Naive-N0.5-Flash charges $0.10 input, $0.40 output, $0.01 cache per million tokens, with Ultrafast mode hitting 2,000 tokens/sec.
What does Naive-N0.5-Flash cost on the API? #
Naive-N0.5-Flash is priced at $0.10 per million input tokens, $0.40 per million output tokens, and $0.01 per million cached tokens. That puts it in the budget tier of coding-focused models, especially for workloads that lean on long context and repeated cache hits, like agentic coding sessions that keep re-reading the same files or repo state.
TL;DR #
- Input tokens cost $0.10 per million , output tokens cost $0.40 per million, and cache reads cost just $0.01 per million, according to the model card.
- Cache pricing is the standout detail at one-tenth the input price, which rewards workflows that repeatedly reuse context, such as long coding sessions or multi-turn agents with a 1M-token window.
- Standard inference runs at 50 tokens/sec per user , while an optional Ultrafast mode pushes that to up to 2,000 tokens/sec, a 40x jump.
- The speed boost comes from NaiveRT , the model’s dedicated inference system, which combines mega-kernel fusion, Programmatic Dependent Launch (PDL), and speculative decoding.
- Naive-N0.5-Flash is a 309B-parameter MoE model with only 15.5B active parameters , which is part of why it can serve cheaply despite its total size.
- Weights are open under the MIT license , so the API pricing is an optional convenience layer, not the only way to run the model.
How does the per-token pricing break down? #
Remy doesn't build the plumbing. It inherits it. #
Other agents wire up auth, databases, models, and integrations from scratch every time you ask them to build something.
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The three-tier structure (input, output, cache) is standard for API-based LLM pricing, but the ratios matter. At $0.10 input and $0.40 output, output tokens cost four times as much as input tokens, which is typical for models where generation is the heavier compute step. The cache-read price of $0.01 per million tokens is ten times cheaper than fresh input tokens, which is a meaningful incentive for anyone building agents or tools that repeatedly send overlapping context (think long-running coding sessions, large codebases read into context, or multi-step agent loops that recheck prior state).
For context, this pricing sits in a bracket that’s cheap relative to frontier closed models, but the real comparison point is other “Flash” or distilled-tier open coding models, which tend to compete heavily on cost per token. The model card frames Naive-N0.5-Flash as aimed squarely at coding and AI R&D use cases, where high token throughput (reading large files, running test loops, generating long patches) makes the cache discount and the low output price practically relevant rather than just a marketing number.
What is Ultrafast mode and how is it different from Standard mode? #
Naive-N0.5-Flash’s inference system, called NaiveRT, exposes two speed tiers:
- Standard mode : 50 tokens/sec per user.
- Ultrafast mode : up to 2,000 tokens/sec.
That’s a roughly 40x difference in raw decoding speed. According to the model card, NaiveRT achieves this through three techniques working together: mega-kernel fusion (combining multiple GPU operations into fewer, larger kernels to cut overhead), Programmatic Dependent Launch (PDL, which overlaps kernel launches with dependent computation to reduce idle GPU time), and speculative decoding (where a smaller or cheaper draft process predicts multiple tokens ahead, which the main model then verifies in parallel, cutting the number of full forward passes needed per output token).
The model card describes NaiveRT as having been built and optimized through “AI-centered R&D,” meaning the optimization work itself leaned on AI tooling rather than purely manual kernel engineering. Details on the implementation are covered in NaiveAI’s technical blog as a dedicated case study, separate from the model release itself.
Why does inference speed matter for a coding model? #
Coding and agentic workloads are latency-sensitive in a way that casual chat isn’t. A single agentic coding task might involve many sequential steps: read a file, propose an edit, run a test, read the output, propose another edit. Each round trip adds the model’s full generation latency. At 50 tokens/sec, a few-hundred-token response takes several seconds; at 2,000 tokens/sec, that same response returns in a fraction of a second.
For interactive use (a developer waiting on autocomplete or a chat-style coding assistant), that difference is the gap between a responsive tool and a sluggish one. For autonomous agent loops that chain dozens or hundreds of model calls to complete a task, the cumulative latency savings compound fast. This is likely why NaiveAI built Ultrafast mode as a distinct tier rather than just trying to make Standard mode faster across the board: not every use case needs or wants to pay for the fastest tier, but agentic workflows specifically benefit from it.
Is Naive-N0.5-Flash’s pricing competitive? #
On paper, yes, for a model built around coding and AI R&D tasks. The combination of low per-token costs and a steep cache discount specifically favors the kind of repeated, context-heavy interaction pattern that coding agents produce. A model that’s cheap per token but expensive to re-read context with would lose much of its advantage in real agentic workloads; Naive-N0.5-Flash’s $0.01 cache price avoids that trap.
The architecture also helps explain how the pricing is sustainable. Naive-N0.5-Flash is a Mixture-of-Experts (MoE) model with 309B total parameters but only 15.5B active parameters per forward pass. MoE architectures route each token through a small subset of the full parameter set, which means inference compute costs scale with the active parameter count, not the total one. That’s a major reason a model this large can be served at these token prices and still support a 1M-token native context window without full-attention layers (the hybrid Sliding-Window Attention and DeepSeek Sparse Attention design keeps per-token decode cost from growing with context length).
It’s worth noting that pricing and speed tiers are specific to NaiveAI’s own hosted API. Because the weights are released under the MIT license, anyone with the hardware (the model card notes FP8-capable NVIDIA GPUs and roughly 315 GB of weight storage) can self-host and sidestep API pricing entirely, trading convenience for infrastructure cost.
Frequently Asked Questions #
How much does Naive-N0.5-Flash cost per million tokens?
$0.10 for input tokens, $0.40 for output tokens, and $0.01 for cache reads, per the official model card.
What is Ultrafast mode in Naive-N0.5-Flash?
It’s an inference speed tier available through NaiveRT, the model’s inference system, that delivers up to 2,000 tokens/sec per user, compared to 50 tokens/sec in Standard mode. It relies on mega-kernel fusion, Programmatic Dependent Launch, and speculative decoding.
Why is cache token pricing so much cheaper?
Cache reads reuse previously processed context instead of recomputing it from scratch, so they’re computationally cheaper for the provider to serve. At $0.01 per million tokens, cache reads cost a tenth of standard input tokens, which rewards workflows that repeatedly reuse large contexts, like coding agents working across a big repository.
Does Ultrafast mode cost more than Standard mode?
The model card lists a single set of per-token prices ($0.10/$0.40/$0.01) without specifying a separate price for Ultrafast mode, so no additional per-token surcharge is confirmed in the available documentation.
Can I avoid API pricing entirely by self-hosting?
Yes. Naive-N0.5-Flash’s weights and inference code are released under the MIT license, so it can be run on your own hardware. The model card notes it requires FP8-capable NVIDIA GPUs and roughly 315 GB just for the weights, before accounting for inference memory overhead.