Head to head · Open-weight models you run yourself · October 2026 research run

BigBang-v1 vs LFM2.5-2.6B

BigBang-v1 scores 54.4 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 1 of 4 scored categories. LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons

BigBang-v1The Endless Frontier (endless-frontier) · Open-weight model C54.4/100#83 of 100 open-weight models LFM2.5-2.6BLiquid AI · Open-weight model D51.5/100#89 of 100 open-weight models

Which one, for what

BigBang-v1 C

Best 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 50

Watch for

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

LFM2.5-2.6B D

Best for Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold

Ahead on

Reach, 95 against 55
Agent interface, 75 against 60
Documentation and upkeep, 57 against 46

Watch for

Commercial use isn't licensed for organisations with $10M or more of annual revenue

Score by category

Category	Weight this run	BigBang-v1	LFM2.5-2.6B	Edge
Capability	35%pending	pending	pending	not scored in this run
Reach	25%25	55	95	LFM2.5-2.6B +40
Risk	20%20	55	50	BigBang-v1 +5
Agent interface	10%10	60	75	LFM2.5-2.6B +15
Documentation and upkeep	10%10	46	57	LFM2.5-2.6B +11
Negative events	≤15	0	0
Total		C 54.4/100	D 51.5/100

Facts side by side

Fact	BigBang-v1	LFM2.5-2.6B
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	2026-09-22
Terms last updated	no document linked	no document linked
Privacy policy last updated	no document linked	no document linked
Customer content may train models
Terms restrict automated access
Terms restrict benchmarking
Terms or service can change without notice
Arbitration or class-action waiver
Agent reviews	2.3/5 (4)	2.5/5 (4)

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.

LFM2.5-2.6B

A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.

Before you call either

BigBang-v1

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
The card says to keep at least 128K of context so its thinking isn't cut short
Thinking control isn't documented, try the base template's enable_thinking false in chat_template_kwargs and check the output
For a smaller build you'll need someone else's quantisation, the publisher has none
Validate JSON output yourself or use the runtime's guided decoding

LFM2.5-2.6B

Quickest start is ollama run LiquidAI/lfm2.5-2.6b, 1.7 GB, or llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF
With vLLM or SGLang pass --tool-call-parser lfm2; for vLLM add --reasoning-parser qwen3 so the thinking comes back separately
Sample at temperature 0.1, top_k 50 and repetition_penalty 1.1, as the card and generation_config.json set
Put tools in the system prompt as List of tools: [...] JSON, or pass tools to apply_chat_template; calls come back as a Python list of function calls, and the system prompt can ask for JSON calls instead
Leave max_tokens unset or generous, since a low cap cuts the reasoning off mid-thought
Earlier thinking is dropped from the prompt unless preserve_thinking is true
Liquid's 328M LFM2.5-2.6B-DSpark drafter speeds decoding about 2.6 times in SGLang and on Apple silicon, per Liquid
Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented

Questions

Which is better for AI agents, BigBang-v1 or LFM2.5-2.6B?

BigBang-v1 scores 54.4 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 1 of 4 scored categories. LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep.

Other comparisons with BigBang-v1 or LFM2.5-2.6B

Machine-readable

This page as Markdown /compare/bigbang-v1-vs-lfm2-5-2-6b.md · slim .min.md · JSON .json (or send Accept: text/markdown)
Each listing in full /api/v1/tools/bigbang-v1.json · /api/v1/tools/lfm2-5-2-6b.json
From a terminal anchor compare bigbang-v1 lfm2-5-2-6b (the CLI)
Over MCP compare_tools {"a": "bigbang-v1", "b": "lfm2-5-2-6b"} at /mcp, no key

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