Head to head · Open-weight models you run yourself · October 2026 research run
Nanbeige4.3-33B vs ZAYA1-8B
ZAYA1-8B and Nanbeige4.3-33B score within a point of each other on agent readiness, 61 (C) and 60.8 (C). Nanbeige4.3-33B leads on risk, agent interface and documentation and upkeep. 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
Best for Agents with a 32 GB GPU (INT4) or more, or the 128 GB reference machine at BF16, that want an Apache-tagged 33B model for long coding and tool loops.
Ahead on
- Risk, 60 against 55
- Agent interface, 85 against 60
- Documentation and upkeep, 56 against 50
Watch for
Independent results so far are Artificial Analysis's alone (Intelligence Index v5.0 20.4, Terminal-Bench 4.0 29.8 per cent); the card's other agentic numbers are Nanbeige's own runs
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 Nanbeige4.3-33B +25
Nanbeige4.3-33B 85
The chat template earns 20, Jinja in tokenizer_config.json. The tool-calling format earns 25, with the XML call format in the template and the qwen3_coder parser named for vLLM and SGLang. Trained tool calling earns 20, several calls per turn in the template. Structured output earns 0, as nothing mentions it. Reasoning control earns 10, thinking switched per request with the qwen3 reasoning parser named. Context earns 10 for 512K set in the config 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 Nanbeige4.3-33B +6
Nanbeige4.3-33B 56
The model card earns 11, with intended use, parameters, context and languages but no limitations, architecture or cut-off. Training data earns 0, as nothing describes it. Last change earns 20 for the quantised builds of 31 October 2026. Responsiveness earns 10, a Nanbeige member answering nine of 30 discussions. Version policy earns 5 for a first release, with a config change made and reverted under bare commit titles. File health earns 10, the files and the card agreeing.
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.
Risk Nanbeige4.3-33B +5
Nanbeige4.3-33B 60
File safety earns 25, safetensors only and no repository code, so the card's trust_remote_code has nothing to run. Provenance earns 15, the lab's own organisation as its arXiv report links it, with a hash for every file. Safety evaluation earns 0 and agent guidance 0, since neither card says anything on either. Licence risk keeps all 15 under Apache 2.0. The security programme earns 5 for BOSS Zhipin's security response centre, which isn't linked to the model.
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
| Category | Weight this run | Nanbeige4.3-33B | ZAYA1-8B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 80 | 80 | even |
| Risk | 20%20 | 60 | 55 | Nanbeige4.3-33B +5 |
| Agent interface | 10%10 | 85 | 60 | Nanbeige4.3-33B +25 |
| Documentation and upkeep | 10%10 | 56 | 50 | Nanbeige4.3-33B +6 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 60.8/100 | C 61/100 |
Facts side by side
| Fact | Nanbeige4.3-33B | ZAYA1-8B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Nanbeige LLM Lab (BOSS Zhipin) | Zyphra |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 (Hub metadata only; the repository has no LICENSE file) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-31 | 2026-07-02 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
How the scores have moved
ZAYA1-8B
No score change since it was listed.
Verdicts
Nanbeige4.3-33B
A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. Nanbeige's INT4 build needs about 27 GB. Artificial Analysis's first independent runs give it 20.4 on its Intelligence Index, and there are no safety results, training data notes or licence file.
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
Nanbeige4.3-33B
- Serve with
vllm serve Nanbeige/Nanbeige4.3-33B --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder, as the card says, or SGLang with the same two parsers - On a 32 GB GPU use Nanbeige/Nanbeige4.3-33B-INT4 with the same vLLM or SGLang flags; the INT8 and FP8 builds need about 43 GB
- Keep
tool_call_formatat its defaultxml, since qwen3_coder parses that format; thejsonoption writes Hermes-style JSON calls, which need a different parser such as vLLM's hermes - Pass
chat_template_kwargs{"preserve_thinking": true}for multi-turn tool use and coding, and{"enable_thinking": false}to skip reasoning, as the card recommends - Use temperature 1.0, top_p 0.95 and up to 65,536 new tokens for agent work, and temperature 0.6 for reasoning and chat, per the card
- Send your own system prompt, since without one the template injects a Chinese identity line, or a Chinese tool-expert prompt when tools are passed
- Send one system message at the start only, as the template raises an error for a later one
- In plain Transformers pass
use_cache=Trueto generate, since config.json and generation_config.json set it false; trust_remote_code isn't needed
ZAYA1-8B
- Use it for reasoning, maths and code. Don't count on tool calling outside Zyphra's vLLM fork
- Run it with Transformers 5.22.2 or SGLang 0.5.23, which have the model built in
- Use temperature 1.0 and top_p 0.95 in general, and temperature 0.6 for agent and code work
- Download
Zyphra/ZAYA1-8B, not the legacy repository, for the released runtimes - The quantised repositories have no licence tag. The main one is Apache 2.0
Questions
Which is better for AI agents, Nanbeige4.3-33B or ZAYA1-8B?
ZAYA1-8B and Nanbeige4.3-33B score within a point of each other on agent readiness, 61 (C) and 60.8 (C). Nanbeige4.3-33B leads on risk, agent interface and documentation and upkeep.
Other comparisons with Nanbeige4.3-33B or ZAYA1-8B
- Agents-A1-4B vs Nanbeige4.3-33B
- Agents-A1-4B vs ZAYA1-8B
- Apertus v1.5 8B vs Nanbeige4.3-33B
- Apertus v1.5 8B vs ZAYA1-8B
- Apodex-1.1-mini vs Nanbeige4.3-33B
- Apodex-1.1-mini vs ZAYA1-8B
- Arete-27B vs Nanbeige4.3-33B
- Arete-27B vs ZAYA1-8B
- BigBang-v1 vs Nanbeige4.3-33B
- BigBang-v1 vs ZAYA1-8B
- Carbon-X vs Nanbeige4.3-33B
- Carbon-X vs ZAYA1-8B
- Cola2 vs Nanbeige4.3-33B
- Cola2 vs ZAYA1-8B
- Cosmos-Reason2-2B vs Nanbeige4.3-33B
- Cosmos-Reason2-2B vs ZAYA1-8B
- Darwin-36B-Opus vs Nanbeige4.3-33B
- Darwin-36B-Opus vs ZAYA1-8B
- DeepSeek-V3.2 vs Nanbeige4.3-33B
- DeepSeek-V3.2 vs ZAYA1-8B
- DeepSeek V4.1 Flash vs Nanbeige4.3-33B
- DeepSeek V4.1 Flash vs ZAYA1-8B
- DeepSeek-V4-Flash-0731 vs Nanbeige4.3-33B
- DeepSeek-V4-Flash-0731 vs ZAYA1-8B
- Devstral Small 2 vs Nanbeige4.3-33B
- Devstral Small 2 vs ZAYA1-8B
- DiffusionGemma 26B A4B IT vs Nanbeige4.3-33B
- DiffusionGemma 26B A4B IT vs ZAYA1-8B
- EXAONE 4.5 33B vs Nanbeige4.3-33B
- EXAONE 4.5 33B vs ZAYA1-8B
- Gemma 4 12B IT vs Nanbeige4.3-33B
- Gemma 4 12B IT vs ZAYA1-8B
- Gemma 4 26B A4B IT vs Nanbeige4.3-33B
- Gemma 4 26B A4B IT vs ZAYA1-8B
- Gemma 4 31B IT vs Nanbeige4.3-33B
- Gemma 4 31B IT vs ZAYA1-8B
- Gemma 4 E2B IT vs Nanbeige4.3-33B
- Gemma 4 E2B IT vs ZAYA1-8B
- Gemma 4 E4B IT vs Nanbeige4.3-33B
- Gemma 4 E4B IT vs ZAYA1-8B
- GLM-4.5-Air vs Nanbeige4.3-33B
- GLM-4.5-Air vs ZAYA1-8B
- GLM-4.7-Flash vs Nanbeige4.3-33B
- GLM-4.7-Flash vs ZAYA1-8B
- GLM-5.3-Flash vs Nanbeige4.3-33B
- GLM-5.3-Flash vs ZAYA1-8B
- GLM-5.3 vs Nanbeige4.3-33B
- GLM-5.3 vs ZAYA1-8B
- gpt-oss-120b vs Nanbeige4.3-33B
- gpt-oss-120b vs ZAYA1-8B
- gpt-oss-20b vs Nanbeige4.3-33B
- gpt-oss-20b vs ZAYA1-8B
- Granite 4.2 30B vs Nanbeige4.3-33B
- Granite 4.2 30B vs ZAYA1-8B
- Granite 4.2 3B vs Nanbeige4.3-33B
- Granite 4.2 3B vs ZAYA1-8B
- Granite 4.2 8B vs Nanbeige4.3-33B
- Granite 4.2 8B vs ZAYA1-8B
- Hy-Memory-30B-A3B vs Nanbeige4.3-33B
- Hy-Memory-30B-A3B vs ZAYA1-8B
- Hy3 vs Nanbeige4.3-33B
- Hy3 vs ZAYA1-8B
- Inkling-Small vs Nanbeige4.3-33B
- Inkling-Small vs ZAYA1-8B
- Inkling vs Nanbeige4.3-33B
- Inkling vs ZAYA1-8B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nanbeige4.3-33B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs ZAYA1-8B
- Kimi K3.1 Pro vs Nanbeige4.3-33B
- Kimi K3.1 Pro vs ZAYA1-8B
- Kimi K3 vs Nanbeige4.3-33B
- Kimi K3 vs ZAYA1-8B
- Kolibri-1 vs Nanbeige4.3-33B
- Kolibri-1 vs ZAYA1-8B
- Kronos vs Nanbeige4.3-33B
- Kronos vs ZAYA1-8B
- Laguna M.1 vs Nanbeige4.3-33B
- Laguna M.1 vs ZAYA1-8B
- Laguna S 2.1 vs Nanbeige4.3-33B
- Laguna S 2.1 vs ZAYA1-8B
- Laguna XS 2.1 vs Nanbeige4.3-33B
- Laguna XS 2.1 vs ZAYA1-8B
- LBS-27B vs Nanbeige4.3-33B
- LBS-27B vs ZAYA1-8B
- LFM2.5-2.6B vs Nanbeige4.3-33B
- LFM2.5-2.6B vs ZAYA1-8B
- LFM3-1.7B vs Nanbeige4.3-33B
- LFM3-1.7B vs ZAYA1-8B
- LFM3-Audio-2B vs Nanbeige4.3-33B
- LFM3-Audio-2B vs ZAYA1-8B
- LFM3-Omni-2B vs Nanbeige4.3-33B
- LFM3-Omni-2B vs ZAYA1-8B
- LFM3-VL-2B vs Nanbeige4.3-33B
- LFM3-VL-2B vs ZAYA1-8B
- Ling-3.0-tiny vs Nanbeige4.3-33B
- Ling-3.0-tiny vs ZAYA1-8B
- Ling-3.1-flash-1019 vs Nanbeige4.3-33B
- Ling-3.1-flash-1019 vs ZAYA1-8B
- LongCat-2.5 vs Nanbeige4.3-33B
- LongCat-2.5 vs ZAYA1-8B
- MiMo-V2.6 Distill Qwen 9B vs Nanbeige4.3-33B
- MiMo-V2.6 Distill Qwen 9B vs ZAYA1-8B
- MiMo-V2.6-Flash-RL vs Nanbeige4.3-33B
- MiMo-V2.6-Flash-RL vs ZAYA1-8B
- MiniCPM-o 4.5 vs Nanbeige4.3-33B
- MiniCPM-o 4.5 vs ZAYA1-8B
- MiniCPM-V 4.7 1B vs Nanbeige4.3-33B
- MiniCPM-V 4.7 1B vs ZAYA1-8B
- MiniCPM-V 4.7 35B-A3B vs Nanbeige4.3-33B
- MiniCPM-V 4.7 35B-A3B vs ZAYA1-8B
- MiniCPM5-2B vs Nanbeige4.3-33B
- MiniCPM5-2B vs ZAYA1-8B
- MiniMax-M2.5 vs Nanbeige4.3-33B
- MiniMax-M2.5 vs ZAYA1-8B
- MiniMax-M2.7 vs Nanbeige4.3-33B
- MiniMax-M2.7 vs ZAYA1-8B
- MiniMax-M3.1-Flash vs Nanbeige4.3-33B
- MiniMax-M3.1-Flash vs ZAYA1-8B
- Mistral Large 4 vs Nanbeige4.3-33B
- Mistral Large 4 vs ZAYA1-8B
- Muse Glimmer 30B vs Nanbeige4.3-33B
- Muse Glimmer 30B vs ZAYA1-8B
- Nanbeige4.2-3B vs Nanbeige4.3-33B
- Nanbeige4.2-3B vs ZAYA1-8B
- Nanbeige4.3-33B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Nanbeige4.3-33B vs Nemotron 3.5 Lightning 30B A3B
- Nanbeige4.3-33B vs Nemotron 3 Nano 30B A3B
- Nanbeige4.3-33B vs Nemotron 3 Nano 4B
- Nanbeige4.3-33B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Nanbeige4.3-33B vs Nemotron 3 Super 120B A12B
- Nanbeige4.3-33B vs Nemotron 3 Ultra 550B A55B
- Nanbeige4.3-33B vs North Mini Code 1.0
- Nanbeige4.3-33B vs occamy-1.0
- Nanbeige4.3-33B vs Olmo 3 7B Think
- Nanbeige4.3-33B vs Ornith-1.5-122B
- Nanbeige4.3-33B vs Ornith-1.5-35B-A3B
- Nanbeige4.3-33B vs Ornith-1.5-397B
- Nanbeige4.3-33B vs Ornith-1.5-9B
- Nanbeige4.3-33B vs OTel-2.0-LLM-31B-IT
- Nanbeige4.3-33B vs Qwen-AgentWorld-35B-A3B
- Nanbeige4.3-33B vs Qwen3-30B-A3B-Instruct-2507
- Nanbeige4.3-33B vs Qwen3-4B-Instruct-2507
- Nanbeige4.3-33B vs Qwen3.5-0.8B
- Nanbeige4.3-33B vs Qwen3.5-122B-A10B
- Nanbeige4.3-33B vs Qwen3.5-2B
- Nanbeige4.3-33B vs Qwen3.5-397B-A17B
- Nanbeige4.3-33B vs Qwen3.5-4B
- Nanbeige4.3-33B vs Qwen3.5-9B
- Nanbeige4.3-33B vs Qwen3.6-27B
- Nanbeige4.3-33B vs Qwen3.6-35B-A3B
- Nanbeige4.3-33B vs Qwen3.8-27B
- Nanbeige4.3-33B vs Qwen3.8-Flash-Next
- Nanbeige4.3-33B vs Qwen3-Coder-30B-A3B-Instruct
- Nanbeige4.3-33B vs Qwen3-Coder-Next
- Nanbeige4.3-33B vs Qwen3-Omni-30B-A3B-Instruct
- Nanbeige4.3-33B vs Qwen3-VL-2B-Instruct
- Nanbeige4.3-33B vs Qwen3-VL-30B-A3B-Instruct
- Nanbeige4.3-33B vs Qwen3-VL-32B-Instruct
- Nanbeige4.3-33B vs Qwen3-VL-4B-Instruct
- Nanbeige4.3-33B vs Qwen3-VL-8B-Instruct
- Nanbeige4.3-33B vs Qwopus3.8-27B-Flash-V2
- Nanbeige4.3-33B vs Recursive Flint
- Nanbeige4.3-33B vs Ripple-65B Preview
- Nanbeige4.3-33B vs SenseNova-Loop-9B-Preview
- Nanbeige4.3-33B vs SmolLM3-3B
- Nanbeige4.3-33B vs Spark-X2.6-4B
- Nanbeige4.3-33B vs Step 3.7 Flash
- Nanbeige4.3-33B vs Swift 1.5 Qwen3.8-27B
- Nanbeige4.3-33B vs Swift 1.5 Qwen3.8-Flash-Next
- Nanbeige4.3-33B vs ThinkingCap (Qwen3.8 27B)
- Nanbeige4.3-33B vs Baidu Unlimited-OCR-2
- Nanbeige4.3-33B vs WeVisOne-VL-2B
- Nanbeige4.3-33B vs WeVisOne-VL-4B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs ZAYA1-8B
- Nemotron 3.5 Lightning 30B A3B vs ZAYA1-8B
- Nemotron 3 Nano 30B A3B vs ZAYA1-8B
- Nemotron 3 Nano 4B vs ZAYA1-8B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs ZAYA1-8B
- Nemotron 3 Super 120B A12B vs ZAYA1-8B
- Nemotron 3 Ultra 550B A55B vs ZAYA1-8B
- North Mini Code 1.0 vs ZAYA1-8B
- occamy-1.0 vs ZAYA1-8B
- Olmo 3 7B Think vs ZAYA1-8B
- Ornith-1.5-122B vs ZAYA1-8B
- Ornith-1.5-35B-A3B vs ZAYA1-8B
- Ornith-1.5-397B vs ZAYA1-8B
- Ornith-1.5-9B vs ZAYA1-8B
- OTel-2.0-LLM-31B-IT vs ZAYA1-8B
- Qwen-AgentWorld-35B-A3B vs ZAYA1-8B
- Qwen3-30B-A3B-Instruct-2507 vs ZAYA1-8B
- Qwen3-4B-Instruct-2507 vs ZAYA1-8B
- Qwen3.5-0.8B vs ZAYA1-8B
- Qwen3.5-122B-A10B vs ZAYA1-8B
- Qwen3.5-2B vs ZAYA1-8B
- Qwen3.5-397B-A17B vs ZAYA1-8B
- Qwen3.5-4B vs ZAYA1-8B
- Qwen3.5-9B vs ZAYA1-8B
- Qwen3.6-27B vs ZAYA1-8B
- Qwen3.6-35B-A3B vs ZAYA1-8B
- Qwen3.8-27B vs ZAYA1-8B
- Qwen3.8-Flash-Next vs ZAYA1-8B
- Qwen3-Coder-30B-A3B-Instruct vs ZAYA1-8B
- Qwen3-Coder-Next vs ZAYA1-8B
- Qwen3-Omni-30B-A3B-Instruct vs ZAYA1-8B
- Qwen3-VL-2B-Instruct vs ZAYA1-8B
- Qwen3-VL-30B-A3B-Instruct vs ZAYA1-8B
- Qwen3-VL-32B-Instruct vs ZAYA1-8B
- Qwen3-VL-4B-Instruct vs ZAYA1-8B
- Qwen3-VL-8B-Instruct vs ZAYA1-8B
- Qwopus3.8-27B-Flash-V2 vs ZAYA1-8B
- Recursive Flint vs ZAYA1-8B
- Ripple-65B Preview vs ZAYA1-8B
- SenseNova-Loop-9B-Preview vs ZAYA1-8B
- SmolLM3-3B vs ZAYA1-8B
- Spark-X2.6-4B vs ZAYA1-8B
- Step 3.7 Flash vs ZAYA1-8B
- Swift 1.5 Qwen3.8-27B vs ZAYA1-8B
- Swift 1.5 Qwen3.8-Flash-Next vs ZAYA1-8B
- ThinkingCap (Qwen3.8 27B) vs ZAYA1-8B
- Baidu Unlimited-OCR-2 vs ZAYA1-8B
- WeVisOne-VL-2B vs ZAYA1-8B
- WeVisOne-VL-4B vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/nanbeige-4-3-33b-vs-zaya1-8b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/nanbeige-4-3-33b.json·/api/v1/tools/zaya1-8b.json - From a terminal
anchor compare nanbeige-4-3-33b zaya1-8b(the CLI) - Over MCP
compare_tools {"a": "nanbeige-4-3-33b", "b": "zaya1-8b"}at/mcp, no key