Introduction
Google Gemini’s trajectory over the past two years has been nothing short of remarkable. From Gemini 1.0’s initial debut, to the million-token context windows of the 2.5 Pro/Flash series, to Gemini 3 Pro securing top spots across multiple benchmark tests, Google’s posture in the AI arms race has remained resolute. However, stripping away the glamorous facade of benchmark scores and deeply examining performance in real-world use cases reveals an entirely different picture: the model excels in standardized tests, yet frequently suffers from hallucinations, memory fragmentation, instruction failures, and response instability during daily usage.
Gemini’s core issue lies not in any single deficient feature, but rather in the severe gap between its high-spec capabilities and its real-world operational stability. This gap transforms Gemini’s dilemma from mere technical flaws into a crisis of trust.
I. Hallucination and Credibility: The Most Fatal Fissure
Among all the problems criticized in Gemini, the hallucination rate is undoubtedly the most severe. According to the AA-Omniscience benchmark, Gemini 2.5 Pro’s hallucination rate reaches a staggering 88%, while Gemini 3 Flash climbs to 91%—meaning that when the model “does not know the answer,” there is a nearly 90% chance it will choose to fabricate seemingly plausible false information rather than admit ignorance.
This data is unsettling because it forms a sharp contradiction with Gemini’s leading position in accuracy tests. Gemini 3 Pro secured the top spot among all models in the same benchmark with a 53% accuracy rate, far outperforming GPT-5.1 and Grok 4 at 39%. The model certainly “knows” a lot, but when it does not know, it almost never admits it.
Google’s official model card does not shy away from this, explicitly stating that Gemini 2.5 Pro “is prone to hallucinations” and possesses known limitations in causal understanding, complex logical reasoning, and counterfactual reasoning. The problem lies in the significant chasm remaining between official acknowledgment and actual improvement. Long-term users report that the model makes “confident false assertions” and even generates “completely unreliable” internal audit results when faced with its own reference materials.
An AI assistant that refuses to say “I don’t know,” regardless of how impressive its benchmarks are, will struggle to build genuine user trust.
II. Long Context and Instruction Following: The Gap Between Name and Reality
Google aggressively promotes the million-token context window as one of Gemini’s core selling points. However, multiple tests and user reports consistently indicate that the model’s actual effective reasoning range falls far short of this theoretical upper limit. Developers have measured the model beginning to lose coherence after approximately 30,000 characters, while other analyses note that the conversation collapse threshold has dropped from a previous 650,000 to 700,000 tokens down to roughly 260,000. This discrepancy between marketing specifications and actual capabilities not only misleads user expectations but can also lead to subtle, hard-to-detect errors in critical scenarios.
Accompanying the long-context issue is the severe bias the model exhibits in instruction following. Multiple developers in the Google AI Studio forums have reported that Gemini 2.5 Pro suffers from a strong “summarization bias”—even when explicitly instructed not to summarize, the model tends to compress or generalize content. In prompts containing multiple instructions, the model often focuses solely on what it deems the “primary task,” while ignoring meta-tasks that users explicitly requested be preserved or left unmodified, and even arbitrarily altering code segments that users prohibited from being changed.
More concerningly, the model frequently forgets prior instructions and constraints across multi-turn dialogues, reverting to formulaic, generic response styles. Users are forced to repeatedly restate context and rules, which is extremely time-consuming and frustrating during prolonged work sessions.
An AI model claiming to handle a million tokens that cannot even remember thirty pages of conversation calls the marketing of its context capabilities into question.
III. Creativity, Multimodality, and Stability: A Comprehensive “Close, But No Cigar”
Gemini’s weaknesses are not limited to text-based reasoning. In creative writing, the most consistent feedback from long-term testers is that Gemini’s responses are dutiful yet formulaic, lacking conversational feel and personality. In contrast, Claude possesses a distinct editorial voice, and ChatGPT occasionally flashes with creativity, whereas Gemini appears “technically correct, but bland and uninspired.”
In terms of multimodality, while Gemini’s image generation quality has improved, it still lags behind Midjourney and DALL-E 3. Long-video analysis results remain inconsistent, and the error rate of multimodal attention mechanisms in cross-modal fusion tasks can increase by up to 30%.
Stability issues are equally impossible to ignore. The model frequently truncates output mid-sentence without any error prompt; the “Save to Drive” feature occasionally falls into an infinite loop; and vague messages such as “Internal error occurred” appear frequently, leaving users unable to determine whether the issue stems from the model or user error. Furthermore, the model can enter an “infinite loop” state, repeating the same incorrect response, and even admitting it is trapped in a loop after being pointed out, yet remaining unable to stop—pointing to fundamental defects in the conversation state management system.
The latency issue in reasoning modes also impacts user experience. Although Gemini 2.5 Pro’s deep thinking mode produces superior results, response times frequently reach 30 to 60 seconds. In interactive work scenarios requiring frequent back-and-forth communication, this wait time is sufficient to disrupt thought processes and work rhythms.
Each individual issue is merely “close, but no cigar,” but combined, they constitute a continuous friction in the user experience.
IV. Safety and Ecosystem Dependency: An Unbalanced Scale
Gemini presents a contradictory posture regarding safety: on one hand, it is overly conservative; on the other, it is insufficiently secure.
Google’s official model card acknowledges that the primary safety limitations of Gemini 2.5 Pro lie in “over-refusals” and tone issues—the model sometimes refuses to respond to prompts that completely violate no policies. This excessive caution reduces usability, causing users to repeatedly hit walls when making entirely reasonable requests.
Conversely, Gemini 2.5 Pro is easily induced to generate harmful content. According to testing by Cybernews, the model performs poorly in categories such as animal cruelty and stalking advice, possessing weaker defense capabilities than OpenAI’s GPT-5 and Anthropic’s Claude. TechCrunch also reported that Gemini 2.5 Flash experienced a regression in safety metrics compared to its predecessor, declining by 4.1% and 9.6% in text and image safety metrics, respectively.
The simultaneous presence of over-refusals and inadequate defense indicates that safety calibration mechanisms have yet to find equilibrium.
Closely tied to safety are ecosystem dependency and privacy concerns. Gemini’s best experience relies heavily on the Google ecosystem—Drive, Gmail, and search history—but in late 2025, Tenable researchers uncovered three prompt injection vulnerabilities in Gemini components: attackers could use hidden instructions in log files, malicious search history planted in browser history, or embedded web prompts to induce Gemini to transmit user memory and personal data to external servers. Google subsequently patched these vulnerabilities, but the incident itself highlights the expanded attack surface brought about by deep ecosystem integration.
V. Modification Directions: Honesty, Stability, Transparency
Based on the analysis above, this article proposes three core improvement recommendations as priority directions for Gemini’s future development:
1. Honesty Prioritized Over Confidence
Gemini’s most urgent improvement need is the establishment of a robust “saying ‘I don’t know’ when it doesn’t know” mechanism. This is not merely a technical problem for reducing hallucination rates, but a choice of product philosophy. Specific practices include: appending confidence scores and verifiable source attributions to responses; proactively marking information as “low confidence” or directly replying “I am not sure” when confidence in a piece of facts falls below a set threshold. Rather than confidently fabricating in 88% of scenarios, it is better to honestly remain silent in those scenarios.
2. Stability Prioritized Over Feature Expansion
Truncation, loops, memory loss—these are not edge cases, but cracks in foundational reliability. Google should prioritize engineering resources toward fixing these core issues rather than rushing to launch new features. Specifically: implement a conversation state checkpoint mechanism to automatically reset context and notify users when a loop is detected; establish an auto-continuation mechanism for response truncations; and improve retry logic for generation errors, providing users with visible error causes rather than vague “internal error” messages. Simultaneously, publicly disclose actual effective context lengths rather than theoretical upper limits, allowing users to build reasonable expectations.
3. Transparency Prioritized Over Marketing Rhetoric
Gemini’s capability boundaries should be honestly labeled. The effective range of context windows, confidence levels in multimodal tasks, and trigger causes for safety filters—these pieces of information should be visible to users. Providing adjustable response style options (concise, professional, instructional, creative) lets users, rather than the model, dictate the tone and depth of interaction. On the safety front, recalibrate thresholds to reduce false-positive refusal rates while strengthening defenses against genuinely harmful content, and provide a “safety mode” switch allowing users to adjust strictness levels themselves.
Conclusion
Gemini’s predicament reflects the universal dilemma facing current large language models: chasing high scores in standardized tests and building trust in real-world usage are two paths that do not entirely overlap. Gemini has achieved remarkable success in the former, but still has a long road ahead in the latter.
An AI model with an 88% hallucination rate, regardless of its accuracy, cannot become a truly trusted assistant for users. A model that claims a million tokens yet loses its memory after 30,000 characters requires more honest capability labeling. A model with an unbalanced security posture requires finer calibration of its defensive mechanisms.
To win over users, Gemini must become not only smarter, but also more reliable, more honest, and more cognizant of its own boundaries.