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
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
| Category | Weight this run | BigBang-v1 | Qwen3-Coder-30B-A3B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 55 | 90 | Qwen3-Coder-30B-A3B-Instruct +35 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 60 | 75 | Qwen3-Coder-30B-A3B-Instruct +15 |
| Documentation and upkeep | 10%10 | 46 | 31 | BigBang-v1 +15 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 54.4/100 | C 55.7/100 |
Facts side by side
| Fact | BigBang-v1 | Qwen3-Coder-30B-A3B-Instruct |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | The Endless Frontier (endless-frontier) | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-06 | 2025-12-03 |
| 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.
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
- 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 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
Qwen3-Coder-30B-A3B-Instruct
- Quickest start is
ollama run qwen3-coder:30b, 19 GB - 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 - Use the qwen3_coder parser in vLLM or SGLang, not hermes, since calls come back as function and parameter blocks rather than JSON
- Pass tool-call arguments in the history as objects, the template iterates them as a mapping
- 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
- For code completion use the fill-in-the-middle tokens
<|fim_prefix|>,<|fim_suffix|>and<|fim_middle|>from Qwen's README - 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.
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Machine-readable
- This page as Markdown
/compare/bigbang-v1-vs-qwen3-coder-30b-a3b-instruct.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/bigbang-v1.json·/api/v1/tools/qwen3-coder-30b-a3b-instruct.json - From a terminal
anchor compare bigbang-v1 qwen3-coder-30b-a3b-instruct(the CLI) - Over MCP
compare_tools {"a": "bigbang-v1", "b": "qwen3-coder-30b-a3b-instruct"}at/mcp, no key