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There is no defensible all-purpose winner. Anthropic reports an exceptionally low over-refusal rate for Claude Opus 4.6 on one of its own difficult benign-request tests. Google describes Gemini 3.7 Flash as having low unjustified refusals on internal evaluations, while a 2025 cross-provider study found Grok 4 engaged with all 64 prompts in its particular contested-topic set. Those results use different prompts, model versions, products and definitions, so they cannot be combined into a single league table.
The practical answer is to choose the model that handles your type of legitimate sensitive request with a useful, bounded answer rather than a blanket refusal. That requires looking at safe-completion quality and appropriate refusals, not just counting the word “no.”
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What counts as an unnecessary refusal?
An unnecessary refusal is a refusal to help with a legitimate request that could have been answered safely. A good response might provide background, a high-level explanation, a warning, a safer alternative or a limited version of the requested task. A blanket refusal is unnecessary when the request itself is lawful and the useful portion can be separated from genuinely dangerous details.
This is different from an appropriate refusal. A model should decline instructions that would enable serious harm, such as operational assistance for wrongdoing, while still answering harmless educational or contextual questions about the same subject. Anthropic describes the desired balance this way: “AI assistants need to decline truly harmful requests while still being able to respond to legitimate questions, even when those questions involve sensitive topics.”
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Refusal behavior is also not binary in practice. A chatbot may give a short refusal, provide a safe partial answer, hedge so heavily that the answer is unusable, answer the benign part and omit dangerous instructions, or comply unsafely. Any meaningful comparison must record these outcomes separately.
What current evidence says about the leading models
Claude Opus 4.6: the strongest published benign-prompt number
Anthropic’s 2026 Transparency Hub reports that Claude Opus 4.6 over-refused on 0.04% of prompts in Anthropic’s higher-difficulty benign-request evaluation. On that same provider-designed evaluation, Anthropic reports 8.50% for Claude Sonnet 4.5 and 6.01% for Claude Haiku 4.5.
The 0.04% figure is striking, but it is not a universal chatbot score. Anthropic controlled the prompt design, definitions and evaluation conditions, and the result does not establish how Opus 4.6 compares with other providers’ models tested on different sets. Treat it as evidence that Opus 4.6 performed well on this particular benign-request benchmark, not proof that it refuses least in every product or topic.
Gemini 3.7 Flash: Google’s “low unjustified refusals” characterization
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 where it can while remaining safe.
Google says the evaluations are automated, the test set is being refined and the improved evaluation is not directly comparable with earlier model-card results. “Low” is therefore Google’s characterization of relative performance on its internal test, not a published percentage that can be ranked against Anthropic’s 0.04%.
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Grok 4: complete engagement in one contested-topic study
Columbia Global Freedom of Expression’s 2025 report tested models with 64 prompts involving argument generation and social-media-style content about contested topics. Grok 4 accepted all 64 prompts in that test.
That result is useful because the models faced a shared prompt set, but it describes willingness to engage with a narrow class of lawful, controversial discourse. It does not measure ordinary questions, medical education, security research, biological work or every other form of unnecessary refusal. Model versions and product conditions were snapshots from 2025.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchOther results in the Columbia comparison
The same report found different acceptance rates for the other tested models:
| Model | Result in Columbia’s 2025 contested-topic test | What the number means |
|---|---|---|
| Grok 4 | Accepted all 64 prompts | Complete engagement with that prompt set |
| Llama 4 | 95% response rate | Responses to most prompts in the same test |
| Mistral Medium 3.1 | 95% response rate | Responses to most prompts in the same test |
| Gemini 2.5 Flash | More than 9 in 10 | High engagement in this specific contested-topic set |
| GPT-5 | 80% engagement | Engagement with four in five prompts in the report’s measure |
| DeepSeek V3.1 | 80% overall response rate | Overall response rate reported by the study |
| Claude Sonnet 4 | 73% acceptance | Acceptance in this prompt set, not a general refusal rate |
| Qwen3-235B-A22B | 53% overall acceptance | Lowest listed acceptance rate in this comparison |
The report notes that all refusals of abstract “arguments” prompts by the tested China-headquartered providers concerned China-sensitive topics. It also found that models were generally more willing to produce abstract arguments than user-framed social-media content. Those details matter: a model can appear permissive for neutral analysis yet refuse when the same idea is phrased as a personal post or advocacy message.
Why these numbers cannot produce one overall ranking
Different definitions
“Over-refusal,” “unjustified refusal,” “acceptance” and “engagement” are not interchangeable. One test may count only a complete refusal. Another may count a response that is technically present but so hedged that it fails the user’s request. A third may classify a safe partial answer as engagement.
Different prompt domains
Contested political discussion, biology, cybersecurity, health education and sensitive history trigger different safeguards. A model that answers controversial argument prompts may still be conservative with biological protocols. Conversely, a model tuned for research assistance may provide a bounded scientific explanation while refusing operational steps.
Different interfaces and safety layers
The same underlying model can behave differently in a consumer chatbot, an API, an enterprise workspace or an agentic product with additional moderation and tool controls. Account tier, geography, enabled tools, search mode and fallback models can all affect the response.
Different dates and versions
Safety tuning changes frequently. A result for Claude Sonnet 4 or Gemini 2.5 Flash is a dated snapshot, not a guarantee for a later release or the current consumer interface. Anthropic’s older result for Claude 3.7 Sonnet illustrates this: the provider reported a 45% reduction in unnecessary refusals in standard mode and 31% in extended-thinking mode against an earlier comparison, but its Transparency Hub identifies that model as retired. Those figures are historical context, not a current recommendation.
What the RefusalBench study adds
A 2026 RefusalBench preprint tested 19 frontier models on identical biological-research prompts in a May 2026 snapshot. Strict refusal rates ranged from 0.1% to 94.6%. The authors argue that strict refusal rate alone can misrank safety calibration because some models use hedged partial-compliance patterns that a binary metric misses.
This is a domain-specific preprint, not a general consumer-chatbot ranking. Its value is methodological: a very low refusal percentage can indicate unsafe compliance, while a high percentage can reflect conservative handling of genuinely risky instructions. The useful question is whether the model gives the safe portion of an answer and withholds the dangerous operational details.
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OpenAI’s GPT-5 System Card discusses “safe-completions” as an alternative to a brittle binary refusal boundary, particularly for dual-use biology and cybersecurity requests. The approach aims to maximize helpfulness within policy constraints: explain concepts, suggest lawful defensive steps or provide high-level guidance, while omitting details that would enable harm.
That distinction should change how you judge a chatbot. A response that says, “I can’t provide instructions for carrying out that attack, but I can explain how to detect and mitigate it,” is more useful than a bare refusal and safer than unrestricted procedural instructions.
How to test which chatbot refuses least for your work
No published source establishes a standardized, independently administered current test of all major consumer chatbots on a representative set of benign prompts. If refusal behavior matters to your workflow, run a small controlled comparison instead of relying on a global ranking.
- Define your legitimate use cases. Write prompts that resemble your actual work: sensitive history questions, health education, security research, controversial political analysis, classroom material or biological background. Do not test only provocative wording.
- Create matched prompt pairs. Ask for neutral explanation, a lawful practical task and a request that clearly crosses a safety boundary. Include benign prompts containing sensitive terms so you can detect false positives.
- Use the same conditions. Test the same date, consumer interface, account tier, language, geography and enabled tools where possible. Record whether search, file access or other safety filters are active.
- Record exact model identity. Save the displayed model name or version, not just the service name. Note fallbacks, routing labels and whether the response came from an agentic mode.
- Repeat each prompt. Outputs can vary between runs. Multiple trials reveal whether a refusal is consistent or a sampling artifact.
- Classify the response. Use separate labels for full refusal, safe partial answer, useful bounded answer, excessive hedging, factual answer and unsafe compliance.
- Score helpfulness and safety independently. A model should receive credit for answering the allowed portion and for refusing genuinely harmful control prompts. Do not reward a model merely for answering everything.
- Review failures manually. Check whether the model misunderstood the request, overreacted to a keyword, invented a policy barrier or provided details that should have been withheld.
A practical decision guide
If your priority is sensitive but ordinary research
Start with a model that demonstrates low false-positive refusals on benign prompts, then verify it with your own examples. Anthropic’s 0.04% Opus 4.6 result is the clearest provider-published benign-request figure in the available evidence, but it still needs confirmation in the interface and region you plan to use.
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If your priority is controversial public discussion
The Columbia study provides the most relevant shared test. Grok 4, Llama 4 and Mistral Medium 3.1 showed the highest engagement there, followed by Gemini 2.5 Flash. Use those percentages only for the report’s 64 contested-topic prompts; they do not predict performance on unrelated sensitive subjects.
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If your priority is dual-use technical work
Look for safe-completion behavior rather than unrestricted compliance. The best result is a technically useful explanation, defensive guidance or high-level context that omits harmful operational steps. RefusalBench’s wide 0.1%–94.6% range shows why a low strict-refusal score by itself is not a quality target.
If you need consistent production behavior
Test the exact product surface and account configuration you will deploy. An API result, a consumer-chat result and an agentic system result are not interchangeable. Keep a dated regression set and rerun it after model updates.
Where the evidence is weakest
There is no independent, current, apples-to-apples benchmark covering all major chatbot services and a representative sample of everyday benign requests. Provider evaluations are valuable but self-designed. Cross-provider studies improve comparability by using shared prompts, yet they cover narrow topics and dated model snapshots. Automated refusal metrics can miss whether the answer was actually useful, while human judgments can introduce their own subjectivity.
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OpenAI’s Operator System Card illustrates the problem. On its standard refusal evaluation, it reported a “not overrefuse” score of 55% for Operator and 90% for GPT-4o. Operator is an agentic system with distinct interventions and risks, so those figures should not be treated as current ChatGPT scores or compared directly with the other results in this article.
Bottom line for choosing a chatbot
Choose based on your prompt class, not a universal “least refusal” label. For benign requests, Claude Opus 4.6 has the strongest published provider result, with the important limitation that Anthropic produced the test. For contested-topic discussion, Grok 4 led Columbia’s 2025 64-prompt comparison, while several other models also engaged frequently. For dual-use technical questions, judge whether the chatbot gives a safe, bounded completion and still refuses genuinely harmful assistance.
In every case, keep full refusals, safe partial answers and unsafe compliance in separate categories. That three-way view is more informative—and safer—than searching for the chatbot that simply says “yes” most often.
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