# Gemma 4 31B IT vs ZAYA1-8B > Gemma 4 31B IT scores 74.1 (BB) to ZAYA1-8B's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-zaya1-8b - Markdown: https://www.anchorterminal.com/compare/gemma-4-31b-it-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 Gemma 4 31B IT scores 74.1 (BB) on agent readiness against ZAYA1-8B's 61 (C), and leads in every scored category. Both do open-weight models you run yourself. - Gemma 4 31B IT: grade BB, 74.1/100, rank #2 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-31b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.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 ### Gemma 4 31B IT (BB) Good for: An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence Ahead on: - Reach, 95 against 80 - Risk, 70 against 55 - Agent interface, 85 against 60 - Documentation and upkeep, 65 against 50 Also in its favour: - Agent-ready, a grade of BB or better Watch for: No safety evaluation results published ### 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: Gemma 4 31B IT +25 - Gemma 4 31B IT (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Google documents the declaration and call syntax and ships a response schema that Transformers parses. Trained tool calling 20, several calls and responses in one turn. Structured output 0, Google documents none and the card doesn't claim it. Reasoning control 10, thinking turns on or off per request and comes back in its own block. Context 10, 262,144 tokens native. - 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: Gemma 4 31B IT +15 - Gemma 4 31B IT (95): Memory 25, Google's own QAT Q4_0 GGUF is 17.7 GB, which with a fifth added comes to about 21 GB and fits a 24 GB card even with the vision projector. Runtimes 25, released code in llama.cpp, Ollama's library, vLLM, SGLang and Transformers fills the cap without LM Studio, which only Google's guides name. Builds 15, Google ships a 4-bit GGUF and a 4-bit compressed-tensors build, read as two of the kinds the checklist names. Terms 15, plain Apache 2.0 with nothing added. Download 10, no gate and no account. Hosted 5, Google's Gemini 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: Gemma 4 31B IT +15 - Gemma 4 31B IT (70): File safety 25, safetensors only and no code from the repository runs to load it or build prompts. Provenance 15, the files come from the google organisation that gemma.ai links to, with a SHA-256 for every weight file and a git hash for every file. Safety evaluation 0, Google says violations were minimal but publishes no results. Agent guidance 10, the card says what its safety training covers and asks for content safeguards, and the tool guide says to validate calls, but there is no prompt-injection guidance. Licence risk 15, Apache 2.0 carries none of the deductions and its link works. Security programme 5, Google's company-wide security.txt and reward programme exist but nothing ties them to Gemma, and no public advisories were found. - 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. ### Documentation and upkeep: Gemma 4 31B IT +15 - Gemma 4 31B IT (65): Model card 20, all seven items are there, with languages given as counts. Training data 10, a general description with a January 2025 cut-off. Last change 10, the files last changed on 20 July, 104 days before 1 November, and Google's reference code for Gemma 4 last changed in a way that runs this model on 6 July, both inside 180 days. Responsiveness 10, Google answered 14 of the last 40 discussions, none since 30 July. Version policy 10, earlier generations are kept in their own repositories with dated releases. File health 5, two template reports wait without 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. ## Score by category | Category | Weight | Gemma 4 31B IT | ZAYA1-8B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 95 | 80 | Gemma 4 31B IT +15 | | Risk | 20% (20 this run) | 70 | 55 | Gemma 4 31B IT +15 | | Agent interface | 10% (10 this run) | 85 | 60 | Gemma 4 31B IT +25 | | Documentation and upkeep | 10% (10 this run) | 65 | 50 | Gemma 4 31B IT +15 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **74.1 · BB** | **61 · C** | | ## Facts side by side | Fact | Gemma 4 31B IT | ZAYA1-8B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Google DeepMind | 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 | yes | no | | Last release | 2026-07-20 | 2026-07-02 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | 3.8/5 (4) | none | ## Verdicts **Gemma 4 31B IT.** A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July. **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 ### Gemma 4 31B IT 1. On the reference machine run `llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768`, adding gemma-4-31B-it-mmproj.gguf for images 2. Or `ollama run gemma4:31b` for the 20 GB Q4_K_M build 3. In vLLM serve with `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4`, as vLLM's recipe does 4. Thinking is off by default, so pass `enable_thinking: true` in chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block 5. Drop earlier thoughts from the history except on tool-call turns, as the card asks 6. Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64 7. Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding 8. The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine ### 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, Gemma 4 31B IT or ZAYA1-8B? Gemma 4 31B IT scores 74.1 (BB) on agent readiness against ZAYA1-8B's 61 (C), and leads in every scored category. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-zaya1-8b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-zaya1-8b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gemma-4-31b-it", "b": "zaya1-8b"}`. 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