ZAYA1-8B

ZAYA1-8B#

Checkpoint has been reshaped for transformers PR, special thank you to @JJJYmmm. The original checkpoint is at Zyphra/ZAYA1-8B-legacy

ZAYA1-8B is a small mixture of experts language model with 760M active parameters and 8.4B total parameters trained end-to-end by Zyphra. ZAYA1-8B sets a new standard of intelligence efficiency for its parameter count through a combination of novel architecture and innovations in pretraining and post-training.

ZAYA1-8B excels at detailed long-form reasoning especially for mathematical and coding task. It punches heavily above its weight in these regimes and due to its inference efficiency and small size can be highly effective in test-time compute harnesses.

Due to its small total parameter count, ZAYA1-8B can also be deployed on-device for local LLM applications.

Learn more in our technical report and blog.

This is the post-trained reasoning version of ZAYA1-8B. The pretraining base can be found here.

Performance#

ZAYA1-8B performs extremely strongly, especially in challenging mathematical, reasoning, and coding benchmarks. ZAYA1-8B is competitive with models several times its own size including frontier-scale reasoning models at mathematical benchmarks.

Image: ZAYA_ttc_paper_light_no_dsv32_lcb_no_o4_hmmt_feb_dsv32_925_claude45_base_labels_matched_gap_transparent

Image: western_os_comparison_transparent_barchart

First we compare ZAYA1-8B to the SOTA Qwen3 and Qwen3.5 model series of approximately the same parameter count as well as the recently released Gemma4 models and secondly to a variety of larger open-weights models.

In-class comparison against open-source reasoning models#

| Category | Benchmark | ZAYA1-8B (0.7B / 8.0B) | Qwen3-4B-Thinking-2507 (4.0B / 4.0B) | Qwen3.5-4B (4.0B / 4.0B) | Gemma-4-E4B-it (4.0B / 8.0B*) |

|---|---|---:|---:|---:|---:|

| Math | AIME'26 | 89.1 | 77.5 | 84.5 | 50.3 |

| Math | HMMT Feb.'26 | 71.6 | 60.8 | 63.6 | 32.1 |

| Math | IMO-AnswerBench | 59.3 | 50.9 | 48.7 | 27.3 |

| Math | APEX-shortlist | 32.2 | 16.9 | -- | 6.1 |

| Code | LiveCodeBench-v6 | 65.8 | 54.2 | -- | 54.2 |

| Knowledge | GPQA-Diamond | 71.0 | 66.5 | 76.2 | 57.4 |

| Knowledge | MMLU-Pro | 74.2 | 74.3 | 79.1 | 70.2 |

| Instruction | IFEval | 85.58 | 86.8 | 89.8 | 88.50 |

| Instruction | IFBench | 52.56 | 52.9 | 59.2 | 42.67 |

| Style & chat | EQBench | 72.95 | 79.6 | 79.5 | 80.15 |

| Style & chat | Creative Writing v3 | 62.97 | 58.6 | 72.9 | 83.75 |

| Agentic | BFCL-v4 | 39.22 | 49.7 | 45.2 | 31.7 |

| Agentic | τ² | 43.12 | 52.9 | 82.1 | 37.7 |

Scaling comparison against larger open-source reasoning models#

All numbers are run on the Zyphra evaluation harness. Models are ordered by total parameter count.

Quickstart#

Prerequisites#

We recommend installing the following libraries in a fresh python environment (tested with python 3.12).

To use ZAYA1-8B, install zaya1-pr branch from our fork of vllm library (the command will trigger a full build of vLLM from source):

pip install "vllm @ git+https://github.com/Zyphra/vllm.git@zaya1-pr"

If you want to run in transformers, install zaya1 branch from our fork of transformers library as well:

pip install "transformers @ git+https://github.com/Zyphra/transformers.git@zaya1"

Deployment#

To start vLLM server, run the following command:

vllm serve Zyphra/ZAYA1-8B --port 8010 \
   --mamba-cache-dtype float32 --dtype bfloat16 \
   --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser zaya_xml

For parallel deployment we recommend using DP with EP as TP for CCA is not supported in the branch above. If running on 8 GPUs, set extra flags -dp 8 -ep to run with DP=EP=8.

For our evaluations and for general use, we recommend temperature 1.0, top-p 0.95, top-k -1. For agent and code use cases, we recommend temperature 0.6, top-p 0.95, top-k -1.

Once the server is up, you can query a model with curl like in the following example:

curl http://localhost:8010/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "Zyphra/ZAYA1-8B",
        "messages": [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Hello. How is it going?"}
        ]
    }'