Feature
Open-source AI models with native tool calling
Native tool calling means the model was trained to emit structured JSON function calls with reliable adherence to a schema. If your agent workflow depends on calling APIs, database queries or shell commands, this is the capability that matters most.
10 models with tool calling
Moonshot AI's 1-trillion-parameter mixture-of-experts (32B active per token). Trained on 15.5T tokens with a heavy emphasis on tool-use and agentic behaviour. Modified-MIT licence with an attribution clause for very-large deployments. Exceptional at long-horizon agent tasks; benchmarked well against Claude Sonnet on SWE-bench Verified.
- Context
- 128K
- License
- kimi
- VRAM Q4
- 600 GB
Meta's July 2024 flagship — the first open-weights model at 405B parameters. Trained on 15T tokens with 128K context. Rivals GPT-4o on many academic benchmarks and set the ceiling for open-weights quality for most of 2024. Running it self-hosted requires serious hardware (8× H100 at fp8 or multi-node at fp16); most users will run it via a hosted provider (Together, Groq, Fireworks). Llama 3.3 70B closed most of the practical gap at a fraction of the cost, so 405B is now most useful when 70B specifically hits its ceiling.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 243 GB
The flagship Qwen 3 release: a 235B-total MoE with 22B active parameters per token. Competitive with DeepSeek V3 and Llama 4 Maverick on reasoning benchmarks while being smaller total. Apache 2.0 — one of the most permissively licenced frontier-class models.
- Context
- 128K
- License
- apache-2-0
- VRAM Q4
- 141 GB
The pre-3.3 70B workhorse. Same base architecture as Llama 3.3 70B but the earlier instruction-tuning recipe. Still widely referenced as a baseline in papers and provider docs, and still the default 70B on some hosted providers.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 42 GB
Larger Hermes 3 variant on top of Llama 3.1 70B. Widely used in agent-heavy workloads that need strong tool use combined with reliable function-calling schemas.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 42 GB
32B sweet-spot Qwen 3, Apache 2.0. Reasoning-mode toggle inherited from smaller siblings; strong on math, code and agentic tool use. Fits on a single H100 in fp16 and on a 4090 at Q4.
- Context
- 33K
- License
- apache-2-0
- VRAM Q4
- 19.2 GB
Meta's April 2025 mixture-of-experts release. 17B active parameters across 16 experts (109B total). Natively multimodal with an unprecedented 10M-token context window — a leap far beyond Llama 3's 128K. Scout was designed to run on a single GPU at Q4 while beating Llama 3.3 70B on reasoning and multilingual benchmarks. The Llama 4 licence tightened acceptable-use provisions vs Llama 3.
- Context
- 10.0M
- License
- llama-4
- VRAM Q4
- 10.2 GB
Larger Llama 4 sibling of Scout — 17B active across 128 experts (400B total). 1M-token native context. Positioned as GPT-4o-class on chat and reasoning while remaining tractable on a single high-end host at fp8. Multimodal from the ground up; instruction-tuned by Meta with a heavier synthetic-data pipeline than Llama 3.
- Context
- 1.0M
- License
- llama-4
- VRAM Q4
- 10.2 GB
NousResearch's community-driven fine-tune on the Llama 3.1 8B base. Tuned for strong tool use, function calling and steerable persona behaviour. Inherits Llama 3's community licence and its 128K context.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 4.8 GB
The April 2025 refresh of Qwen at 8B. Native mixed-mode reasoning: the model can 'think' before answering when triggered, or answer directly for simple queries — configurable per request. Apache 2.0. A strong upgrade over Qwen 2.5 7B on math and code, with much better instruction following.
- Context
- 33K
- License
- apache-2-0
- VRAM Q4
- 4.8 GB