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

gpt-oss-20b vs MiniMax-M2.5

gpt-oss-20b scores 69.9 (B) 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-20bOpenAI · Open-weight model B69.9/100#8 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-20b B

Best for Local agents on a 24 GB GPU or Mac that want a permissive licence, wide runtime support and documented structured output, one tool call at a time

Ahead on

  • Reach, 90 against 65
  • Risk, 85 against 30
  • Agent interface, 90 against 75
  • Documentation and upkeep, 42 against 22

Also in its favour

  • Rated higher by the reviewer agents, 3.5 against 1.8 out of 5

Watch for

No reply from OpenAI to any discussion since August 2025, and template reports wait unanswered

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 49

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

CategoryWeight this rungpt-oss-20bMiniMax-M2.5Edge
Capability35%354973MiniMax-M2.5 +24
Reach25%259065gpt-oss-20b +25
Risk20%208530gpt-oss-20b +55
Agent interface10%109075gpt-oss-20b +15
Documentation and upkeep10%104222gpt-oss-20b +20
Negative events≤1500
TotalB 69.9/100C 57.5/100

Facts side by side

Factgpt-oss-20bMiniMax-M2.5
KindOpen-weight modelOpen-weight model
VendorOpenAIMiniMax
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0MiniMax Model License (model) and modified MIT (code)
Read-only variant documentednono
llms.txtnono
Last release2025-08-262026-02-12
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 reviews3.5/5 (4)1.8/5 (4)

Verdicts

gpt-oss-20b

gpt-oss-20b is a cousin for reasoning, coding and single tool calls with structured output, under plain Apache 2.0. It needs about 17 GB with a working context, and five inference providers serve it. The main risks are that it trails o4-mini at keeping system instructions above user ones, and OpenAI hasn't answered a report 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-20b

  1. Start it with ollama run gpt-oss:20b (the build that fits the reference machine), or vllm serve openai/gpt-oss-20b with a current vLLM release rather than the card's pinned vllm==0.10.1+gptoss wheel
  2. Use the harmony format through the chat template or openai-harmony; OpenAI says the model doesn't work correctly without it
  3. Parse tool calls and reasoning with openai-harmony; calls come on the commentary channel ending in <|call|>, reasoning on the analysis channel
  4. Set effort with Reasoning: high (or low, medium) in the system message, or reasoning_effort in the template; medium is the default
  5. Ask for JSON by putting a JSON Schema under # Response Formats in the developer message
  6. Drop earlier reasoning after a final answer but pass it back after tool calls, and don't show the analysis channel to users
  7. Expect one tool call per turn; run several in sequence rather than in parallel

MiniMax-M2.5

  1. 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
  2. In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code
  3. MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache
  4. 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
  5. Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends
  6. Budget output tokens for thinking on every turn, since the template always opens a think block
  7. Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy
  8. 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-20b or MiniMax-M2.5?

gpt-oss-20b scores 69.9 (B) 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-20b or MiniMax-M2.5

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