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

gpt-oss-120b vs SmolLM3-3B

gpt-oss-120b scores 70.2 (BB) on agent readiness against SmolLM3-3B's 61 (C), and leads in 2 of 4 scored categories. SmolLM3-3B leads on reach and 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-120bOpenAI · Open-weight model BB70.2/100#6 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-120b BB

Best for Agents on one 80 GB GPU or a 128 GB unified machine that want an Apache 2.0 reasoning model with wide runtime support

Ahead on

  • Risk, 95 against 65
  • Agent interface, 90 against 80

Also in its favour

  • Agent-ready, a grade of BB or better

Watch for

Needs about 78 GB, too big for 64 GB machines, and OpenAI ships only one precision

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 75
  • Documentation and upkeep, 54 against 45

Watch for

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

Score by category

CategoryWeight this rungpt-oss-120bSmolLM3-3BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%257585SmolLM3-3B +10
Risk20%209565gpt-oss-120b +30
Agent interface10%109080gpt-oss-120b +10
Documentation and upkeep10%104554SmolLM3-3B +9
Negative events≤1500
TotalBB 70.2/100C 61/100

Facts side by side

Factgpt-oss-120bSmolLM3-3B
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-120b

A reasoning and agent cousin that runs in most runtimes under Apache 2.0 with published safety work. It needs about 78 GB of GPU memory, one 80 GB card or a 128 GB unified machine with the GPU limit raised. The main risks are tool calls one at a time and no replies to reports 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-120b

  1. Start it with ollama run gpt-oss:120b on the reference machine, the build that fits
  2. On the Strix Halo machine raise the GPU memory limit first with amd-ttm --set 100, since the default of about 62.8 GiB is below the 78 GB it needs
  3. With vLLM use vllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choice per vLLM's recipe, or parse raw output with openai-harmony
  4. Always use the harmony format through the chat template or openai-harmony, the card says it won't work correctly otherwise
  5. Set Reasoning: low in the system message for short tasks, medium is the default
  6. Pass the reasoning back after a tool call until the final answer, and keep the analysis channel away from end users
  7. Expect one tool call per turn and loop, don't wait for parallel calls

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-120b or SmolLM3-3B?

gpt-oss-120b scores 70.2 (BB) on agent readiness against SmolLM3-3B's 61 (C), and leads in 2 of 4 scored categories. SmolLM3-3B leads on reach and documentation and upkeep.

Other comparisons with gpt-oss-120b or SmolLM3-3B

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