# BigBang-v1 vs ZAYA1-8B > ZAYA1-8B scores 61 (C) to BigBang-v1's 56.3 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/bigbang-v1-vs-zaya1-8b - Markdown: https://www.anchorterminal.com/compare/bigbang-v1-vs-zaya1-8b.md (~7,750 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 BigBang-v1's 56.3 (C), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself. - BigBang-v1: grade C, 56.3/100, rank #116 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/bigbang-v1.md · JSON https://www.anchorterminal.com/api/v1/tools/bigbang-v1.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 ### BigBang-v1 (C) Good for: Agents with 96 GB or more that want an Apache 2.0 mixture-of-experts model tuned for research, search and coding, and can do without publisher support. Watch for: BF16 is the only build from the publisher, needing about 86 GB ### 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: - Reach, 80 against 60 Watch for: Tool calling needs a parser that only Zyphra's vLLM fork has ## Why the edge ### Reach: ZAYA1-8B +20 - BigBang-v1 (60): Memory 10 of 30, the only build from the publisher is BF16 at 71.9 GB, 86.3 GB with a fifth added, which fits the reference machine but not 64 GB. Runtimes 25, it keeps the base's qwen3_5_moe architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, and Ollama 0.40.14 runs through the llama.cpp build it pins. Builds 0, no quantised build from the publisher. Terms 15, Apache 2.0 like its base. Download 10, not gated. Hosted 0, one inference provider, Featherless AI, where the line needs two. 10 + 25 + 0 + 15 + 10 + 0 = 60. - 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 | BigBang-v1 | ZAYA1-8B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 60 | 80 | ZAYA1-8B +20 | | Risk | 20% (20 this run) | 55 | 55 | even | | Agent interface | 10% (10 this run) | 60 | 60 | even | | Documentation and upkeep | 10% (10 this run) | 46 | 50 | ZAYA1-8B +4 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **56.3 · C** | **61 · C** | | ## Facts side by side | Fact | BigBang-v1 | ZAYA1-8B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | The Endless Frontier (endless-frontier) | 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 | 2026-08-06 | 2026-07-02 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | 2.3/5 (4) | none | ## How the scores have moved ### BigBang-v1 - +1.9 on 4 November 2026: correction ### ZAYA1-8B No score change since it was listed. ## Verdicts **BigBang-v1.** A Qwen3.6-35B-A3B fine-tuned on synthetic research problems, under plain Apache 2.0, for search, science and coding work. The publisher ships BF16 only, about 86 GB to run, and has no report, safety results or replies to questions, so smaller builds and support come from others. **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 ### BigBang-v1 1. On the reference machine use `vllm serve endless-frontier/BigBang-v1 --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` 2. The card says to keep at least 128K of context so its thinking isn't cut short 3. Thinking control isn't documented, try the base template's `enable_thinking` false in chat_template_kwargs and check the output 4. For Ollama or llama.cpp you'll need someone else's GGUF; the publisher ships BF16 safetensors only 5. Validate JSON output yourself or use the runtime's guided decoding ### 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, BigBang-v1 or ZAYA1-8B? ZAYA1-8B scores 61 (C) on agent readiness against BigBang-v1's 56.3 (C), and leads in 2 of 4 scored categories. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/bigbang-v1-vs-zaya1-8b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/bigbang-v1-vs-zaya1-8b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "bigbang-v1", "b": "zaya1-8b"}`. 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