ZAYA1-8B

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.

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

Model	Active	Total	AIME'26	HMMT'26	LCB-v6	IFEval	GPQA-D	MMLU-Pro
ZAYA1-8B	0.7B	8B	89.1	71.6	63.8	85.8	71.0	74.2
Arcee-Trinity-Mini	3B	26B	59.6	36.9	33.3	62.0	46.8	70.6
N3-Nano-30B	3B	30B	90.1	75.5	64.6	92.8	75.1	78.9
OLMo-3.1-32B-Think	32B	32B	78.9	50.6	58.3	93.2	59.6	75.8
Qwen3-Next-80B-A3B-Think	3B	80B	90.2	79.3	67.8	88.5	76.7	82.6
Intellect-3	12B	106B	86.3	72.2	66.8	81.2	74.6	82.3
Mistral-Small-4-119B	6B	119B	86.4	70.6	57.9	84.0	77.2	81.6

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 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"

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.

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?"}
        ]
    }'