gpt-oss-20b vs Olmo 3 7B Think

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.

Olmo 3 7B Think

A fully open 7B reasoning cousin for maths, code and step-by-step answers, with every dataset and checkpoint published and its safety results rerunnable. Ollama's 4-bit build needs about 5.4 GB. It always thinks, wasn't trained for tool calls, has 65K of context, and Ai2 hasn't answered its discussions.

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

Olmo 3 7B Think

1. Quickest start is ollama run olmo-3:7b-think, about 4.5 GB
2. With vLLM, vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536; vLLM runs it through its Transformers backend
3. Sample at temperature 0.6 and top_p 0.95 with up to 32,768 output tokens, as the card recommends, since thinking traces are long
4. Every reply starts with a think block; strip it yourself or use a reasoning parser that reads think tags
5. Pass your own system prompt; the template's default says the cut-off is November 2024 and that no functions are available
6. For tool calls use Olmo 3 7B Instruct instead, which Ai2 trained for function calling
7. A discussion reports endless reasoning loops when served with vLLM, unanswered, so cap max_tokens
8. Check the use against Ai2's Responsible Use Guidelines, which bar automated social-media posting and undisclosed automated content outside research