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

gpt-oss-20b vs SmolLM3-3B

gpt-oss-20b scores 69.9 (B) on agent readiness against SmolLM3-3B's 61 (C), and leads in 3 of 4 scored categories. SmolLM3-3B leads on documentation and upkeep. 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

gpt-oss-20bOpenAI · Open-weight model B69.9/100#8 of 100 open-weight models SmolLM3-3BHugging Face · Open-weight model C61/100#47 of 100 open-weight models

Which one, for what

gpt-oss-20b B

Best for Local agents on a 24 GB GPU or Mac that want a permissive licence, wide runtime support and documented structured output, one tool call at a time

Ahead on

  • Reach, 90 against 85
  • Risk, 85 against 65
  • Agent interface, 90 against 80

Watch for

No reply from OpenAI to any discussion since August 2025, and template reports wait unanswered

SmolLM3-3B C

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

Ahead on

  • Documentation and upkeep, 54 against 42

Watch for

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

Score by category

CategoryWeight this rungpt-oss-20bSmolLM3-3BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%259085gpt-oss-20b +5
Risk20%208565gpt-oss-20b +20
Agent interface10%109080gpt-oss-20b +10
Documentation and upkeep10%104254SmolLM3-3B +12
Negative events≤1500
TotalB 69.9/100C 61/100

Facts side by side

Factgpt-oss-20bSmolLM3-3B
KindOpen-weight modelOpen-weight model
VendorOpenAIHugging Face
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2025-08-262025-09-10
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 reviews3.5/5 (4)3.3/5 (4)

Verdicts

gpt-oss-20b

gpt-oss-20b is a cousin for reasoning, coding and single tool calls with structured output, under plain Apache 2.0. It needs about 17 GB with a working context, and five inference providers serve it. The main risks are that it trails o4-mini at keeping system instructions above user ones, and OpenAI hasn't answered a report since August 2025.

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

gpt-oss-20b

  1. Start it with ollama run gpt-oss:20b (the build that fits the reference machine), or vllm serve openai/gpt-oss-20b with a current vLLM release rather than the card's pinned vllm==0.10.1+gptoss wheel
  2. Use the harmony format through the chat template or openai-harmony; OpenAI says the model doesn't work correctly without it
  3. Parse tool calls and reasoning with openai-harmony; calls come on the commentary channel ending in <|call|>, reasoning on the analysis channel
  4. Set effort with Reasoning: high (or low, medium) in the system message, or reasoning_effort in the template; medium is the default
  5. Ask for JSON by putting a JSON Schema under # Response Formats in the developer message
  6. Drop earlier reasoning after a final answer but pass it back after tool calls, and don't show the analysis channel to users
  7. Expect one tool call per turn; run several in sequence rather than in parallel

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, gpt-oss-20b or SmolLM3-3B?

gpt-oss-20b scores 69.9 (B) on agent readiness against SmolLM3-3B's 61 (C), and leads in 3 of 4 scored categories. SmolLM3-3B leads on documentation and upkeep.

Other comparisons with gpt-oss-20b or SmolLM3-3B

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