OpenAI: gpt-oss-20b (free)

Review this Python function for potential bugs. It's supposed to merge two sorted lists but users report occasional incorrect ordering in the output.

The function assumes both input lists are already sorted, but doesn't validate this precondition. If either list contains unsorted elements, the merge logic will preserve that disorder. Additionally, the comparison operator on line 7 uses `>` instead of `>=`, which means equal elements from the second list always appear before those from the first list—this breaks stability. Consider adding input validation and switching to `>=` to maintain stable merge behavior when elements are equal.

This example shows the model's code review capability with a 131K token context window—useful for analyzing entire codebases in one prompt. As a free tier model, response speed may vary under load, and the lack of public benchmarks means quality on complex reasoning tasks is unverified compared to paid alternatives.

Summarize the key regulatory changes in this 40-page compliance document, focusing on what our engineering team needs to implement by Q2.

The document introduces three main changes: (1) Data retention policies now require 90-day audit logs for all API requests involving personal data, with specific fields mandated in section 4.2. (2) New encryption standards (AES-256-GCM minimum) apply to data at rest by March 31st. (3) Third-party data processors must complete certification by April 15th—this affects your current analytics vendor. The engineering team should prioritize the logging infrastructure first, as it has the longest implementation timeline and affects multiple services.

The 131K token context window handles long documents without chunking, making this model suitable for policy analysis and technical documentation review. Being free means no per-token cost for large document ingestion, though output quality on nuanced legal interpretation hasn't been independently benchmarked.

Generate a customer support response template for users reporting slow dashboard load times. Keep it empathetic but technical enough to gather useful debugging info.

Thanks for reporting the slow dashboard performance—we know how frustrating that is when you're trying to get work done. To help us investigate, could you share: (1) Which browser and version you're using, (2) Approximately how many projects are in your workspace, (3) Whether the slowness happens on initial load or when switching between views, and (4) Your approximate location or timezone. In the meantime, try clearing your browser cache or switching to a different browser as a temporary workaround. We'll prioritize this based on what we learn from your setup.

This demonstrates the model's ability to balance tone and technical precision in customer-facing content. The free pricing makes it viable for high-volume support template generation. However, without benchmark data, consistency across different writing styles and domains remains untested compared to established paid models.

When free inference makes sense for early-stage product testing

A 3-person startup building a customer support chatbot needs to run 200+ test conversations daily without burning runway on API costs. gpt-oss-20b at $0.00/Mtok lets you iterate on prompt engineering, test conversation flows, and validate your product hypothesis before committing to paid inference. The 131k context window handles full support ticket histories without truncation. The trade-off: no public benchmarks means you're flying blind on quality until you test it yourself. If your prototype shows traction and you need predictable response quality, budget for a switch to GPT-4o-mini or Claude Haiku. Use this model to prove the concept exists, not to ship production traffic.

Free tier for academic work with no budget constraints

A university team building a research paper summarizer for their capstone project has zero budget and needs to process 50-100 papers per week. gpt-oss-20b gives them unlimited inference to experiment with summarization prompts and compare outputs across different paper types. The 131k window fits most academic papers in a single call. The risk: without benchmark data, you can't cite model performance in your final report or compare it to published baselines. If your project needs reproducible results or you're publishing findings, switch to a benchmarked model like Llama 3.1 8B where you can reference MMLU scores. For exploratory coursework where the process matters more than the metrics, this model removes the cost barrier entirely.

Why zero-cost inference is the wrong call for compliance work

A 12-person law firm wants to use AI for contract redlining to cut paralegal hours. gpt-oss-20b's free pricing looks attractive, but the absence of public benchmarks means you have no third-party validation of accuracy on legal reasoning tasks. Contract errors cost more than API fees—missing a liability clause in a $2M deal erases years of savings. Models like Claude 3.5 Sonnet score 88.7% on the GPQA benchmark (graduate-level reasoning) and cost $3/Mtok input, giving you auditable performance data and vendor SLAs. Use free models for internal meeting notes or draft emails, but any document that touches client risk or regulatory compliance needs a benchmarked model with a support contract behind it.