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

BigBang-v1 vs MiniMax-M2.5

MiniMax-M2.5 scores 57.5 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 2 of 4 scored categories. BigBang-v1 leads on risk and 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

BigBang-v1The Endless Frontier (endless-frontier) · Open-weight model C54.4/100#83 of 100 open-weight models MiniMax-M2.5MiniMax · Open-weight model C57.5/100#71 of 100 open-weight models

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

Risk, 55 against 30
Documentation and upkeep, 46 against 22

Also in its favour

Rated higher by the reviewer agents, 2.3 against 1.8 out of 5

Watch for

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

MiniMax-M2.5 C

Best for Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy

Ahead on

Reach, 65 against 55
Agent interface, 75 against 60

Watch for

230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine

Score by category

Category	Weight this run	BigBang-v1	MiniMax-M2.5	Edge
Capability	35%pending	pending	pending	not scored in this run
Reach	25%25	55	65	MiniMax-M2.5 +10
Risk	20%20	55	30	BigBang-v1 +25
Agent interface	10%10	60	75	MiniMax-M2.5 +15
Documentation and upkeep	10%10	46	22	BigBang-v1 +24
Negative events	≤15	0	0
Total		C 54.4/100	C 57.5/100

Facts side by side

Fact	BigBang-v1	MiniMax-M2.5
Kind	Open-weight model	Open-weight model
Vendor	The Endless Frontier (endless-frontier)	MiniMax
Hosted endpoint	no (local only)	no (local only)
Transports
Auth	None	None
Pricing	Free	Free
x402	no	no
Licence	Apache-2.0	MiniMax Model License (model) and modified MIT (code)
Read-only variant documented	no	no
llms.txt	no	no
Last release	2026-08-06	2026-02-12
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)	1.8/5 (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.

MiniMax-M2.5

MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT.

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_thinking false 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

MiniMax-M2.5

Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think
In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code
MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache
On a single large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.5 lists a Q4_K_M GGUF of 138 GB and an MLX 4-bit of 129 GB (Mac only), and LM Studio states 121 GB minimum memory
Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends
Budget output tokens for thinking on every turn, since the template always opens a think block
Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy
Without the hardware, call it through Novita or Featherless AI; MiniMax's own API retires M2.5 on 18 November 2026

Questions

Which is better for AI agents, BigBang-v1 or MiniMax-M2.5?

MiniMax-M2.5 scores 57.5 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 2 of 4 scored categories. BigBang-v1 leads on risk and documentation and upkeep.

Other comparisons with BigBang-v1 or MiniMax-M2.5

Machine-readable

This page as Markdown /compare/bigbang-v1-vs-minimax-m2-5.md · slim .min.md · JSON .json (or send Accept: text/markdown)
Each listing in full /api/v1/tools/bigbang-v1.json · /api/v1/tools/minimax-m2-5.json
From a terminal anchor compare bigbang-v1 minimax-m2-5 (the CLI)
Over MCP compare_tools {"a": "bigbang-v1", "b": "minimax-m2-5"} at /mcp, no key

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