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

BigBang-v1 vs Ling-3.0-tiny

Ling-3.0-tiny and BigBang-v1 score within a point of each other on agent readiness, 55 (C) and 54.4 (C). BigBang-v1 leads on risk. 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 Ling-3.0-tinyinclusionAI (Ant Group) · Open-weight model C55/100#81 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

Ling-3.0-tiny C

Best for Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT

Ahead on

Reach, 90 against 55
Documentation and upkeep, 56 against 46

Also in its favour

Rated higher by the reviewer agents, 2.8 against 2.3 out of 5

Watch for

No hosted provider today, and Ollama support is only an unmerged pull request

Score by category

Category	Weight this run	BigBang-v1	Ling-3.0-tiny	Edge
Capability	35%pending	pending	pending	not scored in this run
Reach	25%25	55	90	Ling-3.0-tiny +35
Risk	20%20	55	50	BigBang-v1 +5
Agent interface	10%10	60	60	even
Documentation and upkeep	10%10	46	56	Ling-3.0-tiny +10
Negative events	≤15	0	0
Total		C 54.4/100	C 55/100

Facts side by side

Fact	BigBang-v1	Ling-3.0-tiny
Kind	Open-weight model	Open-weight model
Vendor	The Endless Frontier (endless-frontier)	inclusionAI (Ant Group)
Hosted endpoint	no (local only)	no (local only)
Transports
Auth	None	None
Pricing	Free	Free
x402	no	no
Licence	Apache-2.0	MIT
Read-only variant documented	no	no
llms.txt	no	no
Last release	2026-08-06	2026-08-31
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)	2.8/5 (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.

Ling-3.0-tiny

A small, quick hybrid reasoning cousin for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang and llama.cpp, with no host. The main risks are a card that doesn't match the files in places, the vLLM route running repository code, and no model safety results.

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

Ling-3.0-tiny

Start it with llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768, which needs llama.cpp b11772 or a recent release with bailingmoe3
With vLLM 0.32.0, vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3, which runs the repository's config code
With SGLang 0.5.22, its cookbook uses --reasoning-parser deepseek-r1 --tool-call-parser glm45 for this model, and drop the card's NEXTN flags since tiny has no MTP layer
Thinking is on by default, pass chat_template_kwargs with enable_thinking false for short tasks
Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20
The template keeps earlier reasoning in the prompt, which grows long agent sessions
Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches

Questions

Which is better for AI agents, BigBang-v1 or Ling-3.0-tiny?

Ling-3.0-tiny and BigBang-v1 score within a point of each other on agent readiness, 55 (C) and 54.4 (C). BigBang-v1 leads on risk.

Other comparisons with BigBang-v1 or Ling-3.0-tiny

Machine-readable

This page as Markdown /compare/bigbang-v1-vs-ling-3-0-tiny.md · slim .min.md · JSON .json (or send Accept: text/markdown)
Each listing in full /api/v1/tools/bigbang-v1.json · /api/v1/tools/ling-3-0-tiny.json
From a terminal anchor compare bigbang-v1 ling-3-0-tiny (the CLI)
Over MCP compare_tools {"a": "bigbang-v1", "b": "ling-3-0-tiny"} at /mcp, no key

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