# ZAYA1-8B vs DeepSeek-R1-0528: which open model enterprises should use, and how to run it with Regolo

For most companies, ZAYA1-8B is the better open-weight choice when coding, reasoning efficiency, and serving cost matter more than raw scale, while DeepSeek-R1-0528 is the safer choice for very long inputs and long outputs in a single pass. Zyphra positions ZAYA1-8B as competitive with or ahead of DeepSeek-R1-0528 on several hard reasoning and coding benchmarks despite using about 760M active parameters, while DeepSeek-R1-0528 publishes a 163,840-token context window and up to 33K output tokens.

![](https://regolo.ai/wp-content/uploads/2026/05/chart_capabilities-2-1024x683.png)

![](http://regolo.ai/wp-content/uploads/2026/05/chart_capabilities-3-1024x683.png)

The table below uses editorial scores from 0 to 100 built from the published benchmark claims, listed API pricing, context limits, release dates, and published modality or deployment characteristics in the cited sources.

| Metric | ZAYA1-8B | DeepSeek-R1-0528 |  |
|---|---|---|---|
| Capabilities score | 91/100 | 89/100 | ZAYA has the edge on reasoning density and coding-focused value. |
| Pricing efficiency | 98/100; public metadata shows $0 / $0 on one model listing, and Zyphra also presented free serverless availability at launch. | 78/100; public provider listings start at $0.20 input / $0.25 output per 1M tokens, with many listings materially higher. | ZAYA is the stronger quality-to-cost play. cloudprice+2 |
| Context window size | 131K | 163,840 | DeepSeek is better when one request must absorb more material. cloudprice+1 |
| Output capacity | Not publicly specified in the reviewed ZAYA sources; neutral score used in the composite. | 33K | DeepSeek is safer for long single-pass generation. |
| Recency | May 11, 2026 | May 28, 2025 | ZAYA is fresher. |
| Composite score globale | 85.1/100 | 84.9/100 | Near tie overall, but for different reasons. |

## Benchmarks

![](https://regolo.ai/wp-content/uploads/2026/05/chart_capabilities-1-1024x592.png)Useful when you need to choose the model for a specific project. Scenario: Your development team needs a code review assistant for a medium-large Python codebase. You look at the "Coding & Code Review" bar: ZAYA1-8B scores 9.4 versus DeepSeek's 9.0, and has significantly better latency (9.1 versus 6.4). The choice is clear without having to read technical papers.

![](https://regolo.ai/wp-content/uploads/2026/05/chart_benchmark-1-1024x592.png)Useful when you need to justify your choice to an IT manager or procurement representative who requests a structured evaluation. Scenario: You need to explain in 30 seconds why Regolo recommends ZAYA over DeepSeek for an internal agent. Show this chart: ZAYA dominates in Pricing Efficiency and Recency, while DeepSeek dominates in Output Capacity and Context Window. Each stakeholder immediately sees which metric matters for their budget or use case.

![](https://regolo.ai/wp-content/uploads/2026/05/chart_pricing-1024x593.png)Useful when estimating the total cost of ownership (TCO) for a production deployment. Scenario: Your team processes 50 million tokens per day in batch extraction. With DeepSeek at the medium tier, you pay $2.20 in input and $8 in output per million; with ZAYA1-8B in controlled hosting, the base cost is $0 on Zyphra's public serverless. The logarithmic scale immediately shows that this isn't a marginal difference, but rather orders of magnitude.

![](https://regolo.ai/wp-content/uploads/2026/05/chart_context-1024x570.png)Useful when the operational constraint is the length of the input document or the output response. Scenario: A legal department needs to parse 120-page contracts in a single API call. With ZAYA1-8B at 131K tokens, the document barely gets through; with DeepSeek at 163.8K tokens, there's room for even longer contracts. The second panel shows that if the response needs to be long—detailed reports, extended code, complete documentation—DeepSeek with 33K output tokens is the only choice with a published limit.

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## How companies can use Zaya1-8B with Regolo GPU

To publish ZAYA1 on Regolo infrastracture, use **Custom Models**:

1. Import the model directly from Hugging Face
2. Save it to your private library,
3. Deploy it on a dedicated GPU instance sized for the model’s VRAM needs.

![](http://regolo.ai/wp-content/uploads/2026/02/Screenshot-2026-02-22-alle-19.15.26-1024x487.png)---

## FAQ

## Which model is cheaper to run?

ZAYA1-8B looks cheaper in the reviewed public listings: one model listing shows $0 / $0 metadata and Zyphra also presented free serverless availability at launch, while DeepSeek-R1-0528 starts at $0.20 / $0.25 per 1M tokens and is often listed much higher by providers.

## Which model is better for long PDFs, due diligence, or policy packs?

DeepSeek-R1-0528 is the safer answer because it publishes a 163,840-token context window and up to 33K output tokens, while the reviewed ZAYA sources show a 131K context listing and no public max-output figure.

## Which model is better for coding teams?

ZAYA1-8B is the more interesting choice for coding-heavy teams because Zyphra positions it as especially strong in mathematics, reasoning, and coding, and explicitly compares it favorably with DeepSeek-R1-0528 on several hard benchmarks despite far smaller active size.[](https://huggingface.co/Zyphra/ZAYA1-8B)

## What about OCR and image understanding?

Use a vision-language model instead. Zyphra’s own ZAYA1-VL-8B is the relevant sibling here, and Zyphra says it is strong at document understanding, grounding, and OCR-type tasks.

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