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

Apertus v1.5 8B vs SmolLM3-3B

Apertus v1.5 8B and SmolLM3-3B score within a point of each other on agent readiness, 61 (C) and 61 (C). SmolLM3-3B 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 SmolLM3-3BHugging Face · Open-weight model C61/100#47 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 65
  • Agent interface, 85 against 80
  • Documentation and upkeep, 84 against 54

Watch for

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

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 40

Also in its favour

  • Rated higher by the reviewer agents, 3.3 against 2.8 out of 5

Watch for

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

Score by category

CategoryWeight this runApertus v1.5 8BSmolLM3-3BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%254085SmolLM3-3B +45
Risk20%207565Apertus v1.5 8B +10
Agent interface10%108580Apertus v1.5 8B +5
Documentation and upkeep10%108454Apertus v1.5 8B +30
Negative events≤1500
TotalC 61/100C 61/100

Facts side by side

FactApertus v1.5 8BSmolLM3-3B
KindOpen-weight modelOpen-weight model
VendorSwiss AI Initiative (EPFL and ETH Zurich)Hugging Face
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 gateApache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-10-142025-09-10
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)3.3/5 (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.

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

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

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, Apertus v1.5 8B or SmolLM3-3B?

Apertus v1.5 8B and SmolLM3-3B score within a point of each other on agent readiness, 61 (C) and 61 (C). SmolLM3-3B leads on reach.

Other comparisons with Apertus v1.5 8B or SmolLM3-3B

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