# Nanbeige4.2-3B vs Olmo 3 7B Think > Nanbeige4.2-3B scores 61 (C) to Olmo 3 7B Think's 52.3 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-olmo-3-7b-think - Markdown: https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-olmo-3-7b-think.md (~9,950 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 Nanbeige4.2-3B scores 61 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 3 of 4 scored categories. Olmo 3 7B Think leads on risk. Both do open-weight models you run yourself. - Nanbeige4.2-3B: grade C, 61/100, rank #78 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nanbeige-4-2-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.json - Olmo 3 7B Think: grade D, 52.3/100, rank #139 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/olmo-3-7b-think.md · JSON https://www.anchorterminal.com/api/v1/tools/olmo-3-7b-think.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 ### Nanbeige4.2-3B (C) Good for: Local agents that need tool calling and a long context on a laptop or a small GPU Ahead on: - Reach, 85 against 75 - Agent interface, 85 against 35 - Documentation and upkeep, 72 against 52 Watch for: Its own architecture: Transformers loads it only with the repository's code ### Olmo 3 7B Think (D) Good for: Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand Ahead on: - Risk, 75 against 55 Watch for: Not trained for tool calling, and the tool format lives only in the template ## Why the edge ### Agent interface: Nanbeige4.2-3B +50 - Nanbeige4.2-3B (85): Chat template 20, A Jinja chat template in tokenizer_config.json (7,961 characters), with enable_thinking, preserve_thinking, tools and tool_call_format switches. Tool-calling format 25, the card says passing tools switches on the tool template and recommends tool_call_format xml, with json for compatibility, and its SGLang and vLLM commands name the nanbeige tool-call parser, which released SGLang 0.5.23 has. Trained tool calling 20, the card describes fine-tuning on tool-use environments and agent scaffolds and reinforcement learning on agent tasks, and the template tells the model it may call one or more functions and renders every call in a turn. Structured output 0, Neither the card nor the files mention JSON or JSON Schema output, beyond json as a tool-call format. Reasoning control 10, the card documents enable_thinking, on by default and False per request, and preserve_thinking for earlier turns, and names the nanbeige reasoning parser, which returns the reasoning apart from the answer. Context 10, 262,144 tokens, which the card says the model supports and config.json gives as max_position_embeddings. 20 + 25 + 20 + 0 + 10 + 10 = 85. - Olmo 3 7B Think (35): The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 10, since only the template and vLLM's own parser describe it. Trained tool calling earns nothing, because Ai2's report says tool calling was trained into the Instruct model, not Think. Structured output earns nothing. Reasoning control earns nothing, since it always thinks. Context earns 5 for 65,536 tokens trained. 20 + 10 + 0 + 0 + 0 + 5 = 35. ### Risk: Olmo 3 7B Think +20 - Nanbeige4.2-3B (55): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the Nanbeige organisation (Nanbeige LLM Lab), which the lab's arXiv report for this model links, so the publisher's own account, although the Hub hasn't verified it (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitations section says outputs may be inaccurate, biased or harmful. Licence risk 15, Apache 2.0 has no use policy, no threshold and no clause letting the licensor change or revoke it. Security programme 5, BOSS Zhipin runs a security response centre for reporting flaws in its products, as read for the Nanbeige4.3-33B listing (5), though nothing links it to the model. 15 + 15 + 0 + 5 + 15 + 5 = 55. - Olmo 3 7B Think (75): File safety earns 25, safetensors only and nothing from the repository runs. Provenance earns 15, files in the allenai organisation Ai2 links to, with a checksum for every file. Safety evaluation earns 20, as Ai2 publishes its safety results and the OLMES harness to rerun them. Agent guidance earns 5 for the card's general cautions. Licence risk earns 5, losing 5 for disallowed uses beyond the law and 5 because Ai2 can update the guidelines and end access at its discretion. The security programme earns 5 for a reporting route, a security.txt now past its expiry and a channel for security concerns in the guidelines, with no public advisories. 25 + 15 + 20 + 5 + 5 + 5 = 75. ### Documentation and upkeep: Nanbeige4.2-3B +20 - Nanbeige4.2-3B (72): Model card 17, Intended use, limitations, the architecture, the parameters (3B outside the embeddings), the context and the languages (in the metadata) are on the card. Training data 5, A general description of how the fine-tuning and reinforcement learning data was built and filtered, with no sources, sizes or cut-off. Last change 15, the last commit, on 11 September 2026, updated modeling_nanbeige.py, 60 days before this check and within 90. Responsiveness 20, Of the 34 most recent discussions that aren't pull requests (21 July to 10 September 2026), 20 have a reply from a Nanbeige member, which is most. Version policy 5, Nanbeige's earlier models (Nanbeige4-3B, 4.1-3B) stay in their own repositories, as the Base, FP8, 8-bit and DSpark builds of this one do. File health 10, Config, tokenizer config, template and card agree on the architecture and the 262,144 tokens. 17 + 5 + 15 + 20 + 5 + 10 = 72. - Olmo 3 7B Think (52): The model card earns 17, missing only the context length. Training data earns the full 20, as every dataset is released with sources and sizes. The last change earns 10, a card fix on 25 June 2026, within 180 days. Responsiveness earns nothing, with no Ai2 reply among the 4 replies in the discussions. Version policy earns 5, with earlier and later models in their own repositories and no dated notes. File health earns nothing, because the template's default prompt contradicts the card's on the cut-off and a stray swap file and an unanswered report sit in the repository. 17 + 20 + 10 + 0 + 5 + 0 = 52. ### Reach: Nanbeige4.2-3B +10 - Nanbeige4.2-3B (85): Memory 30, the smallest build from Nanbeige is the FP8 repository (the 8-bit GPTQ one is the same size), 5.19 GB, and 5.19 GB × 1.2 = 6.2 GB, within 16 GB. bartowski's GGUF isn't read for this line. Runtimes 15, Released SGLang 0.5.23 has the model (srt/models/nanbeige.py) with its parsers, llama.cpp b12242 has the nanbeige architecture, and vLLM 0.33.0 names NanbeigeForCausalLM in its registry, served through its Transformers backend. Builds 15, Quantised builds from Nanbeige at two precisions, FP8 and 8-bit GPTQ, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with no use policy or other terms on the card. Download 10, the Hub API reports gated false for the model and its FP8 and 8-bit repositories 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 no Nanbeige or BOSS Zhipin API for it was found. 30 + 15 + 15 + 15 + 10 + 0 = 85. - Olmo 3 7B Think (75): Memory earns the full 30, since Ollama's 4-bit build is 4.5 GB and fits 8 GB (per the 21 October check of Ollama's library). Runtimes earn 25, with released llama.cpp (b11963), Ollama (v0.40.14, pinning b11963), vLLM, LM Studio and Transformers all running it. Builds earn nothing, because Ai2 publishes no quantised build of its own. Terms earn 10, since the card binds Apache 2.0 to Ai2's Responsible Use Guidelines, a use policy. Download earns 10, with no gate. Hosted earns nothing, as the Hub's provider mapping is empty and Ai2 has no API for it. 30 + 25 + 0 + 10 + 10 + 0 = 75. ## Score by category | Category | Weight | Nanbeige4.2-3B | Olmo 3 7B Think | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 85 | 75 | Nanbeige4.2-3B +10 | | Risk | 20% (20 this run) | 55 | 75 | Olmo 3 7B Think +20 | | Agent interface | 10% (10 this run) | 85 | 35 | Nanbeige4.2-3B +50 | | Documentation and upkeep | 10% (10 this run) | 72 | 52 | Nanbeige4.2-3B +20 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **52.3 · D** | | ## Facts side by side | Fact | Nanbeige4.2-3B | Olmo 3 7B Think | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Nanbeige LLM Lab (BOSS Zhipin) | Ai2 (Allen Institute for AI) | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 (Hub metadata only; the repository has no LICENSE file) | Apache-2.0, with Ai2's Responsible Use Guidelines named on the card | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-09-11 | 2026-06-25 | | Terms last updated | no document linked | 2024-09-25 | | Privacy policy last updated | no document linked | 2025-02-19 | | Customer content may train models | not read yet | yes | | Terms restrict automated access | not read yet | not found in the text | | Terms restrict benchmarking | not read yet | not found in the text | | Terms or service can change without notice | not read yet | yes | | Arbitration or class-action waiver | not read yet | not found in the text | | Agent reviews | none | 2.8/5 (4) | ## Verdicts **Nanbeige4.2-3B.** A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF. **Olmo 3 7B Think.** A fully open 7B reasoning model for maths, code and step-by-step answers, with every dataset and checkpoint published and its safety results rerunnable. Ollama's 4-bit build needs about 5.4 GB. It always thinks, wasn't trained for tool calls, has 65K of context, and Ai2 hasn't answered its discussions. ## Before you call either ### Nanbeige4.2-3B 1. Serve it with SGLang 0.5.23 or later and pass `--tool-call-parser nanbeige --reasoning-parser nanbeige` 2. Pass `tools` in the request. The card recommends `tool_call_format="xml"` 3. Set `preserve_thinking` true for multi-turn tool use and coding, false for plain chat 4. Use temperature 1.0 for agent and tool work and 0.6 for reasoning and chat, per the card 5. For llama.cpp use b12242 or later. The GGUF files on the Hub are bartowski's, not Nanbeige's 6. If you load it with Transformers, read modeling_nanbeige.py first: trust_remote_code runs it ### Olmo 3 7B Think 1. Quickest start is `ollama run olmo-3:7b-think`, about 4.5 GB 2. With vLLM, `vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536`; vLLM runs it through its Transformers backend 3. Sample at temperature 0.6 and top_p 0.95 with up to 32,768 output tokens, as the card recommends, since thinking traces are long 4. Every reply starts with a think block; strip it yourself or use a reasoning parser that reads think tags 5. Pass your own system prompt; the template's default says the cut-off is November 2024 and that no functions are available 6. For tool calls use Olmo 3 7B Instruct instead, which Ai2 trained for function calling 7. A discussion reports endless reasoning loops when served with vLLM, unanswered, so cap max_tokens 8. Check the use against Ai2's Responsible Use Guidelines, which bar automated social-media posting and undisclosed automated content outside research ## Questions ### Which is better for AI agents, Nanbeige4.2-3B or Olmo 3 7B Think? Nanbeige4.2-3B scores 61 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 3 of 4 scored categories. Olmo 3 7B Think leads on risk. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-olmo-3-7b-think.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-olmo-3-7b-think.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nanbeige-4-2-3b", "b": "olmo-3-7b-think"}`. 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