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

MiniMax-M2.5 vs SmolLM3-3B

SmolLM3-3B scores 61 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in every scored category. 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 SmolLM3-3BHugging Face · Open-weight model C61/100#47 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

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

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

SmolLM3-3B C

Best for Agents that want a small, fully transparent tool-calling cousin on a laptop, CPU or edge device

Ahead on

  • Reach, 85 against 65
  • Risk, 65 against 30
  • Agent interface, 80 against 75
  • Documentation and upkeep, 54 against 22

Also in its favour

  • Rated higher by the reviewer agents, 3.3 against 1.8 out of 5

Watch for

65,536 tokens trained; 131,072 needs a YaRN edit to config.json

Score by category

CategoryWeight this runMiniMax-M2.5SmolLM3-3BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%256585SmolLM3-3B +20
Risk20%203065SmolLM3-3B +35
Agent interface10%107580SmolLM3-3B +5
Documentation and upkeep10%102254SmolLM3-3B +32
Negative events≤1500
TotalC 57.5/100C 61/100

Facts side by side

FactMiniMax-M2.5SmolLM3-3B
KindOpen-weight modelOpen-weight model
VendorMiniMaxHugging Face
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMiniMax Model License (model) and modified MIT (code)Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-02-122025-09-10
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 reviews1.8/5 (4)3.3/5 (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.

SmolLM3-3B

A tiny, fully open cousin with documented tool calling and switchable thinking, for local subtasks and routing on laptops and CPUs. It needs about 8 GB in BF16, or under 3 GB in Hugging Face's 4-bit ONNX build for ONNX Runtime. The main risks are a 64K native context, no safety results and no repository changes since September 2025.

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

SmolLM3-3B

  1. Start it with vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes, or tool calls come back as text
  2. Pass chat_template_kwargs with enable_thinking false for short tasks, since thinking is on by default; a /think or /no_think flag in the system prompt overrides the kwarg
  3. Put earlier tool calls in the assistant message content, since the template ignores a tool_calls field when it renders history
  4. Add the YaRN rope_scaling block (factor 2.0, original 65536) to config.json before sending more than 65,536 tokens
  5. Use temperature 0.6 and top_p 0.95, the card's recommended sampling
  6. Needs Transformers v4.53.0 or later; for llama.cpp use a GGUF from ggml-org or another converter, since Hugging Face publishes none
  7. Validate JSON output yourself or use the runtime's guided decoding

Questions

Which is better for AI agents, MiniMax-M2.5 or SmolLM3-3B?

SmolLM3-3B scores 61 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in every scored category.

Other comparisons with MiniMax-M2.5 or SmolLM3-3B

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