Head to head · Open-weight models you run yourself · October 2026 research run
BigBang-v1 vs Ling-3.1-flash-1019
BigBang-v1 scores 56.3 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 1 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 4969 open weights comparisons
Which one, for what
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 45
Watch for
BF16 is the only build from the publisher, needing about 86 GB
Best for Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding
Ahead on
- Documentation and upkeep, 56 against 46
Watch for
574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Risk BigBang-v1 +10
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.
Ling-3.1-flash-1019 45
File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the repository's configuration_bailing_moe_v3.py, since vLLM 0.33.0 registers no bailing_hybrid config of its own. Provenance 15, files in the inclusionAI organisation that inclusion-ai.org links to, per the Ling-3.0-tiny listing (10). Safety evaluation 0, no safety results for the model on its card or in the training-content summary. Agent guidance 0, the card has nothing on prompt injection, refusals or safeguards, and inclusionAI publishes no guardrail probe for this model as it did for Ling 3.0. Licence risk 15, MIT with no use policy, no field-of-use limits, no threshold and no clause letting inclusionAI change or revoke it. Security programme 0, www.inclusion-ai.org answers 404 for security.txt on 10 November 2026, and the Ling-3.0-tiny listing found no SECURITY.md or disclosure policy in the inclusionAI .github repository. 15 + 15 + 0 + 0 + 15 + 0 = 45.
Documentation and upkeep Ling-3.1-flash-1019 +10
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.
Ling-3.1-flash-1019 56
Model card 11, Intended use (complex reasoning, multi-step tasks and agentic workflows), architecture, parameters (560B total, 25B active) and context (1M claimed) are on the card. Training data 15, the training-content summary the card links names the source categories (Common Crawl and other public datasets, commercially licensed data, the Ant Spider crawler, synthetic data), the size band (over 10 trillion text tokens), data collected up to about July 2026 and the filtering, without listing datasets with sizes. Last change 20, the newest commit, on 4 November 2026, edited the card, six days before this check, and the files went up on 27 October. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (both opened on 27 October 2026) has a reply from an inclusionAI member. Version policy 5, the update went into a repository of its own and Ling-3.1-flash and Ling 3.0 stay in theirs. File health 5, the card states a 1M-token context while config.json gives 262,144 with no rope scaling, and its serve commands name the earlier repository as the model path. 11 + 15 + 20 + 0 + 5 + 5 = 56.
Score by category
| Category | Weight this run | BigBang-v1 | Ling-3.1-flash-1019 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 60 | 60 | even |
| Risk | 20%20 | 55 | 45 | BigBang-v1 +10 |
| Agent interface | 10%10 | 60 | 60 | even |
| Documentation and upkeep | 10%10 | 46 | 56 | Ling-3.1-flash-1019 +10 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 56.3/100 | C 54.8/100 |
Facts side by side
| Fact | BigBang-v1 | Ling-3.1-flash-1019 |
|---|---|---|
| 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 | 2026-11-04 |
| 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
Ling-3.1-flash-1019
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.
Ling-3.1-flash-1019
inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has no 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_thinkingfalse in chat_template_kwargs and check the output - For Ollama or llama.cpp you'll need someone else's GGUF; the publisher ships BF16 safetensors only
- Validate JSON output yourself or use the runtime's guided decoding
Ling-3.1-flash-1019
- Serve it with SGLang 0.5.23 or later on 8 H200 or 4 GB300 GPUs, per the card, and use this repository's name as the model path, not the earlier one the card's command shows
- With vLLM pass
--trust-remote-code,--tool-call-parser ling3and--reasoning-parser ling3 - Set
chat_template_kwargsenable_thinkingfalse for direct answers. Reasoning comes back inreasoning_contentper SGLang's cookbook - Plan on 262,144 tokens of context. The 1M figure needs the YaRN override in SGLang's cookbook
- Use temperature 1.0, top_p 0.95 and top_k 20, per the card
- Validate JSON output yourself, since structured output isn't documented
Questions
Which is better for AI agents, BigBang-v1 or Ling-3.1-flash-1019?
BigBang-v1 scores 56.3 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 1 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep.
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Machine-readable
- This page as Markdown
/compare/bigbang-v1-vs-ling-3-1-flash-1019.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/bigbang-v1.json·/api/v1/tools/ling-3-1-flash-1019.json - From a terminal
anchor compare bigbang-v1 ling-3-1-flash-1019(the CLI) - Over MCP
compare_tools {"a": "bigbang-v1", "b": "ling-3-1-flash-1019"}at/mcp, no key