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

BigBang-v1 vs Qwen3.5-9B

Qwen3.5-9B scores 60.5 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 2 of 4 scored categories. BigBang-v1 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 2870 open weights comparisons

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

  • Documentation and upkeep, 46 against 34

Watch for

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

Qwen3.5-9B C

Best for Agents that want a small multimodal tool-calling cousin on a laptop or a 16 GB GPU, under a plain licence

Ahead on

  • Reach, 85 against 55
  • Agent interface, 70 against 60

Watch for

No quantised build from Qwen itself

Score by category

CategoryWeight this runBigBang-v1Qwen3.5-9BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%255585Qwen3.5-9B +30
Risk20%205555even
Agent interface10%106070Qwen3.5-9B +10
Documentation and upkeep10%104634BigBang-v1 +12
Negative events≤1500
TotalC 54.4/100C 60.5/100

Facts side by side

FactBigBang-v1Qwen3.5-9B
KindOpen-weight modelOpen-weight model
VendorThe Endless Frontier (endless-frontier)Alibaba (Qwen team)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-08-062026-03-02
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno 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 reviews2.3/5 (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.

Qwen3.5-9B

A small dense vision-language cousin with trained tool calling, switchable thinking and a 262,144-token context, for local agent subtasks. Ollama's 4-bit build needs about 8 GB and the BF16 files about 24 GB, in every major runtime. The main risk is neglect, with no file changes since March, no replies from Qwen to reports and no safety results.

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 a smaller build you'll need someone else's quantisation, the publisher has none
  5. Validate JSON output yourself or use the runtime's guided decoding

Qwen3.5-9B

  1. Smallest start is ollama run qwen3.5:9b (6.6 GB, q4_k_m)
  2. On vLLM run vllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, or tool calls come back as text
  3. Tool calls use the XML style <tool_call><function=name><parameter=x>, not Hermes JSON, so use the qwen3_coder parser and not hermes
  4. Thinking is on by default; pass chat_template_kwargs with enable_thinking false for short tasks, since /nothink isn't supported
  5. There's no generation_config.json, so pass sampling yourself, temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5 when thinking, or 0.7, 0.8, 20, 1.5 without
  6. Keep at least 128K of context when thinking, and turn on YaRN only beyond 262,144 tokens
  7. Validate JSON output yourself or use the runtime's guided decoding

Questions

Which is better for AI agents, BigBang-v1 or Qwen3.5-9B?

Qwen3.5-9B scores 60.5 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 2 of 4 scored categories. BigBang-v1 leads on documentation and upkeep.

Other comparisons with BigBang-v1 or Qwen3.5-9B

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