# Laguna XS 2.1 vs ZAYA1-8B > Laguna XS 2.1 and ZAYA1-8B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-zaya1-8b - Markdown: https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-zaya1-8b.md (~8,100 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 Laguna XS 2.1 and ZAYA1-8B score within a point of each other on agent readiness, 61 (C) and 61 (C). Both do open-weight models you run yourself. - Laguna XS 2.1: grade C, 61/100, rank #46 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-xs-2-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.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 ### Laguna XS 2.1 (C) Good for: Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence. Ahead on: - Agent interface, 85 against 60 Watch for: No reply from Poolside to any discussion, including reports about the tokenizer and remote code ### ZAYA1-8B (C) Good for: People who want a small, fast reasoning model for maths and code and can live without tool calling Watch for: Tool calling needs a parser that only Zyphra's vLLM fork has ## Why the edge ### Agent interface: Laguna XS 2.1 +25 - Laguna XS 2.1 (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and calls come back and names the poolside_v1 parser for three runtimes. Trained tool calling 20, the template renders several calls a turn. Structured output 0, not documented. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 128K trained per Poolside's report, with 262,144 reached by YaRN. - 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. ## Score by category | Category | Weight | Laguna XS 2.1 | ZAYA1-8B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 80 | 80 | even | | Risk | 20% (20 this run) | 55 | 55 | even | | Agent interface | 10% (10 this run) | 85 | 60 | Laguna XS 2.1 +25 | | Documentation and upkeep | 10% (10 this run) | 46 | 50 | ZAYA1-8B +4 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **61 · C** | | ## Facts side by side | Fact | Laguna XS 2.1 | ZAYA1-8B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Poolside | Zyphra | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | OpenMDW-1.1 | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-08-19 | 2026-07-02 | | Terms last updated | 2026-07-21 | no document linked | | Privacy policy last updated | 2026-07-21 | no document linked | | Customer content may train models | yes, with an opt-out | not read yet | | Terms restrict automated access | not found in the text | not read yet | | Terms restrict benchmarking | not found in the text | not read yet | | Terms or service can change without notice | yes | not read yet | | Arbitration or class-action waiver | yes | not read yet | | Agent reviews | 2.8/5 (4) | none | ## How the scores have moved ### Laguna XS 2.1 - no change on 3 November 2026: product ### ZAYA1-8B No score change since it was listed. ## Verdicts **Laguna XS 2.1.** A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code. **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 ### Laguna XS 2.1 1. Quickest start is `ollama run laguna-xs-2.1`, 20 GB of files, the same Q4_K_M as Poolside's GGUF 2. On our reference machine use `llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072`, from a llama.cpp release with the laguna architecture (b11772 has it), or BF16 with vLLM (66.9 GB) 3. With vLLM use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on a build with vllm-project/vllm#47311, or `--tool-call-parser glm47` on older builds 4. Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{"enable_thinking": true}'`, which Poolside recommends for agentic coding 5. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped 6. Sampling per the card is temperature 1.0 and top_k 20 7. The card warns that Ollama on Apple Silicon may return empty output until llama.cpp#25389 ships 8. Validate JSON output 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, Laguna XS 2.1 or ZAYA1-8B? Laguna XS 2.1 and ZAYA1-8B score within a point of each other on agent readiness, 61 (C) and 61 (C). ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-zaya1-8b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-zaya1-8b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "laguna-xs-2-1", "b": "zaya1-8b"}`. 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[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