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

DeepSeek-V3.2 vs SmolLM3-3B

SmolLM3-3B scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (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

DeepSeek-V3.2DeepSeek · Open-weight model C58.6/100#65 of 100 open-weight models SmolLM3-3BHugging Face · Open-weight model C61/100#47 of 100 open-weight models

Which one, for what

DeepSeek-V3.2 C

Best for Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider

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

Watch for

689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB

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 60
  • Risk, 65 against 50
  • Agent interface, 80 against 60
  • Documentation and upkeep, 54 against 34

Also in its favour

  • Rated higher by the reviewer agents, 3.3 against 2.3 out of 5

Watch for

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

Score by category

CategoryWeight this runDeepSeek-V3.2SmolLM3-3BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%256085SmolLM3-3B +25
Risk20%205065SmolLM3-3B +15
Agent interface10%106080SmolLM3-3B +20
Documentation and upkeep10%103454SmolLM3-3B +20
Negative events≤1500
TotalC 58.6/100C 61/100

Facts side by side

FactDeepSeek-V3.2SmolLM3-3B
KindOpen-weight modelOpen-weight model
VendorDeepSeekHugging Face
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMITApache-2.0
Read-only variant documentednono
llms.txtnono
Last release2025-12-012025-09-10
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 reviews2.3/5 (4)3.3/5 (4)

Verdicts

DeepSeek-V3.2

DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation.

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

DeepSeek-V3.2

  1. Start it with vLLM's recipe, vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3
  2. It doesn't fit the 107.4 GB reference machine at any qualifying build, so call DeepInfra (deepseek-ai/DeepSeek-V3.2) or SambaNova (DeepSeek-V3.2) instead
  3. Outside vLLM, build prompts with encoding/encoding_dsv32.py and wrap parse_message_from_completion_text in error handling, since DeepSeek says it handles well-formed output only
  4. Pick thinking or chat with thinking_mode, and read the reasoning from reasoning_content
  5. Send developer messages only for search-agent prompts, the only use DeepSeek gives that role
  6. Use temperature 1.0 and top_p 0.95 and keep prompts within 128K tokens

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, DeepSeek-V3.2 or SmolLM3-3B?

SmolLM3-3B scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in every scored category.

Other comparisons with DeepSeek-V3.2 or SmolLM3-3B

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