources.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.

Capabilities

Capabilities

Use Case Fit

Glossary