# BigBang-v1 vs LFM3-1.7B

> LFM3-1.7B 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-lfm3-1-7b
- Markdown: https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-1-7b.md (~9,000 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 <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. 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 scores 61 (C) on agent readiness against BigBang-v1's 56.3 (C), and leads in 3 of 4 scored categories. BigBang-v1 leads on risk. 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
- 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
- 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.

Ahead on:
- Risk, 55 against 40

Watch for: BF16 is the only build from the publisher, needing about 86 GB

### 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 60
- Agent interface, 100 against 60
- Documentation and upkeep, 67 against 46

Watch for: vLLM and SGLang have no released support, though the card lists both


## Why the edge

### Agent interface: LFM3-1.7B +40

- 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.
- 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.

### Reach: LFM3-1.7B +25

- 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.
- 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.

### Documentation and upkeep: LFM3-1.7B +21

- BigBang-v1 (46): Model card 11 of 20, limitations, architecture and a cut-off are missing. Training data 5, a general description of about 10,000 synthetic examples. Last change 15, the card last changed on 6 August 2026, 90 days before this check. Responsiveness 0, no publisher reply to any of 7 questions. Version policy 5, a first release. File health 10, the files agree with the card and the base. 11 + 5 + 15 + 0 + 5 + 10 = 46.
- 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.

### Risk: BigBang-v1 +15

- BigBang-v1 (55): File safety 25, safetensors only and nothing from the repository runs. Provenance 15, the files are in the publisher's own organisation with a SHA-256 for every weight file. Safety evaluation 0. Agent guidance 0. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no website or security contact. 25 + 15 + 0 + 0 + 15 + 0 = 55.
- 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.

## Score by category

| Category | Weight | BigBang-v1 | LFM3-1.7B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 60 | 85 | LFM3-1.7B +25 |
| Risk | 20% (20 this run) | 55 | 40 | BigBang-v1 +15 |
| Agent interface | 10% (10 this run) | 60 | 100 | LFM3-1.7B +40 |
| Documentation and upkeep | 10% (10 this run) | 46 | 67 | LFM3-1.7B +21 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **56.3 · C** | **61 · C** | |

## Facts side by side

| Fact | BigBang-v1 | LFM3-1.7B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | The Endless Frontier (endless-frontier) | Liquid AI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | LFM Open License v1.0 (free commercial use below $10M annual revenue) |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2026-08-06 | 2026-11-09 |
| 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

### LFM3-1.7B

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.

**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.

## 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

### 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

## Questions

### Which is better for AI agents, BigBang-v1 or LFM3-1.7B?

LFM3-1.7B scores 61 (C) on agent readiness against BigBang-v1's 56.3 (C), and leads in 3 of 4 scored categories. BigBang-v1 leads on risk.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-1-7b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-1-7b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "bigbang-v1", "b": "lfm3-1-7b"}`. From a terminal: `anchor compare bigbang-v1 lfm3-1-7b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/bigbang-v1.json and https://www.anchorterminal.com/api/v1/tools/lfm3-1-7b.json

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- [LFM3-1.7B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-2b.md)
- [LFM3-1.7B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-4b.md)
- [LFM3-1.7B vs ZAYA1-8B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-zaya1-8b.md)

---

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