# LFM3-1.7B vs ZAYA1-8B > LFM3-1.7B 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/lfm3-1-7b-vs-zaya1-8b - Markdown: https://www.anchorterminal.com/compare/lfm3-1-7b-vs-zaya1-8b.md (~9,100 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 LFM3-1.7B and ZAYA1-8B score within a point of each other on agent readiness, 61 (C) and 61 (C). ZAYA1-8B leads on risk. Both do open-weight models you run yourself. - LFM3-1.7B: grade C, 61/100, rank #69 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm3-1-7b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm3-1-7b.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 ### LFM3-1.7B (C) Good for: Agents on a laptop, phone or small server that need tool calling and a long context from a model under 2 GB at 4 bits Ahead on: - Reach, 85 against 80 - Agent interface, 100 against 60 - Documentation and upkeep, 67 against 50 Watch for: vLLM and SGLang have no released support, though the card lists both ### 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: - Risk, 55 against 40 Watch for: Tool calling needs a parser that only Zyphra's vLLM fork has ## Why the edge ### Agent interface: LFM3-1.7B +40 - LFM3-1.7B (100): Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, tools, deferred tools, tool results and the thinking switch. Tool-calling format 25, the card and Liquid's tool-use page document tools passed through `tools=` in apply_chat_template, calls returned as XML and results sent back as tool messages. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so several calls fit in one turn, and the card says the model was trained on tool calls and agentic traces and with reinforcement learning inside agent harnesses. Structured output 15, Liquid's API guide documents `response_format` with a JSON Schema, with lfm3-1.7b as the model in its examples, and says the schema is checked before generation starts. Reasoning control 10, the card documents `enable_thinking=False` in the chat template to turn reasoning off per request, with reasoning on by default inside think tags, and `preserve_thinking` for earlier turns. Context 10, 256K tokens per the card, which says the mid-training stage extended the context window to 256K, and max_position_embeddings 262144 in config.json. 20 + 25 + 20 + 15 + 10 + 10 = 100. - 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: LFM3-1.7B +17 - LFM3-1.7B (67): Model card 17, Intended use (local agents, tool calling, structured outputs, fine-tuning), a limitation (not recommended for agentic coding or complex multi-step reasoning), architecture (24 layers: 13 gated short convolutions, 5 sliding-window and 6 global attention), parameters (1.73B), context (256K) and 16 languages. Training data 5, A general description: 22T tokens of pre-training in two stages with the kinds of data added since LFM2, 1.75T tokens of supervised fine-tuning, then mixed reinforcement learning, distillation from six specialist teachers and agentic reinforcement learning. Last change 20, the last commit, on 9 November 2026, added the OpenVINO checkpoint to the card's inference table, a day before this check. Responsiveness 10, Of the 22 discussions that aren't pull requests (27 October to 8 November 2026), 7 have a reply from a LiquidAI member, which is some. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 10, Config, template and card agree on the lfm3 architecture, the 262,144-token context and the thinking switch. 17 + 5 + 20 + 10 + 5 + 10 = 67. - 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: ZAYA1-8B +15 - LFM3-1.7B (40): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40. - 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. ### Reach: LFM3-1.7B +5 - LFM3-1.7B (85): Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB, and 1.10 GB × 1.2 = 1.3 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 15, Released llama.cpp has the lfm3 architecture (src/models/lfm3.cpp at b12242), Ollama's library has an lfm3 entry, and LM Studio's catalogue runs the architecture (its liquid/lfm3-vl-2b entry; lmstudio-community publishes a GGUF of this model). Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-1.7b among its models, so the publisher hosts it. 30 + 15 + 15 + 10 + 10 + 5 = 85. - 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 | LFM3-1.7B | ZAYA1-8B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 85 | 80 | LFM3-1.7B +5 | | Risk | 20% (20 this run) | 40 | 55 | ZAYA1-8B +15 | | Agent interface | 10% (10 this run) | 100 | 60 | LFM3-1.7B +40 | | Documentation and upkeep | 10% (10 this run) | 67 | 50 | LFM3-1.7B +17 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **61 · C** | | ## Facts side by side | Fact | LFM3-1.7B | ZAYA1-8B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Liquid AI | Zyphra | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | yes | no | | Last release | 2026-11-09 | 2026-07-02 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | ## Verdicts **LFM3-1.7B.** A 1.73B model that fits an 8 GB machine with room to spare, with a documented tool-call format, deferred tools, a thinking switch and a 256K context. It runs today in llama.cpp, Ollama and LM Studio only, loads in Transformers through repository code, and has no safety results, security contact or data sources. **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 ### LFM3-1.7B 1. Start it with `llama-server -hf LiquidAI/LFM3-1.7B-GGUF:Q4_K_M --jinja` or `ollama run lfm3` 2. Use temperature 0.1, top_k 50 and repetition penalty 1.1 for chat, tools and agents, per the card 3. Pass `enable_thinking=False` for direct answers, and expect it to reason anyway on hard tasks 4. For a large tool catalogue mark tools `defer_loading` and give it a search tool 5. Don't plan on vLLM or SGLang until a release carries LFM3 6. Liquid doesn't recommend it for agentic coding or complex multi-step reasoning ### 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, LFM3-1.7B or ZAYA1-8B? LFM3-1.7B and ZAYA1-8B score within a point of each other on agent readiness, 61 (C) and 61 (C). ZAYA1-8B leads on risk. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/lfm3-1-7b-vs-zaya1-8b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm3-1-7b-vs-zaya1-8b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "lfm3-1-7b", "b": "zaya1-8b"}`. 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