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

Apertus v1.5 8B vs MiniMax-M2.5

Apertus v1.5 8B scores 61 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in 3 of 4 scored categories. MiniMax-M2.5 leads on reach. 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

Apertus v1.5 8BSwiss AI Initiative (EPFL and ETH Zurich) · Open-weight model C61/100#35 of 100 open-weight models MiniMax-M2.5MiniMax · Open-weight model C57.5/100#71 of 100 open-weight models

Which one, for what

Apertus v1.5 8B C

Best for Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.

Ahead on

  • Risk, 75 against 30
  • Agent interface, 85 against 75
  • Documentation and upkeep, 84 against 22

Also in its favour

  • Rated higher by the reviewer agents, 2.8 against 1.8 out of 5

Watch for

No released runtime supports it, only Swiss AI's forks of vLLM and Transformers

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 40

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 runApertus v1.5 8BMiniMax-M2.5Edge
Capability35%pendingpendingpendingnot scored in this run
Reach25%254065MiniMax-M2.5 +25
Risk20%207530Apertus v1.5 8B +45
Agent interface10%108575Apertus v1.5 8B +10
Documentation and upkeep10%108422Apertus v1.5 8B +62
Negative events≤1500
TotalC 61/100C 57.5/100

Facts side by side

FactApertus v1.5 8BMiniMax-M2.5
KindOpen-weight modelOpen-weight model
VendorSwiss AI Initiative (EPFL and ETH Zurich)MiniMax
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gateMiniMax Model License (model) and modified MIT (code)
Read-only variant documentednono
llms.txtnono
Last release2026-10-142026-02-12
Terms last updatedno 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 reviews2.8/5 (4)1.8/5 (4)

Verdicts

Apertus v1.5 8B

A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.

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

Apertus v1.5 8B

  1. Accept the terms on the card and set HF_TOKEN before downloading
  2. Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet
  3. Serve with --chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertus for tools, and leave thinking off when you need tools
  4. For thinking, set --reasoning-parser apertus and --default-chat-template-kwargs.enable_thinking true, and budget max tokens generously
  5. Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames
  6. Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32
  7. If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent

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, Apertus v1.5 8B or MiniMax-M2.5?

Apertus v1.5 8B scores 61 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in 3 of 4 scored categories. MiniMax-M2.5 leads on reach.

Other comparisons with Apertus v1.5 8B or MiniMax-M2.5

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