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.
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-pr branch from our fork of vllm library (the command will trigger a full build of vLLM from source):
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:
bash
1curl http://localhost:8010/v1/chat/completions \2 -H "Content-Type: application/json"\3 -d '{
4 "model": "Zyphra/ZAYA1-8B",
5 "messages": [
6 {"role": "system", "content": "You are a helpful assistant."},
7 {"role": "user", "content": "Hello. How is it going?"}
8 ]
9 }'
