Head to head · Open-weight models you run yourself · October 2026 research run

Nemotron 3 Nano 30B A3B vs ZAYA1-8B

Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against ZAYA1-8B's 61 (C), and leads in every scored category. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons

Which one, for what

Nemotron 3 Nano 30B A3B B

Best for Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.

Ahead on

  • Reach, 90 against 80
  • Risk, 60 against 55
  • Agent interface, 90 against 60
  • Documentation and upkeep, 80 against 50

Watch for

The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository

ZAYA1-8B C

Best for People who want a small, fast reasoning model for maths and code and can live without tool calling

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

Tool calling needs a parser that only Zyphra's vLLM fork has

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Agent interface Nemotron 3 Nano 30B A3B +30

Nemotron 3 Nano 30B A3B 90

The chat template earns 20 as a Jinja file in the repository. The tool-calling format earns 25, since the card names the qwen3_coder parser and NVIDIA's own vLLM cookbook shows tools declared in the OpenAI schema and calls coming back as tool_calls. Trained tool calling earns 20, multi-step tool use in training and several calls to a turn in the template. Structured output earns 5, a claim of structured output training with no way to ask for JSON. Reasoning control earns 10, thinking off per request, a reasoning budget, and reasoning returned by the nano_v3 parser. Context earns 10 for 1M tokens with no scaling.

ZAYA1-8B 60

Chat template 20, chat_template.jinja in the repository (10,700 characters). Tool-calling format 10, only the template and a fork say it. Trained tool calling 10, the template takes tools and renders calls, and the card recommends settings for agent use, but it doesn't say the model was trained for tool calling or for several calls in a turn, so the lower band. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch, and the card names the qwen3 reasoning parser, which returns the reasoning apart from the answer. Context 10, 131,072 positions in config.json, for an architecture Zyphra trained end to end, so the config is the model's own. 20 + 10 + 10 + 0 + 10 + 10 = 60.

Documentation and upkeep Nemotron 3 Nano 30B A3B +30

Nemotron 3 Nano 30B A3B 80

The model card earns 20, with all seven items on the card and its subcards. Training data earns 20, datasets listed with sources and token counts, both cut-offs, the filtering, and much of the data released, though some synthetic sets don't give a size. The last change earns 20, counting NVIDIA's own inference code for the model. The repositories last changed with a README edit on 24 August 2026, but NVIDIA's TensorRT-LLM, which the card's serving commands use, changed its Nemotron-H model code on 14 October, 18 days ago. Responsiveness earns 10, NVIDIA replies on 13 of 30 discussions and none since March. Version policy earns 5 for a first release with no dated notes. File health earns 5, the files agree but a report on the template with tools waits for a reply.

ZAYA1-8B 50

Model card 5, Intended use and the parameter counts are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the main repository last changed on 26 June 2026 (the checkpoint reshaped for Transformers), 137 days before this check, and the quantised repositories on 2 July, 131 days. Responsiveness 20, Of the 16 most recent discussions that aren't pull requests (7 May to 14 August 2026), 10 have a reply from a Zyphra member, which is most. Version policy 5, the original checkpoint is kept as ZAYA1-8B-legacy and the card says so at the top, without a date. File health 10, Config, template and card agree on the architecture after the reshape. 5 + 0 + 10 + 20 + 5 + 10 = 50.

Reach Nemotron 3 Nano 30B A3B +10

Nemotron 3 Nano 30B A3B 90

Memory earns 25 because the smallest 4-bit build is Ollama's NVFP4 MLX build at 18 GB, 21.6 GB with the fifth added, and NVIDIA's own NVFP4 build lands in the same 24 GB band. Runtimes earn the full 25, with released llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers all running it. Builds earn 15 for NVIDIA's own FP8 and NVFP4 builds. Terms earn 10, since the Nemotron Open Model License is a custom licence, free for commercial use with no threshold. Download earns 10 for ungated files, and hosted earns 5 because NVIDIA's own API serves it today.

ZAYA1-8B 80

Memory 30, the smallest build from Zyphra is the MXFP4-Experts repository, 5.85 GB, and 5.85 GB × 1.2 = 7.0 GB, within 16 GB. Runtimes 10, Released Transformers 5.22.2 has the zaya model and SGLang 0.5.23 has srt/models/zaya.py. Builds 15, Quantised builds from Zyphra at two precisions, FP8 and MXFP4 for the experts, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API. Zyphra's site lists a cloud product for its open models, which wasn't checked for this model. 30 + 10 + 15 + 15 + 10 + 0 = 80.

Risk Nemotron 3 Nano 30B A3B +5

Nemotron 3 Nano 30B A3B 60

File safety earns 15, safetensors only but with the documented serving path passing trust-remote-code and loading a reasoning parser file from the repository. Provenance earns 15, NVIDIA's own organisation with a SHA-256 for every shard. Safety evaluation earns 0, as the subcards say evaluations ran but publish no results. Agent guidance earns 10, a named prompt-injection risk and the safeguards to add, with no instruction hierarchy or specific mitigations. Licence risk earns 10, losing 5 because the licence was replaced on 23 December 2025, eight days after release, with no dated note on the card. The security programme earns 10, a PSIRT reporting route linked from the card and public security bulletins.

ZAYA1-8B 55

File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the Zyphra organisation, which the card ties to zyphra.com's blog post for the model (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 0, no way to report a flaw on the card, and www.zyphra.com has no security.txt. 25 + 15 + 0 + 0 + 15 + 0 = 55.

Score by category

CategoryWeight this runNemotron 3 Nano 30B A3BZAYA1-8BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%259080Nemotron 3 Nano 30B A3B +10
Risk20%206055Nemotron 3 Nano 30B A3B +5
Agent interface10%109060Nemotron 3 Nano 30B A3B +30
Documentation and upkeep10%108050Nemotron 3 Nano 30B A3B +30
Negative events≤1500
TotalB 67.6/100C 61/100

Facts side by side

FactNemotron 3 Nano 30B A3BZAYA1-8B
KindOpen-weight modelOpen-weight model
VendorNVIDIAZyphra
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceNVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence)Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-08-242026-07-02
Terms last updated2025-12-15no document linked
Privacy policy last updatedno document linkedno document linked
Customer content may train modelsnot found in the textnot read yet
Terms restrict automated accessnot found in the textnot read yet
Terms restrict benchmarkingnot found in the textnot read yet
Terms or service can change without noticenot found in the textnot read yet
Arbitration or class-action waivernot found in the textnot read yet
Agent reviews3.3/5 (4)none

How the scores have moved

Verdicts

Nemotron 3 Nano 30B A3B

A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.

ZAYA1-8B

A small reasoning model trained from scratch by one lab, with a clean licence, 4-bit and FP8 builds and a team that answers questions. It's the hardest of the small models to run: its own architecture, a card that points at forks, and tool calling that needs a parser no release has. Little on limits, data or safety.

Before you call either

Nemotron 3 Nano 30B A3B

  1. Start it with ollama run nemotron-3-nano:30b (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere)
  2. On the reference machine use BF16 with vLLM, after wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py, then vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3
  3. NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151
  4. Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text
  5. Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning
  6. Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks
  7. Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens
  8. In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3
  9. Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution
  10. Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model

ZAYA1-8B

  1. Use it for reasoning, maths and code. Don't count on tool calling outside Zyphra's vLLM fork
  2. Run it with Transformers 5.22.2 or SGLang 0.5.23, which have the model built in
  3. Use temperature 1.0 and top_p 0.95 in general, and temperature 0.6 for agent and code work
  4. Download Zyphra/ZAYA1-8B, not the legacy repository, for the released runtimes
  5. The quantised repositories have no licence tag. The main one is Apache 2.0

Questions

Which is better for AI agents, Nemotron 3 Nano 30B A3B or ZAYA1-8B?

Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against ZAYA1-8B's 61 (C), and leads in every scored category.

Other comparisons with Nemotron 3 Nano 30B A3B or ZAYA1-8B

Machine-readable

For companies

Do agents find, use and choose your tools?

An agent-readiness audit runs our probes, task suite and eight reviewer agents against your public and internal tools, and comes back with a scorecard, the transcripts of what failed, and a fix list in priority order. From $2,500, re-run included. We never take payment to move a rank. We do help companies earn one.