# BigBang-v1 vs MiniCPM-V 4.7 1B > MiniCPM-V 4.7 1B 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-minicpm-v-4-7-1b - Markdown: https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-1b.md (~8,650 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 MiniCPM-V 4.7 1B scores 61 (C) on agent readiness against BigBang-v1's 56.3 (C), and leads in 3 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 - MiniCPM-V 4.7 1B: grade C, 61/100, rank #76 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-1b.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 ### MiniCPM-V 4.7 1B (C) Good for: Agents that need a cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model Ahead on: - Reach, 95 against 60 - Agent interface, 70 against 60 Watch for: No limitations section, safety results, agent-use guidance or security contact ## Why the edge ### Reach: MiniCPM-V 4.7 1B +35 - 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. - MiniCPM-V 4.7 1B (95): Memory 30, OpenBMB's own IQ4_XS GGUF is 0.64 GB, 1.54 GB with the 0.90 GB vision projector, and 1.54 GB × 1.2 = 1.9 GB, which fits 16 GB with room to spare. Runtimes 25, MiniCPMV4_7ForConditionalGeneration in vLLM's registry at v0.33.0, the minicpmv4_7 model type in SGLang's minicpmv.py at v0.5.23, the minicpmv4_7 model in Transformers at v5.22.2, a MiniCPM-V 4.7 guide and conversion code in llama.cpp at b12242, and an openbmb/minicpm-v4.7:1b tag in OpenBMB's namespace on Ollama, read as the 35B-A3B listing's is. Builds 15, OpenBMB publishes AWQ, GPTQ and int4 safetensors builds and a GGUF repository from IQ3_XS to F16, so quantised builds at two precisions or more. Terms 15, Apache 2.0 in the card's metadata and as the LICENSE of OpenBMB's MiniCPM-V repository, an OSI licence. Download 10, gated is false on the main repository and on the GGUF, AWQ, GPTQ and int4 repositories, so the files download with no account or terms. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and no provider's own list was read. 30 + 25 + 15 + 15 + 10 + 0 = 95. ### Agent interface: MiniCPM-V 4.7 1B +10 - BigBang-v1 (60): Chat template 20, the base's template in the repository. Tool-calling format 10 of 25, the card names the qwen3_coder parser and nothing more. Trained tool calling 20, tuned for tool use with several calls a turn. Structured output 0. Reasoning control 0, the card doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens. 20 + 10 + 20 + 0 + 0 + 10 = 60. - MiniCPM-V 4.7 1B (70): Chat template 20, chat_template.jinja in the repository, 7,249 characters. Tool-calling format 10, the card shows tools declared in the OpenAI tools array in a request to Transformers' server, but not how a call comes back, and names no parser for vLLM or SGLang. Trained tool calling 20, the template loops over tool_calls, so it renders several calls in one assistant turn, and the card gives a tool-calling request as a supported use. Structured output 0, Neither JSON nor JSON Schema output is documented or claimed for the model on the card. Reasoning control 10, the card passes enable_thinking per request in its examples, the template has been off by default since 19 October 2026, and the GGUF repository keeps thinking builds in their own folder. Context 10, 262,144 tokens, max_position_embeddings in the text config. 20 + 10 + 20 + 0 + 10 + 10 = 70. ## Score by category | Category | Weight | BigBang-v1 | MiniCPM-V 4.7 1B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 60 | 95 | MiniCPM-V 4.7 1B +35 | | Risk | 20% (20 this run) | 55 | 55 | even | | Agent interface | 10% (10 this run) | 60 | 70 | MiniCPM-V 4.7 1B +10 | | Documentation and upkeep | 10% (10 this run) | 46 | 48 | MiniCPM-V 4.7 1B +2 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **56.3 · C** | **61 · C** | | ## Facts side by side | Fact | BigBang-v1 | MiniCPM-V 4.7 1B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | The Endless Frontier (endless-frontier) | OpenBMB | | 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 | 2026-10-30 | | 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 ### MiniCPM-V 4.7 1B 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. **MiniCPM-V 4.7 1B.** A vision-language cousin small enough for a phone, under plain Apache 2.0, that every runtime we count can load. The projector is bigger than the language model, so plan for about 2 GB. The card says nothing about limits or training data and doesn't show how a tool call comes back. ## 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 ### MiniCPM-V 4.7 1B 1. Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:1b` 2. For image input in llama.cpp add `mmproj-model-f16.gguf`, or the smaller 4x projector 3. The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace 4. Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends 5. Check tool calls in your runtime first, since the card names no parser 6. Validate JSON output yourself, since structured output isn't documented ## Questions ### Which is better for AI agents, BigBang-v1 or MiniCPM-V 4.7 1B? MiniCPM-V 4.7 1B scores 61 (C) on agent readiness against BigBang-v1's 56.3 (C), and leads in 3 of 4 scored categories. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-1b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-1b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "bigbang-v1", "b": "minicpm-v-4-7-1b"}`. 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