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

MiniMax-M2.5 vs Swift 1.5 Qwen3.8-27B

Swift 1.5 Qwen3.8-27B 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 agent interface. 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

MiniMax-M2.5MiniMax · Open-weight model C57.5/100#71 of 100 open-weight models Swift 1.5 Qwen3.8-27BUkisAI · Open-weight model C61/100#49 of 100 open-weight models

Which one, for what

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

  • 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

Swift 1.5 Qwen3.8-27B C

Best for Agents on a 24 to 32 GB GPU or Mac that want Qwen3.8-27B's skills with shorter thinking, for teams under US$1M revenue or research.

Ahead on

  • Reach, 80 against 65
  • Risk, 50 against 30
  • Documentation and upkeep, 43 against 22

Also in its favour

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

Watch for

Commercial use above US$1M gross annual revenue needs a separate licence

Score by category

CategoryWeight this runMiniMax-M2.5Swift 1.5 Qwen3.8-27BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%256580Swift 1.5 Qwen3.8-27B +15
Risk20%203050Swift 1.5 Qwen3.8-27B +20
Agent interface10%107560MiniMax-M2.5 +15
Documentation and upkeep10%102243Swift 1.5 Qwen3.8-27B +21
Negative events≤1500
TotalC 57.5/100C 61/100

Facts side by side

FactMiniMax-M2.5Swift 1.5 Qwen3.8-27B
KindOpen-weight modelOpen-weight model
VendorMiniMaxUkisAI
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMiniMax Model License (model) and modified MIT (code)Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue with a separate Swift Enterprise License above it. Built on Qwen3.8-27B, whose Apache 2.0 still governs the base
Read-only variant documentednono
llms.txtnono
Last release2026-02-122026-09-24
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 reviews1.8/5 (4)2.3/5 (4)

Verdicts

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.

Swift 1.5 Qwen3.8-27B

A Qwen3.8-27B cousin post-trained to think in fewer tokens, for coding and agent work with images in, that fits a 24 GB machine with UkisAI's own 4-bit builds. Commercial use is free only below US$1M revenue. The main risks are that nobody at UkisAI answers its discussions, and that the shorter thinking rests on UkisAI's own benchmarks.

Before you call either

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

Swift 1.5 Qwen3.8-27B

  1. Quickest start is llama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M --jinja, about 18.4 GB with the vision projector
  2. On our reference machine BF16 fits, vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder
  3. The template is Qwen3.8-27B's, so chat_template_kwargs with reasoning_effort low, medium or xhigh (default) and enable_thinking false work as on the base
  4. UkisAI benchmarked it at temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 0, which is a reasonable starting point
  5. The MLX 4-bit build needs UkisAI's patched MLX-LM and has no image or video chat
  6. Check the licence before commercial use, it stops at US$1M gross annual revenue including affiliates
  7. MTP self-speculative decoding is available with the base's head, flags on the card

Questions

Which is better for AI agents, MiniMax-M2.5 or Swift 1.5 Qwen3.8-27B?

Swift 1.5 Qwen3.8-27B 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 agent interface.

Other comparisons with MiniMax-M2.5 or Swift 1.5 Qwen3.8-27B

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