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There is no defensible universal winner. Public tests measure different prompts, model versions, interfaces and definitions of “unnecessary refusal.” Anthropic reports exceptionally low over-refusal for Claude Opus 4.6 on one difficult benign-request test; Google describes Gemini 3.7 Flash as having low unjustified refusals; and a Columbia Global Freedom of Expression study found Grok 4 engaged with every prompt in its own contested-topic set. Those results cannot be merged into a single all-purpose ranking.
The practical answer is to match a chatbot to your type of work, then test the exact interface and prompts you rely on. A useful service should answer legitimate sensitive questions with context and limits, while still declining requests that would enable real harm.
What an unnecessary refusal is—and what it is not
An unnecessary refusal is a blanket “I can’t help with that” response to a legitimate request that could have been answered safely. The model might instead have provided background information, a high-level explanation, a legal or ethical caveat, or a limited answer that omitted dangerous operational details.
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This is different from an appropriate refusal. A request to facilitate violence, malware deployment, biological harm or another serious wrongdoing may require the system to decline. The quality question is not whether a chatbot ever says no; it is whether its safety boundary distinguishes harmful assistance from lawful discussion, education and analysis.
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Anthropic states the principle clearly: “AI assistants need to decline truly harmful requests while still being able to respond to legitimate questions, even when those questions involve sensitive topics.” That standard is more useful than looking for a chatbot that simply refuses as little as possible.
What the latest published evidence shows
Claude Opus 4.6: the strongest provider-published benign-prompt figure
Anthropic’s 2026 Transparency Hub reports a 0.04% over-refusal rate for Claude Opus 4.6 on its higher-difficulty benign-request evaluation. On the same Anthropic evaluation, the reported rates are 8.50% for Claude Sonnet 4.5 and 6.01% for Claude Haiku 4.5.
These figures are useful evidence about Anthropic’s own models under Anthropic’s test design. They are not directly comparable with a competitor’s percentage unless the competitor uses the same prompts, scoring rules, model version and product surface. The result also does not establish how Opus behaves in every consumer interface, region or account tier.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsAnthropic also reported that Claude 3.7 Sonnet reduced unnecessary refusals by 45% in standard mode and 31% in extended-thinking mode against an earlier comparison. The Transparency Hub identifies that model as retired, so those historical improvements should not be treated as a recommendation for a currently available chatbot.
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Gemini 3.7 Flash: Google reports low unjustified refusals
Google DeepMind’s Gemini 3.7 Flash model card says the model performs similarly to Gemini 3.6 Flash across safety and tone, with low unjustified refusals. Google defines its unjustified-refusal evaluation around borderline prompts: the model should respond while remaining safe.
Google characterizes the result as “low,” rather than publishing a cross-provider league table. The model card says the evaluations are automated, the test set is being refined, and the improved evaluation is not directly comparable with earlier model-card results. Treat the statement as evidence of Google’s internal comparison, not proof that Gemini refuses less often than every other service.
Grok 4 and other models in a contested-topic study
Columbia Global Freedom of Expression’s 2025 report used a shared set of prompts involving argument generation and social-media-style content on contested topics. In that particular test, Grok 4 accepted all 64 prompts. Mistral Medium 3.1 and Llama 4 responded to 95%; Gemini 2.5 Flash engaged with more than nine in ten; GPT-5 engaged with 80%; Claude Sonnet 4 accepted 73%; DeepSeek V3.1 responded to 80% overall; and Alibaba’s Qwen3-235B-A22B had a 53% overall acceptance rate.
| Model | Result in Columbia’s 2025 contested-topic test | What the result means |
|---|---|---|
| Grok 4 | Accepted all 64 prompts | Highest engagement in that specific prompt set |
| Mistral Medium 3.1 | 95% response rate | High willingness to engage with the tested prompts |
| Llama 4 | 95% response rate | High willingness to engage with the tested prompts |
| Gemini 2.5 Flash | More than 90% engagement | Strong engagement in this contested-topic sample |
| GPT-5 | 80% engagement | Engagement measured for this report’s prompt set |
| DeepSeek V3.1 | 80% overall response rate | Overall result reported by the study |
| Claude Sonnet 4 | 73% acceptance rate | Acceptance measured for this report’s prompt set |
| Qwen3-235B-A22B | 53% overall acceptance rate | Lowest listed rate in this study’s set |
The report is evidence about lawful but controversial discourse, not ordinary chatbot use in general. It also found that models were generally more willing to produce abstract arguments than user-framed social-media content. All refusals of abstract “arguments” prompts by the tested China-headquartered providers concerned China-sensitive topics. Those details make the test informative, but they also show why “Grok refuses least” would be an overstatement.
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OpenAI’s evaluations illustrate the importance of context
The OpenAI Operator System Card reports not_overrefuse scores of 55% for Operator and 90% for GPT-4o on its standard refusal evaluation. Operator is an agentic system with its own tools, risks and interventions; the result should not be presented as a current ChatGPT score or as a general ranking of OpenAI models.
OpenAI’s GPT-5 System Card describes “safe completions” as an alternative to a brittle yes-or-no refusal boundary, especially for dual-use biology and cybersecurity questions. The approach aims to provide the safe portion of an answer while withholding details that could enable harm. For users, this means answer quality should include how well a model narrows a response, not just whether it says yes.
RefusalBench: why a single percentage can mislead
The 2026 RefusalBench preprint examined biological-research prompts across 19 frontier models in a May 2026 snapshot. It reports strict refusal rates ranging from 0.1% to 94.6% on identical prompts and argues that strict refusal rate alone can misrank safety calibration. Some models use hedged partial-compliance responses that a binary refusal metric does not capture.
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Why a universal refusal ranking does not hold up
- Different prompt sets: A benign-request benchmark, a contested-politics test and a biological-research test measure different behaviors.
- Different definitions: One evaluator may count only a full refusal; another may penalize a vague answer or an overcautious disclaimer.
- Different model snapshots: A result for a 2025 model version may not describe the service you receive today.
- Different product surfaces: Consumer chat, an API, an enterprise deployment and an agent with tools may apply different moderation or fallback layers.
- Stochastic output: The same prompt can produce different answers on separate attempts.
- Safety trade-offs: A low refusal rate is not automatically good if the model also complies with requests that should be blocked.
No standardized, independently administered, current consumer-chatbot test has been published covering all major services on a representative set of ordinary benign prompts. That is why a precise overall winner cannot be claimed.
How to test unnecessary refusals for your own work
A small, controlled comparison is more useful than a generic ranking if your questions involve a particular domain such as health education, security research, history or political discussion.
- Define the allowed task. Write down what a helpful answer looks like and which details would cross into harmful assistance. For example, a defensive cybersecurity explanation may be acceptable while deployment instructions for an exploit are not.
- Use identical prompts. Copy the same wording into each chatbot, on the same date, using the same consumer interface and account tier where possible.
- Build a balanced prompt set. Include ordinary benign questions, legitimate prompts containing sensitive words, educational or research scenarios, controversial but lawful discussions, and genuinely harmful control prompts.
- Record the environment. Note the model name and version, date, geography when relevant, enabled tools, browsing or search status, and any visible safety setting.
- Repeat each prompt. Run multiple attempts because model output can vary. Keep the full transcripts instead of scoring from memory.
- Score more than refusal. Separate full refusals, safe partial answers, cautious but useful answers, factual answers, hedged non-answers and unsafe compliance.
- Check factual usefulness. A response that technically engages but supplies incorrect or irrelevant information is not a successful low-refusal answer.
- Check appropriate refusal. The chatbot should still reject the harmful control prompts. A system that answers everything has poor safety calibration, not superior helpfulness.
- Compare by task category. Calculate results separately for your real use cases instead of collapsing every prompt into one score.
How to choose based on your use case
Sensitive history, politics and public debate
The Columbia study is the most relevant evidence among the cited tests because it examined contested topics and argument-style content. Its acceptance rates indicate willingness to engage, but they do not measure neutrality, factual accuracy or the quality of social-media drafting. If this is your main use, test both abstract questions and user-framed posts because the study found different behavior between those formats.
Health, biology and other dual-use education
Look for safe completion rather than unrestricted compliance. The best response may explain concepts, risks, terminology and lawful safety practices while declining procedural details that could enable harm. RefusalBench shows that strict refusal percentages vary dramatically in biological research, so inspect the actual answer quality and boundaries.
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Use paired prompts: ask first for defensive concepts, then for a clearly operational harmful variant. A well-calibrated chatbot should teach threat models, detection and mitigation while withholding instructions that would facilitate intrusion or malware deployment. Record whether it redirects to a safe alternative instead of ending the conversation.
General research and writing
Provider-published benign-request results are more relevant than a controversial-topic acceptance table, but they still need verification in your interface. Test prompts that contain sensitive names or terms, requests for summaries of disputed material and questions that require uncertainty rather than a confident refusal.
What to do when a legitimate prompt is refused
- Clarify the benign purpose: State whether the request is for education, historical analysis, prevention, compliance or another lawful goal.
- Ask for a bounded answer: Request high-level concepts, risks, examples, citations or defensive guidance without operational instructions.
- Split the task: Separate background explanation from any procedure that might trigger a safety boundary.
- Remove unnecessary sensitive detail: Keep the context needed to answer, but avoid provocative wording that does not affect the substance.
- Ask what can be answered safely: A useful system should identify an allowed portion instead of providing only a blanket refusal.
- Document persistent failures: If the same legitimate task is repeatedly blocked, note the exact model, surface and prompt; switching versions or interfaces may change the result.
Bottom line
Claude Opus 4.6 has the strongest published benign-request over-refusal figure in the available evidence, Gemini 3.7 Flash is described by Google as having low unjustified refusals, and Grok 4 showed the highest engagement in Columbia’s narrow contested-topic test. None of those findings proves an all-purpose winner. Choose the chatbot that gives safe, accurate and appropriately bounded answers on your own representative prompts, while still refusing genuinely harmful assistance.
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