Naive-N0.5-Flash Pricing: Free Weights, Cheap API Tokens
Naive-N0.5-Flash weights are free under MIT license. API pricing runs $0.10/$0.40/$0.01 per million input, output, and cache tokens.
What does Naive-N0.5-Flash cost?
Naive-N0.5-Flash is free to download and run yourself: the model weights and inference code are released under the MIT license, with no usage fees or restrictions on commercial use. If you’d rather call it through NaiveAI’s hosted API instead of running the 309B-parameter model on your own hardware, pricing is set at $0.10 per million input tokens, $0.40 per million output tokens, and $0.01 per million cached tokens.
TL;DR
- The weights are free. Naive-N0.5-Flash ships under the MIT license, so anyone can download, modify, fine-tune, or deploy it commercially without paying NaiveAI anything.
- API pricing is split into three tiers. Input tokens run $0.10 per million, output tokens run $0.40 per million, and cache reads run $0.01 per million, a structure that rewards repeated or long-context prompts.
- Self-hosting isn’t free in practice. The model needs roughly 315 GB just to load the weights, plus additional headroom for inference, meaning you need serious FP8-capable GPU infrastructure to run it locally.
- The cache tier is where the real savings show up. At $0.01 per million tokens, cached reads cost a fraction of fresh input tokens, which matters a lot given the model’s native 1M-token context window.
- This is a coding and AI R&D model, not a general chatbot. The pricing and architecture choices (sparse attention, long context) are tuned for agentic coding and research workloads rather than casual chat use.
- Inference speed factors into effective cost. NaiveAI’s own inference stack, NaiveRT, claims up to 2,000 tokens/second in “Ultrafast” mode, which changes the cost-per-task math even at fixed token prices.
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How does the pricing break down per tier?
The API charges three separate rates depending on what kind of tokens you’re using:
- Input tokens: $0.10 per million. This is what you pay for new text sent to the model, prompts, documents, code, whatever you’re feeding in for the first time.
- Output tokens: $0.40 per million. Generated text costs four times more than input, which is standard across most commercial LLM APIs since generation is more compute-intensive than reading a prompt.
- Cache reads: $0.01 per million. If you’re resending context the model has already processed (a common pattern in agentic coding workflows where a large codebase or conversation history sits in context across many calls), you pay a tenth of the standard input rate.
That cache pricing is the detail worth paying attention to. Given the model’s native 1M-token context window, workflows that keep a large chunk of stable context (a repo, a spec, a long chat history) and only append small deltas each turn will see most of their tokens hit the cheap cache tier instead of the full input rate. For agentic coding tools that repeatedly re-send large contexts, that’s a meaningful cost difference over thousands of calls.
Is running it yourself actually free?
Technically yes, financially it’s complicated. The MIT license means there’s no licensing fee, no usage cap, and no restriction on commercial deployment. But “free” only covers the software. The hardware requirement is substantial: Naive-N0.5-Flash is a 309B-parameter Mixture-of-Experts model, and even though only 15.5B parameters are active per token, the full weight set still needs to be loaded into memory. The model card puts that at approximately 315 GB for the FP8 version, before accounting for the extra memory inference itself requires (KV cache, activations, and so on).
That means self-hosting only makes financial sense if you already have, or plan to invest in, a multi-GPU FP8-capable setup, most likely a cluster of high-memory NVIDIA GPUs. For individual developers or small teams, the $0.10/$0.40/$0.01 API pricing will almost always be cheaper than provisioning and maintaining that kind of hardware, unless you’re running the model at very high volume or need it fully on-premises for data control reasons.
Why does the pricing structure favor agentic coding workloads?
Naive-N0.5-Flash was built specifically for coding and AI R&D tasks, and both the architecture and the pricing reflect that focus. The model’s hybrid Sliding-Window Attention and DeepSeek Sparse Attention design keeps decoding costs manageable even at a full 1M-token context, which is exactly the kind of context length agentic coding tools need when they’re holding an entire codebase, test suite, or research paper in memory across a long session.
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The cheap cache-read rate ($0.01 per million tokens) directly supports that use case. Agent loops in tools like Claude Code or similar harnesses tend to resend large amounts of stable context turn after turn: the same files, the same instructions, the same accumulated conversation. If every one of those resends cost the full input rate, long agentic sessions would get expensive fast. Pricing cache reads at a tenth of standard input cost changes that math, making sustained, high-context agent workflows considerably cheaper per session than a flat per-token rate would.
Output tokens are priced at $0.40 per million, roughly 4x the input rate, which is in line with how most providers price generation relative to reading. For coding tasks specifically, that’s the tier to watch, since code generation, refactors, and long diffs all count as output tokens.
Is Naive-N0.5-Flash worth using over other open-weight models?
That depends on your workload and what you’re comparing it against. In terms of raw pricing, $0.10/$0.40 per million input/output tokens sits at the lower end of what’s currently available for a frontier-scale open-weight model, and the free MIT-licensed weights remove the licensing barrier entirely if you have the hardware to self-host. Combined with a native 1M-token context window and an inference stack (NaiveRT) built for high throughput, the pricing model is clearly aimed at developers running long, iterative, agentic workloads rather than short one-off queries.
Whether it’s “worth it” compared to hosted alternatives depends on your specific benchmarks and latency needs, which is a separate question from pricing. But from a pure cost-access standpoint, Naive-N0.5-Flash offers two real paths: free self-hosting for teams with the GPU budget, and a low, cache-friendly API rate for everyone else.
Frequently Asked Questions
Is Naive-N0.5-Flash completely free to use?
The model weights and inference code are free under the MIT license, meaning no licensing fees for self-hosting. The hosted API is not free and charges per token across three tiers: input, output, and cache reads.
How much GPU memory do I need to run Naive-N0.5-Flash myself?
The model weights occupy approximately 315 GB on their own, with additional GPU memory needed on top of that for inference (KV cache and activations). This requires FP8-capable NVIDIA GPUs, effectively ruling out consumer hardware.
What’s the difference between input, output, and cache token pricing?
Input tokens ($0.10/million) are new content sent to the model for the first time. Output tokens ($0.40/million) are what the model generates in response. Cache reads ($0.01/million) apply to previously processed context being reused, which is common in long agentic sessions.
Why is the cache token rate so much cheaper?
Cache reads let the API reuse computation from context the model has already processed, rather than reprocessing it from scratch. This is especially valuable given the model’s 1M-token context window, where agentic workflows often resend large amounts of stable context across many calls.
Can I use Naive-N0.5-Flash commercially without paying NaiveAI?
Yes, if you self-host. The MIT license permits commercial use of the weights and code without any fee owed to NaiveAI. You only pay if you use their hosted API instead of running the model on your own infrastructure.