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

BigBang-v1 vs Qwen3-Coder-30B-A3B-Instruct

Qwen3-Coder-30B-A3B-Instruct scores 55.7 (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 31

Watch for

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

Qwen3-Coder-30B-A3B-Instruct C

Best for Coding agents and local coding tools that want a fast, capable open model on one 24 GB GPU or a 32 GB Mac, with a plain licence.

Ahead on

  • Reach, 90 against 55
  • Agent interface, 75 against 60

Also in its favour

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

Watch for

No safety results, prompt-injection guidance or security contact, though it's meant to run commands

Score by category

CategoryWeight this runBigBang-v1Qwen3-Coder-30B-A3B-InstructEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%255590Qwen3-Coder-30B-A3B-Instruct +35
Risk20%205555even
Agent interface10%106075Qwen3-Coder-30B-A3B-Instruct +15
Documentation and upkeep10%104631BigBang-v1 +15
Negative events≤1500
TotalC 54.4/100C 55.7/100

Facts side by side

FactBigBang-v1Qwen3-Coder-30B-A3B-Instruct
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 release2026-08-062025-12-03
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)2.8/5 (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.

Qwen3-Coder-30B-A3B-Instruct

A fast coding cousin, with 3.3B of 30.5B parameters active per token, for agentic coding tools, repository reading and fill-in-the-middle without a thinking step. It needs about 21 to 23 GB at 4 bits, and several providers host it. The main risk is that it runs commands for agents with no safety results or guidance, and Alibaba doesn't answer users.

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-Coder-30B-A3B-Instruct

  1. Quickest start is ollama run qwen3-coder:30b, 19 GB
  2. On our reference machine use BF16 with vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder, 61 GB of weights fits the GPU share
  3. Use the qwen3_coder parser in vLLM or SGLang, not hermes, since calls come back as function and parameter blocks rather than JSON
  4. Pass tool-call arguments in the history as objects, the template iterates them as a mapping
  5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and repetition_penalty 1.05, with up to 65,536 output tokens
  6. For code completion use the fill-in-the-middle tokens <|fim_prefix|>, <|fim_suffix|> and <|fim_middle|> from Qwen's README
  7. Sandbox the commands and files it touches, since nothing from Alibaba covers prompt injection

Questions

Which is better for AI agents, BigBang-v1 or Qwen3-Coder-30B-A3B-Instruct?

Qwen3-Coder-30B-A3B-Instruct scores 55.7 (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-Coder-30B-A3B-Instruct

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