Model Detail
Qwen3-Coder-30B-A3B-Instruct-GGUF
▲ 0.6%Qwen3-Coder-30B-A3B-Instruct-GGUF is a code generation model with 30B parameters released by unsloth. The model is registered under the text-generation pipeline tag on Hugging Face, and supports text->text inputs, distributed under the permissive apache-2.0 license.
Qwen3-Coder-30B-A3B-Instruct-GGUF has been evaluated across multiple task suites. On task-specific evaluations the model scores 60.4% resolved on SWE-Bench.
Qwen3-Coder-30B-A3B-Instruct-GGUF is priced at $0/M input tokens and $0/M output tokens. Operationally the model offers a 262K-token context window, which matters when sizing it for prompt-heavy or latency-sensitive workloads. At this input rate the model sits in the commodity tier and is suitable for high-volume workloads where per-call cost dominates the decision.
Qwen3-Coder-30B-A3B-Instruct-GGUF ships with 30B parameters, distributed as a quantized weight variant for lower-VRAM inference. The published knowledge cutoff is 2025-06-30, so newer events will not be reflected in zero-shot answers without retrieval. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
Downloads of Qwen3-Coder-30B-A3B-Instruct-GGUF have moved +0.6% over the past 24 hours. That is a slight downtrend, consistent with normal cooling as newer models compete for the same workloads. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.
Qwen3-Coder-30B-A3B-Instruct-GGUF is best fit for code completion, repository-scale Q&A, and pair-programming integrations, high-volume batch jobs where per-call cost dominates the budget, and long-context tasks such as full-codebase analysis or book-length summarization (262K tokens). It is a less obvious choice for one-shot generation of security-critical code without review. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
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