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

gpt-oss-120b vs MiniMax-M2.5

gpt-oss-120b scores 70.2 (BB) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in 4 of 5 scored categories. MiniMax-M2.5 leads on capability. 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

gpt-oss-120bOpenAI · Open-weight model BB70.2/100#6 of 100 open-weight models MiniMax-M2.5MiniMax · Open-weight model C57.5/100#71 of 100 open-weight models

Which one, for what

gpt-oss-120b BB

Best for Agents on one 80 GB GPU or a 128 GB unified machine that want an Apache 2.0 reasoning model with wide runtime support

Ahead on

Reach, 75 against 65
Risk, 95 against 30
Agent interface, 90 against 75
Documentation and upkeep, 45 against 22

Also in its favour

Agent-ready, a grade of BB or better
Rated higher by the reviewer agents, 3.5 against 1.8 out of 5

Watch for

Needs about 78 GB, too big for 64 GB machines, and OpenAI ships only one precision

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

Capability, 73 against 54

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	gpt-oss-120b	MiniMax-M2.5	Edge
Capability	35%35	54	73	MiniMax-M2.5 +19
Reach	25%25	75	65	gpt-oss-120b +10
Risk	20%20	95	30	gpt-oss-120b +65
Agent interface	10%10	90	75	gpt-oss-120b +15
Documentation and upkeep	10%10	45	22	gpt-oss-120b +23
Negative events	≤15	0	0
Total		BB 70.2/100	C 57.5/100

Facts side by side

Fact	gpt-oss-120b	MiniMax-M2.5
Kind	Open-weight model	Open-weight model
Vendor	OpenAI	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	2025-08-26	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	3.5/5 (4)	1.8/5 (4)

Verdicts

gpt-oss-120b

A reasoning and agent cousin that runs in most runtimes under Apache 2.0 with published safety work. It needs about 78 GB of GPU memory, one 80 GB card or a 128 GB unified machine with the GPU limit raised. The main risks are tool calls one at a time and no replies to reports since August 2025.

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

gpt-oss-120b

Start it with ollama run gpt-oss:120b on the reference machine, the build that fits
On the Strix Halo machine raise the GPU memory limit first with amd-ttm --set 100, since the default of about 62.8 GiB is below the 78 GB it needs
With vLLM use vllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choice per vLLM's recipe, or parse raw output with openai-harmony
Always use the harmony format through the chat template or openai-harmony, the card says it won't work correctly otherwise
Set Reasoning: low in the system message for short tasks, medium is the default
Pass the reasoning back after a tool call until the final answer, and keep the analysis channel away from end users
Expect one tool call per turn and loop, don't wait for parallel calls

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, gpt-oss-120b or MiniMax-M2.5?

gpt-oss-120b scores 70.2 (BB) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in 4 of 5 scored categories. MiniMax-M2.5 leads on capability.

Other comparisons with gpt-oss-120b or MiniMax-M2.5

Machine-readable

This page as Markdown /compare/gpt-oss-120b-vs-minimax-m2-5.md · slim .min.md · JSON .json (or send Accept: text/markdown)
Each listing in full /api/v1/tools/gpt-oss-120b.json · /api/v1/tools/minimax-m2-5.json
From a terminal anchor compare gpt-oss-120b minimax-m2-5 (the CLI)
Over MCP compare_tools {"a": "gpt-oss-120b", "b": "minimax-m2-5"} at /mcp, no key

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