Contest-style problem solving
Generate candidate Python solutions for algorithmic problems with explicit time, memory, input, and output constraints.
Independent tool overview
NousCoder-14B at a glance
NousCoder-14B is a downloadable, open-weight Qwen3-based model optimized for solving long-form Python competitive-programming problems.
Visit the official NousCoder-14B site ↗
- Best for
- Python competitive programming and code-model research
- Model
- Qwen3-based, about 15B parameters
- Context
- Up to 81,920 tokens in the published configuration
- License
- Apache 2.0
- Price
- Free weights; compute and hosting extra
- Reviewed
- August 29, 2026
Overview
What NousCoder-14B is
NousCoder-14B is a roughly 15-billion-parameter coding model from Nous Research. It starts from Qwen3-14B and uses reinforcement learning on 24,000 verifiable programming problems, making it most relevant to algorithmic coding, contest practice, and research on code reasoning.
Nous reports 67.87% Pass@1 on its LiveCodeBench v6 evaluation at an 81,920-token context, compared with 60.79% for its Qwen3-14B baseline. That is a strong result for the tested window and setup, but it should not be read as a general software-engineering score: the training and evaluation focus on Python solutions to competitive-programming problems.
The model weights are free to download under Apache 2.0. This is not a polished coding app or managed API, and the official Hugging Face page did not list an inference provider at review time. Users need compatible local or cloud inference infrastructure, and should independently test correctness, security, latency, and memory use.
Use cases
Who NousCoder-14B is best for
The strongest fit depends on the job you need the product to complete, not the size of its feature list.
Local coding-model experiments
Run or quantize an Apache-licensed model on infrastructure you control.
Reinforcement-learning research
Study an openly documented model trained with verifiable code execution and outcome-based rewards.
Capabilities
Core NousCoder-14B features
Competitive-programming specialization
Post-trained specifically on verifiable algorithmic coding problems rather than positioned as a general chat model.
Long context
The published configuration supports up to 81,920 tokens using YaRN context scaling.
Open weights
BF16 Safetensors weights are available from the official Nous Research repository on Hugging Face.
Permissive license
The model card lists Apache 2.0, allowing broad use subject to the license terms.
Documented training method
Nous publishes the dataset composition, reward design, infrastructure, hyperparameters, and several RL objective comparisons.
Community quantizations
Third-party quantized versions exist for lower-memory runtimes, though they are separate community artifacts and require their own verification.
Process
How the NousCoder-14B workflow works
Step 1
Confirm the task fit
Choose NousCoder for algorithmic Python generation; benchmark another model if the real task is repository editing, tool use, or multi-language development.
Step 2
Choose a serving format
Download the official BF16 weights or evaluate a reputable quantization that fits the available memory and inference stack.
Step 3
Use the correct chat template
Load the official tokenizer and model configuration rather than guessing prompt tokens or context settings.
Step 4
Sandbox generated code
Execute candidates with strict time, memory, network, and filesystem limits, just as the research uses isolated verification.
Step 5
Evaluate on your workload
Measure compile rate, test-pass rate, latency, cost, and security on unseen examples that match the intended use.
Cost
NousCoder-14B pricing and free plan
NousCoder-14B's official weights are free under Apache 2.0. There is no bundled managed service, so the real cost is the hardware, storage, inference platform, and engineering needed to run it.
Official model weights
Free
Download the BF16 Safetensors model from Nous Research's Hugging Face repository.
- Apache 2.0 license
- Approximately 15B parameters
- Official BF16 repository is about 29.6 GB
- Inference hardware, storage, and operations are not included
Self-hosted or cloud inference
Usage-based
Run the model on owned hardware or a compatible third-party GPU platform.
- Cost depends on precision, context length, throughput, and provider
- Long-context inference increases memory requirements
- Third-party quantizations and endpoints are not maintained by Nous Research
- Validate license, privacy, and performance before production use
Pricing checked . Check current pricing at the source ↗
Assessment
NousCoder-14B strengths and limitations
Where it stands out
- Strong Nous-reported improvement over its Qwen3-14B baseline on LiveCodeBench v6
- Open weights with a permissive Apache 2.0 license
- Detailed public write-up of the training and verification approach
- Long published context window for complex problem statements and reasoning
- Can be run in an environment the user controls
What to consider
- Specialization is narrow: published training and evaluation center on Python competitive-programming tasks
- LiveCodeBench results do not establish performance on repository navigation, debugging, frontend work, or production maintenance
- The official full-precision weights require substantial storage and runtime memory
- Nous does not bundle a consumer interface or official hosted endpoint with the model
- Long generations can saturate the context window; Nous identifies response-length control as future work
- Generated code can still be incorrect or unsafe and must be reviewed and sandboxed
Compare
NousCoder-14B alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Coding
Qwen3-Coder
Choose Qwen3-Coder for a broader agentic coding model designed around repository-scale and tool-using workflows.
Explore Qwen3-Coder →Coding
Devstral 2
Choose Devstral 2 when software-engineering agents and real codebase tasks matter more than contest-style problem solving.
Explore Devstral 2 →Coding
GLM 5.2
Choose GLM 5.2 for a newer general coding model with a managed API option and broader agentic positioning.
Explore GLM 5.2 →Questions
NousCoder-14B FAQs
What is NousCoder-14B?
It is a Qwen3-14B-based language model post-trained with reinforcement learning to generate Python solutions for competitive-programming problems.
Is NousCoder-14B free?
The official model weights are free to download under Apache 2.0. You still pay for the hardware, cloud compute, storage, and engineering used to run them.
What benchmark score does NousCoder-14B report?
Nous reports 67.87% Pass@1 on LiveCodeBench v6 at an 81,920-token evaluation context, versus 60.79% for its Qwen3-14B baseline in the same write-up.
Can NousCoder-14B replace a coding agent?
Not by itself. It is a model checkpoint, not a complete agent with repository search, file editing, tool execution, approvals, and change verification.
How large is NousCoder-14B?
Hugging Face labels it as roughly 15B parameters, and the official BF16 repository is about 29.6 GB before accounting for runtime memory and cache.
Does it support an 82K context window?
The official configuration sets a maximum of 81,920 tokens using YaRN scaling. Actual usable context depends on the inference engine, memory, and prompt.
Is generated code safe to run?
No model-generated code should be trusted automatically. Review it and run it in a sandbox with strict filesystem, network, time, and memory controls.
Bottom line
Our NousCoder-14B verdict
NousCoder-14B is an interesting open model for competitive-programming research and controlled local experiments. Its license, detailed training write-up, and benchmark gain are meaningful strengths. For day-to-day software engineering, a newer agentic coding model is usually the better default, and anyone deploying NousCoder should budget for infrastructure and rigorous execution safeguards.
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