Dear Gemini API Team and fellow developers,
I’m writing this as someone who has been a paying Gemini API user and a genuine advocate for the platform. I’ve recommended Gemini to fellow developers, written about its strengths, and chosen it as my primary model over competitors on multiple occasions — precisely because of its unmatched knowledge depth, extraordinary long-context capabilities, and the literary quality of its outputs.
It is because I want Gemini to succeed — and because I believe it deserves to lead the LLM API market — that I feel compelled to share detailed, constructive feedback on the recent API changes introduced with Gemini 3.6 Flash, 3.5 Flash-Lite, and the forthcoming model updates.
I believe these changes, while well-intentioned, pose a serious risk to Gemini’s competitive position, developer trust, and long-term commercial viability. I want to explain why — not from the perspective of one niche use case, but across the full spectrum of developers and applications that this affects.
Summary of Changes in Question
For those not yet aware, the Gemini 3.6 generation introduced the following breaking changes:
- Complete prohibition of model prefill: API requests ending with role: “model” now return HTTP 400 errors
- Deprecation of sampling parameters: temperature, top_p, and top_k are ignored (and will eventually throw errors)
- Migration to Interactions API: Developers are directed to replace prefill-based workflows with system_instruction and Structured Outputs
I understand that these changes likely stem from legitimate safety and architectural goals. I respect those goals. What I want to discuss is whether the current implementation achieves them without unnecessarily damaging the platform’s value proposition — and whether better alternatives exist.
- Developer Trust: The Cost of Breaking Changes Without Adequate Transition
The Concern
temperature, top_p, and top_k are not obscure, experimental features. They are foundational LLM API parameters — as standard as max_tokens or stop_sequences. They have been part of every major LLM API since GPT-3’s launch in 2020. They are taught in university courses, covered in Google’s own documentation and Codelabs, and used in virtually every non-trivial LLM application.
Removing them without a meaningful deprecation period sends a deeply concerning signal to every developer currently building on Gemini — or considering doing so:
“Any API feature you depend on today may disappear in the next model release, without warning, without migration support, and without an equivalent replacement.”
Why This Matters Commercially
Developer platform loyalty is built on predictability. When an engineering team commits to building a product on a specific API, they are making a bet — a bet that the platform’s behavior will remain stable enough to support their investment.
Every breaking change that arrives without adequate notice erodes the confidence behind that bet. And in a market where switching to a competitor’s API often requires only changing an endpoint URL and an API key, the switching cost is near zero but the trust cost is enormous.
Enterprise procurement teams evaluate API stability as a first-order criterion. A platform that removes industry-standard parameters in a point release — without a published deprecation timeline, migration guide, or developer advisory period — will struggle in enterprise evaluation processes, regardless of the underlying model quality.
Constructive Suggestion
Rather than abrupt removal, consider a staged deprecation path: announce the intended changes 3-6 months in advance, provide a documented migration guide with equivalent alternatives for every removed feature, and maintain backward compatibility for at least one major version cycle. This is standard practice in mature API ecosystems (Stripe, Twilio, AWS) and would preserve developer trust while still achieving the architectural goals.
- Competitive Positioning: Every Competitor Offers What Gemini Just Removed
This is perhaps the most straightforward and commercially urgent concern. Here is a factual comparison of sampling parameter support across current major LLM APIs:
| Feature | Gemini 3.6 flash+ | OpenAI GPT-5.6 | Anthropic Claude Opus 4.8 | DeepSeek V4 | Mistral Large | Meta Llama 4 (self-hosted) |
|---|---|---|---|---|---|---|
| temperature |  Removed |
 Full control |
Full control |
Full control |
Full control |
Full control |
| top_p | Removed |
Full control |
Full control |
Full control |
Full control |
Full control |
| top_k | Removed |
— | Full control |
Full control |
Full control |
Full control |
| Prefill / Assistant priming | Blocked |
Supported |
Supported |
Supported |
Supported |
Full control |
| Structured output control | |
|
|
|
|
|
Gemini is now the only major LLM API that does not offer developer control over output sampling.
In every competitive evaluation — whether conducted by a startup CTO, an enterprise architecture team, or an independent developer choosing a platform for a side project — this table speaks for itself. Gemini has moved from “feature parity with additional advantages” to “strictly less capable as a developer platform” on a dimension that every competitor treats as table stakes.
The Strategic Risk
Model quality alone does not win API market share. Developer experience wins API market share. Gemini’s model quality is genuinely world-class — I would argue its knowledge depth, long-context coherence, and reasoning capabilities are best-in-class in several categories. But if the API wrapping that model is less flexible, less controllable, and less predictable than every alternative, developers will choose inferior models on superior platforms.
The risk is not that developers leave because they hate Gemini. The risk is that they leave because they can’t build what they need on Gemini — and they can build it on every other platform.
- Impact on Legitimate Commercial Applications
Temperature and sampling control are not niche features used by hobbyists. They are core engineering tools used across the entire spectrum of commercial LLM applications. Here are specific, real-world use cases that are directly degraded by these changes:
3.1 Customer Service & Support Automation
Need: Low temperature (0.1-0.3) for highly consistent, predictable, brand-safe responses.
Impact: Without temperature control, customer-facing chatbot outputs become unpredictable. An enterprise deploying a Gemini-powered support agent cannot guarantee response consistency across thousands of daily interactions. This is a dealbreaker for any regulated industry (finance, healthcare, legal) where output consistency is a compliance requirement.
3.2 Creative Content & Marketing Copy Generation
Need: Higher temperature (0.8-1.2) for diverse, surprising, creative outputs.
Impact: Marketing teams using Gemini to generate ad copy, social media content, or brainstorming ideas lose the ability to “turn up creativity.” The locked default may be too conservative for creative applications or too random for structured ones — and the developer has no way to adjust.
3.3 Code Generation & Technical Assistance
Need: Very low temperature (0.0-0.2) for deterministic, precise outputs.
Impact: Code generation requires near-zero randomness. A developer tool that sometimes produces slightly different code for the same prompt is unreliable. Without temperature=0, Gemini-powered coding tools cannot guarantee reproducible outputs — a fundamental requirement for CI/CD integration and automated testing.
3.4 Data Extraction & Structured Output Pipelines
Need: Deterministic output (temperature=0.0) combined with format control.
Impact: ETL pipelines that use Gemini to extract structured data from unstructured text require perfectly reproducible outputs. Any randomness introduces parsing failures, data inconsistencies, and pipeline unreliability. While Structured Outputs partially address format concerns, they do not replace the need for deterministic sampling.
3.5 A/B Testing & Variant Generation
Need: Precise temperature control to generate controlled variants of the same content.
Impact: A common workflow: generate version A at temp=0.3, version B at temp=0.7, version C at temp=1.0, then test which performs best. This is impossible without temperature control. Developers must resort to crude workarounds (prompt variations) that are less controlled and more costly.
3.6 Game & Interactive Entertainment
Need: Tunable randomness for NPC dialogue, procedural narrative, and world-building.
Impact: Game developers using Gemini for dynamic NPC interactions need characters that feel natural but not erratic. Temperature tuning is the primary tool for achieving this balance. Locking it removes a critical creative control that every competing API provides.
These are not edge cases. These represent the core commercial use cases that drive API revenue.
- Impact on Research and Education
Academic Research
AI researchers rely on sampling parameter control for:
- Reproducibility: Scientific papers require exact parameter specifications to enable replication. A study conducted on Gemini with locked, undisclosed sampling parameters cannot be properly reproduced or compared.
- Safety Research: Ironically, AI safety researchers need the ability to test model behavior across different temperature ranges to identify and characterize safety boundaries. Removing this ability makes Gemini less evaluable from a safety perspective — reducing transparency rather than enhancing safety.
- Benchmark Comparisons: Standardized benchmarks (MMLU, HumanEval, etc.) are often run at specific temperature settings. Gemini’s results become incomparable if the sampling strategy is opaque and uncontrollable.
Education
Thousands of courses, tutorials, and textbooks worldwide teach temperature, top_p, and top_k as fundamental concepts for understanding LLM behavior. Google’s own educational materials — including AI Studio tutorials and Google Cloud documentation — have extensively used these parameters as teaching tools.
Educators who currently use Gemini API for hands-on instruction now face an awkward situation: they must explain to students that the concepts they’re learning work on every other API but not on the one made by the company that helped pioneer them.
- Accessibility and Assistive Technology
A less obvious but important impact: some assistive technology developers use prefill to control output format for compatibility with screen readers and other accessibility tools. Temperature control is also used to ensure consistent, predictable outputs for users with cognitive disabilities who rely on LLM-powered communication aids.
These are small user groups, but they represent exactly the kind of inclusive use cases that Google has historically championed. Removing the tools they depend on — without providing documented alternatives — is inconsistent with Google’s accessibility commitments.
- Cost Efficiency and Developer Economics
Temperature control is also a cost optimization tool:
- Low temperature = more concise, focused outputs = fewer tokens = lower cost per request
- High temperature = more diverse outputs = potentially more retries needed = higher cost
Without the ability to optimize sampling for their specific use case, developers lose a key lever for managing API costs. The only “adjustment” available becomes retrying requests and hoping for better outputs — which increases both latency and spend.
For cost-sensitive startups and individual developers, this is a direct financial penalty that makes Gemini less attractive compared to APIs that allow cost optimization through parameter tuning.
- Prefill Prohibition: Broader Impact Beyond Safety
I understand that prefill (role: “model” at the end of a request) can be misused. But it also serves critical legitimate functions:
| Legitimate Use Case | How Prefill Was Used | Alternative Available? |
|---|---|---|
| JSON/XML format enforcement | Prime the model with {result: to ensure valid structured output | Partial (Structured Outputs), but less flexible |
| Multi-agent systems | Inject Agent A’s conclusion as Agent B’s starting context | No clean alternative |
| Continuation / completion tasks | Provide a text fragment and ask the model to continue from that exact point | No equivalent |
| Translation with format preservation | Prime the output with the target format header | Workarounds are fragile |
| Consistent response formatting | Ensure every response starts with a specific structure for downstream parsing | Partial, but less reliable |
| Persona voice consistency | Prime with a character’s speech pattern to maintain voice | Must rely entirely on system prompt |
A blanket prohibition on prefill eliminates all of these legitimate workflows in order to address misuse that could potentially be handled through more targeted mechanisms.
Constructive Suggestion
Rather than a blanket HTTP 400 rejection for any request ending in role: “model”, consider:
- Content-based filtering on the prefill content itself (flag clearly manipulative primes while allowing structural ones)
- A dedicated “completion mode” API endpoint designed for legitimate continuation tasks
- Allowlisting prefill for verified/paid developer accounts while restricting it on free tiers
These approaches address the safety concern while preserving legitimate functionality.
- The Bigger Picture: Ecosystem Competition and Developer Migration
I want to be very direct about the strategic implication, because I believe this is ultimately the most important consideration for the Gemini team’s leadership:
Every feature removal that places Gemini behind its competitors accelerates developer migration to platforms that offer more control.
The current LLM API landscape is intensely competitive:
- OpenAI continues to expand developer controls and customization options with each GPT-5 update
- Anthropic has built a reputation for excellent developer experience, with Claude Opus 4 offering full parameter control plus highly effective prefill support
- DeepSeek offers aggressive pricing with full parameter access, rapidly gaining share among cost-conscious developers
- Mistral positions itself explicitly as the “developer-friendly” European alternative with maximum API flexibility
- Meta’s Llama 4 continues to improve, offering complete control through self-hosting with zero ongoing API costs
Gemini’s core competitive advantages — knowledge depth, long-context performance, multimodal capabilities — are being undermined by an API layer that is becoming the least flexible in the market.
The developer community is watching. The narrative forming on Reddit (r/LocalLLaMA, r/MachineLearning), Hacker News, and X/Twitter is not “Gemini’s model is bad” — it’s “Gemini’s model is great but Google keeps making it harder to use.” This is a far more dangerous narrative, because it means Google is losing developers not due to model quality (which is hard to fix) but due to product decisions (which are entirely within Google’s control to reverse).
The Migration Pattern
Developer tries Gemini → Impressed by model quality →
Builds application on Gemini API →
API breaking change removes features they depend on →
Developer is forced to evaluate alternatives →
Finds that competitors offer equal or better developer experience →
Migrates → Does not come back even if Google reverses the changes
This cycle is happening right now. Every developer lost to this
cycle is a developer whose future token spend goes to a competitor.
- A Constructive Path Forward: Tiered Access Model
I don’t believe the answer is “revert everything and ignore safety concerns.” Safety matters, and I respect Google’s commitment to responsible AI development.
What I believe would serve both safety goals and developer needs is a tiered access model:
Proposed Tiered API Access:
┌───────────────────────────────────
│ Tier 1: Standard (Free / Low-volume)
│ ──────────────────────────────────
│ • Sampling parameters: Limited range
│ (e.g., temperature 0.0-1.0 only)
│ • Prefill: Disabled
│ • Safety filters: Full
│ • Target: Casual users, experimentation
│
│ Rationale: Maximum safety for the lowest-trust tier
├───────────────────────────────────
│ Tier 2: Developer (Verified / Paid)
│ ──────────────────────────────────
│ • Sampling parameters: Full range
│ • Prefill: Enabled with content monitoring
│ • Safety filters: Configurable
│ • Target: Professional developers, startups
│
│ Rationale: Verified developers need professional
│ tools; accountability exists through billing info
├───────────────────────────────────
│ Tier 3: Enterprise (Contract / Custom)
│ ──────────────────────────────────
│ • Full parameter control
│ • Custom safety configurations
│ • Dedicated support and SLAs
│ • Target: Enterprise customers
│
│ Rationale: Enterprise customers have legal and
│ compliance frameworks that provide accountability
└───────────────────────────────────
This approach:
- Maintains strong safety defaults for anonymous/free-tier usage
- Restores professional tooling for verified, paying developers
- Preserves Gemini’s competitive positioning against alternatives
- Aligns incentives: developers who want more control must verify identity and pay, increasing both security and revenue
- Mirrors the approach already used by other Google products (YouTube API quotas, Maps API tiers, Cloud IAM permission levels)
This is not a radical proposal. It is the standard approach used by mature API platforms across the industry.
- A Note on Creative Writing and Interactive Fiction
I want to briefly mention one additional community that has been significantly impacted, because their experience illustrates the broader issue particularly well.
There is a large and growing community of users who use Gemini for collaborative creative writing and interactive fiction — essentially using AI as a narrative engine for co-authored literary works. Tools like SillyTavern (20,000+ GitHub stars), Agnai, and RisuAI serve tens of thousands of active users in this space.
Gemini has been this community’s preferred model — not because it’s the easiest to use, but because its knowledge depth, long-context coherence, and literary quality are genuinely best-in-class. These users chose Gemini on merit and have been vocal advocates for the platform.
The removal of temperature control and prefill has disproportionately impacted these users, as creative writing inherently requires:
- Variable output creativity (temperature tuning)
- Narrative continuation from specific points (a form of prefill)
- Stylistic consistency across long sessions (parameter stability)
These are the same capabilities needed by game studios building narrative AI, screenwriters using AI for brainstorming, marketing teams generating creative copy, and authors using AI as a writing partner. The creative writing community is the canary in the coal mine — their pain today will be every creative professional’s pain tomorrow.
I raise this not as a special plea for one community, but as evidence that these API changes affect a far broader range of legitimate, valuable, revenue-generating use cases than the safety benefits they provide.
Closing Thoughts
I want to end where I started: I am writing this because I want Gemini to win.
Gemini’s underlying technology is extraordinary. The model’s knowledge, reasoning, and creative capabilities are — in my experience — unmatched across multiple dimensions. I have been consistently impressed, and I have consistently recommended the platform to others.
But a great model behind a restrictive API is like a Ferrari engine in a car with no steering wheel. Developers don’t just need power — they need control. And right now, every competitor offers more control than Gemini does.
The changes introduced in Gemini 3.6 may address legitimate safety concerns at the API level. But the implementation — abrupt, total, without tiered alternatives or adequate transition support — risks sacrificing developer trust and competitive position for safety gains that could be achieved through more nuanced approaches.
I respectfully urge the Gemini team to:
- Acknowledge that the removal of sampling parameters and prefill affects legitimate commercial, research, educational, and creative use cases — not just adversarial misuse
- Explore a tiered access model that restores professional developer controls for verified, paying users
- Publish a clear deprecation and migration policy for future API changes, with adequate advance notice
- Engage with the developer community on these changes — not just through documentation updates, but through direct dialogue
The LLM API market is at a pivotal moment. Developers are making platform commitments right now that will shape the ecosystem for years. Gemini has every advantage it needs to lead that ecosystem — except the trust that comes from treating developers as partners rather than risks to be managed.
I hope this feedback is received in the constructive spirit in which it is intended. I remain a Gemini user, a Gemini advocate, and someone who believes the best AI platform should also be the most empowering one.
Thank you for your time and consideration.
Sincerely,
A developer who wants Gemini to be the best it can be
I encourage every developer reading this who has been affected by these changes — regardless of your specific use case — to share your experience below. The Gemini team needs to see the full breadth and depth of impact across the developer community.
Although I’m not sure the Google team will see my post, I’m still trying to make my voice heard. If possible, could you please send the link to my post to your Reddit community? Our requests need support and to be seen. Thank you very much.