Head to head · Open-weight models you run yourself · October 2026 research run
BigBang-v1 vs Laguna M.1
Laguna M.1 scores 55.5 (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
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 36
Watch for
BF16 is the only build from the publisher, needing about 86 GB
Best for Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.
Ahead on
- Reach, 65 against 55
- Agent interface, 85 against 60
Watch for
The smallest build needs about 167 GB, more than a 128 GB machine
Score by category
| Category | Weight this run | BigBang-v1 | Laguna M.1 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 55 | 65 | Laguna M.1 +10 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 60 | 85 | Laguna M.1 +25 |
| Documentation and upkeep | 10%10 | 46 | 36 | BigBang-v1 +10 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 54.4/100 | C 55.5/100 |
Facts side by side
| Fact | BigBang-v1 | Laguna M.1 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | The Endless Frontier (endless-frontier) | Poolside |
| 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 | 2026-07-14 |
| Terms last updated | no document linked | |
| Privacy policy last updated | 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) | 2.5/5 (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.
Laguna M.1
A large coding and agent cousin under plain Apache 2.0, for agents with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine, and no local runtime library packages it. The main risks are no upkeep since July, no hosted fallback shown today, no safety results, and repository code on the SGLang route.
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
Laguna M.1
- Run the NVFP4 build on Blackwell GPUs with
vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice, on vLLM 0.21.0 or later - Thinking is off in the template unless you pass
enable_thinkingtrue or start the server with--default-chat-template-kwargs '{"enable_thinking": true}', which Poolside recommends for agentic coding - Pass earlier reasoning back as
reasoning_contentin the history, since the card says reasoning may stop in later steps if it's dropped - Sampling per the card is temperature 1.0 and top_k 20
- The FP8 build's Docker image and serve.sh predate upstream support and set a 4,096-token context, so prefer a released vLLM
- It doesn't fit our 128 GB reference machine at any published precision
- Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
Questions
Which is better for AI agents, BigBang-v1 or Laguna M.1?
Laguna M.1 scores 55.5 (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.
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Machine-readable
- This page as Markdown
/compare/bigbang-v1-vs-laguna-m-1.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/bigbang-v1.json·/api/v1/tools/laguna-m-1.json - From a terminal
anchor compare bigbang-v1 laguna-m-1(the CLI) - Over MCP
compare_tools {"a": "bigbang-v1", "b": "laguna-m-1"}at/mcp, no key