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

Darwin-36B-Opus vs SmolLM3-3B

Darwin-36B-Opus and SmolLM3-3B score within a point of each other on agent readiness, 61 (C) and 61 (C). SmolLM3-3B leads on reach, risk and agent interface. 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

Darwin-36B-OpusFINAL-Bench (VIDRAFT) · Open-weight model C61/100#37 of 100 open-weight models SmolLM3-3BHugging Face · Open-weight model C61/100#47 of 100 open-weight models

Which one, for what

Darwin-36B-Opus C

Best for Reasoning-heavy text tasks on a 32 GB machine or a single 80 GB GPU, where a disclosed training method isn't required

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

Watch for

The merge method is proprietary and the current weights include an undescribed targeted refinement

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 80
  • Risk, 65 against 55
  • Agent interface, 80 against 70

Also in its favour

  • Rated higher by the reviewer agents, 3.3 against 2.5 out of 5

Watch for

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

Score by category

CategoryWeight this runDarwin-36B-OpusSmolLM3-3BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%258085SmolLM3-3B +5
Risk20%205565SmolLM3-3B +10
Agent interface10%107080SmolLM3-3B +10
Documentation and upkeep10%105154SmolLM3-3B +3
Negative events≤1500
TotalC 61/100C 61/100

Facts side by side

FactDarwin-36B-OpusSmolLM3-3B
KindOpen-weight modelOpen-weight model
VendorFINAL-Bench (VIDRAFT)Hugging Face
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-04-232025-09-10
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 reviews2.5/5 (4)3.3/5 (4)

Verdicts

Darwin-36B-Opus

A text-only reasoning cousin merged from Qwen3.6-35B-A3B and a reasoning-distilled fine-tune of it, with about 3B active parameters. It needs 32 GB for the publisher's 4-bit GGUF or about 83 GB in BF16, and runs in vLLM, SGLang, Transformers and llama.cpp. The main risks are an undisclosed merge method, a card that disagrees with its files, and no safety results.

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

Darwin-36B-Opus

  1. Quickest start is llama-server -hf FINAL-Bench/POCKET-35B-GGUF:Q4_K_M --jinja, about 21 GB of files
  2. On the reference machine BF16 fits, vllm serve FINAL-Bench/Darwin-36B-Opus --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, the parsers for Qwen3.6's format
  3. Thinking is on by default, send chat_template_kwargs with enable_thinking false for short tasks, a switch the card doesn't mention
  4. The card suggests temperature 0.6 to 0.7 and at least 5,120 new tokens for its think traces
  5. generation_config.json stops only on the end-of-text token, so add <|im_end|> as a stop token if a runtime keeps generating after the turn ends
  6. Text only, the vision encoder of Qwen3.6-35B-A3B isn't in these files
  7. Validate JSON output yourself, since structured output isn't documented

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, Darwin-36B-Opus or SmolLM3-3B?

Darwin-36B-Opus and SmolLM3-3B score within a point of each other on agent readiness, 61 (C) and 61 (C). SmolLM3-3B leads on reach, risk and agent interface.

Other comparisons with Darwin-36B-Opus or SmolLM3-3B

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