# Qwen3-30B-A3B-Instruct-2507 vs ZAYA1-8B > ZAYA1-8B scores 61 (C) to Qwen3-30B-A3B-Instruct-2507's 56.6 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-zaya1-8b - Markdown: https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-zaya1-8b.md (~9,750 tokens) - Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - DNS: dig +short TXT .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. under the zone, and the same under grade.letme.dev) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-11-10 ZAYA1-8B scores 61 (C) on agent readiness against Qwen3-30B-A3B-Instruct-2507's 56.6 (C), and leads in 1 of 4 scored categories. Qwen3-30B-A3B-Instruct-2507 leads on reach and agent interface. Both do open-weight models you run yourself. - Qwen3-30B-A3B-Instruct-2507: grade C, 56.6/100, rank #113 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-30b-a3b-instruct-2507.json - ZAYA1-8B: grade C, 61/100, rank #85 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/zaya1-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/zaya1-8b.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### Qwen3-30B-A3B-Instruct-2507 (C) Good for: Agents that want an instruct model with tool calling on one 24 GB GPU or a 32 GB Mac, with a plain licence and a 262,144-token context. Ahead on: - Reach, 90 against 80 - Agent interface, 75 against 60 Watch for: No safety results, prompt-injection guidance or security contact ### ZAYA1-8B (C) Good for: People who want a small, fast reasoning model for maths and code and can live without tool calling Ahead on: - Documentation and upkeep, 50 against 26 Watch for: Tool calling needs a parser that only Zyphra's vLLM fork has ## Why the edge ### Documentation and upkeep: ZAYA1-8B +24 - Qwen3-30B-A3B-Instruct-2507 (26): Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, named languages and a knowledge cut-off aren't. Training data 5, the cited Qwen3 report describes the data in general terms without a cut-off. Last change 0, the files and card last changed on 17 September 2025 (Hub lastModified on 4 November), and QwenLM/Qwen3's commits since are documentation only (the newest 8 January 2026). Responsiveness 0, counted by script on 4 November, with 32 discussions, 23 not pull requests (the newest 18 June 2026), and the only reply from a Qwen organisation member is Haihao's on discussion 3 (30 July 2025), who isn't counted as the publisher's staff (the ehartford and danielhanchen reading). Version policy 5, the older Qwen3-30B-A3B stays in its own repository without a note. File health 5, the files agree with the card but reports about the 1M mode wait without a reply. 11 + 5 + 0 + 0 + 5 + 5 = 26. - 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. ### Agent interface: Qwen3-30B-A3B-Instruct-2507 +15 - Qwen3-30B-A3B-Instruct-2507 (75): Chat template 20, a Jinja template in tokenizer_config.json. Tool-calling format 25, Qwen's own Function Calling guide documents the Hermes-style declaration and how calls come back, and names vLLM's hermes parser. Trained tool calling 20, the template allows one or more calls a turn. Structured output 0, not documented for the model, and Qwen's docs only point to vLLM's generic guided decoding. Reasoning control 0, this edition has no thinking. Context 10, 262,144 tokens native, with the 1M mode not counted. Template and config unchanged on 4 November. 20 + 25 + 20 + 0 + 0 + 10 = 75. - 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. ### Reach: Qwen3-30B-A3B-Instruct-2507 +10 - Qwen3-30B-A3B-Instruct-2507 (90): Memory 25 of 30, LM Studio's MLX 4-bit build is 17.2 GB and Ollama's 4-bit build 19 GB, 21 to 23 GB with a fifth added, which fits 24 GB. Runtimes 25, released Transformers, vLLM, SGLang and llama.cpp run the qwen3_moe architecture, and Ollama's library and LM Studio's catalogue carry it. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 15, Apache 2.0 with nothing added. Download 10, neither repository is gated. Hosted 5, Featherless AI on Hugging Face plus DekaLLM and SiliconFlow on OpenRouter serve it. On 4 November both repositories were still ungated with no file changed since 17 September 2025, and Featherless AI was still live in the Hub's provider mapping (OpenRouter's providers per 21 October). 25 + 25 + 10 + 15 + 10 + 5 = 90. - 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. ## Score by category | Category | Weight | Qwen3-30B-A3B-Instruct-2507 | ZAYA1-8B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 90 | 80 | Qwen3-30B-A3B-Instruct-2507 +10 | | Risk | 20% (20 this run) | 55 | 55 | even | | Agent interface | 10% (10 this run) | 75 | 60 | Qwen3-30B-A3B-Instruct-2507 +15 | | Documentation and upkeep | 10% (10 this run) | 26 | 50 | ZAYA1-8B +24 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **56.6 · C** | **61 · C** | | ## Facts side by side | Fact | Qwen3-30B-A3B-Instruct-2507 | ZAYA1-8B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Alibaba (Qwen team) | Zyphra | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2025-09-17 | 2026-07-02 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | 2.8/5 (4) | none | ## Verdicts **Qwen3-30B-A3B-Instruct-2507.** A general-purpose instruct model with 3.3B of 30.5B parameters active per token, for instruction following, tool calling and long documents without a thinking step. It needs about 21 to 23 GB at 4 bits under Apache 2.0, and providers host it. Its files haven't changed in over a year, Alibaba hasn't answered its discussions, and no safety results are published. **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 ### Qwen3-30B-A3B-Instruct-2507 1. Quickest start is `ollama run qwen3:30b-instruct`, 19 GB. Plain `qwen3:30b` and `qwen3:30b-a3b` are the Thinking-2507 edition, not this one 2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3-30B-A3B-Instruct-2507 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser hermes`, 61 GB of weights fits the GPU share 3. Use the hermes parser in vLLM or qwen25 in SGLang, the template emits JSON inside tool_call tags 4. Don't pass enable_thinking, this edition never thinks and the card says the flag isn't needed 5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and min_p 0, with presence_penalty up to 2 against repetition 6. Leave config.json as it is unless you need more than 262,144 tokens, the 1M mode swaps in config_1m.json and runs one sequence at a time 7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented ### 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, Qwen3-30B-A3B-Instruct-2507 or ZAYA1-8B? ZAYA1-8B scores 61 (C) on agent readiness against Qwen3-30B-A3B-Instruct-2507's 56.6 (C), and leads in 1 of 4 scored categories. Qwen3-30B-A3B-Instruct-2507 leads on reach and agent interface. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-zaya1-8b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-zaya1-8b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-30b-a3b-instruct-2507", "b": "zaya1-8b"}`. 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[Qwen3-30B-A3B-Instruct-2507 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-spark-x2-6-4b.md) - [Qwen3-30B-A3B-Instruct-2507 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-step-3-7-flash.md) - [Qwen3-30B-A3B-Instruct-2507 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-swift-1-5-qwen3-8-27b.md) - [Qwen3-30B-A3B-Instruct-2507 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-swift-1-5-qwen3-8-flash-next.md) - [Qwen3-30B-A3B-Instruct-2507 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-thinkingcap-qwen3-8-27b.md) - [Qwen3-30B-A3B-Instruct-2507 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-unlimited-ocr-2.md) - [Qwen3-30B-A3B-Instruct-2507 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-wevisone-vl-2b.md) - [Qwen3-30B-A3B-Instruct-2507 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-wevisone-vl-4b.md) - [Qwen3-4B-Instruct-2507 vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-zaya1-8b.md) - [Qwen3.5-0.8B vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-zaya1-8b.md) - [Qwen3.5-122B-A10B vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-zaya1-8b.md) - [Qwen3.5-2B vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-zaya1-8b.md) - [Qwen3.5-397B-A17B vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-zaya1-8b.md) - [Qwen3.5-4B vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-zaya1-8b.md) - [Qwen3.5-9B vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-zaya1-8b.md) - [Qwen3.6-27B vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-zaya1-8b.md) - [Qwen3.6-35B-A3B vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-zaya1-8b.md) - [Qwen3.8-27B vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-zaya1-8b.md) - [Qwen3.8-Flash-Next vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-zaya1-8b.md) - [Qwen3-Coder-30B-A3B-Instruct vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-zaya1-8b.md) - [Qwen3-Coder-Next vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-coder-next-vs-zaya1-8b.md) - [Qwen3-Omni-30B-A3B-Instruct vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-zaya1-8b.md) - [Qwen3-VL-2B-Instruct vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-zaya1-8b.md) - [Qwen3-VL-30B-A3B-Instruct vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-zaya1-8b.md) - [Qwen3-VL-32B-Instruct vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-zaya1-8b.md) - [Qwen3-VL-4B-Instruct vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-zaya1-8b.md) - [Qwen3-VL-8B-Instruct vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-zaya1-8b.md) - [Qwopus3.8-27B-Flash-V2 vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-zaya1-8b.md) - [Recursive Flint vs ZAYA1-8B](https://www.anchorterminal.com/compare/recursive-flint-vs-zaya1-8b.md) - [Ripple-65B Preview vs ZAYA1-8B](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-zaya1-8b.md) - [SenseNova-Loop-9B-Preview vs ZAYA1-8B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b.md) - [SmolLM3-3B vs ZAYA1-8B](https://www.anchorterminal.com/compare/smollm3-3b-vs-zaya1-8b.md) - [Spark-X2.6-4B vs ZAYA1-8B](https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-zaya1-8b.md) - [Step 3.7 Flash vs ZAYA1-8B](https://www.anchorterminal.com/compare/step-3-7-flash-vs-zaya1-8b.md) - [Swift 1.5 Qwen3.8-27B vs ZAYA1-8B](https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-zaya1-8b.md) - [Swift 1.5 Qwen3.8-Flash-Next vs ZAYA1-8B](https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-zaya1-8b.md) - [ThinkingCap (Qwen3.8 27B) vs ZAYA1-8B](https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-zaya1-8b.md) - [Baidu Unlimited-OCR-2 vs ZAYA1-8B](https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-zaya1-8b.md) - [WeVisOne-VL-2B vs ZAYA1-8B](https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-zaya1-8b.md) - [WeVisOne-VL-4B vs ZAYA1-8B](https://www.anchorterminal.com/compare/wevisone-vl-4b-vs-zaya1-8b.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp