Google

Gemini 1.0 Pro Deprecated

This model always redirects to the latest model in the Google Gemini Pro family.

Apr 27, 2026 1.0M context 2,048 tokens output
Text Image File Video Tools Structured Output

Model Overview

High-signal model metadata in a structured two-column overview table.

Provider

The entity that provides this model.

Google

Model ID

The routed model identifier exposed by upstream providers.

~google/gemini-pro-latest

Input Context Window

The number of tokens supported by the input context window.

1.0M tokens

Maximum Output Tokens

The number of tokens that can be generated by the model in a single request.

2,048 tokens tokens

Open Source

Whether the model's code is available for public use.

No

Release Date

When the model was first released.

Apr 27, 2026 24 days ago

Knowledge Cut-off Date

When the model's knowledge was last updated.

Unknown

API Providers

The providers that offer this model. This is not an exhaustive list.

Google

Modalities

Types of data this model can process.

Text Image Audio Video File

What is Gemini 1.0 Pro Deprecated

A fuller summary of positioning, capabilities, and source-specific details for Gemini 1.0 Pro Deprecated.

This model always redirects to the latest model in the Google Gemini Pro family.

Capabilities

What Gemini 1.0 Pro Deprecated supports

RN

Reasoning Controls

OpenRouter lists GPT-5.5 with reasoning support and explicit reasoning-related request parameters.

JSON

Structured Outputs

Structured output settings are exposed through OpenRouter for schema-driven or format-controlled responses.

TL

Tool Calling

Tool invocation and tool selection are supported in the routed OpenRouter interface for this model.

MM

Multimodal I/O

This model accepts text input, image input, file input, audio input, video input and returns text output.

CTX

Large Context Window

OpenRouter currently lists a context window of 1.0M with up to 2,048 tokens maximum output tokens.

Pricing for Gemini 1.0 Pro Deprecated

Primary API pricing shown in the same “quick compare” spirit as the reference page.

Price Comparison

Additional usage-cost dimensions synced into the project for this model.

Image input $2.00
Audio input $2.00
Web search $14000.00
Reasoning $12.00
Cache read $0.20
Cache write $0.38
maxTemperature 1
maxResponseSize 2,048 tokens

API Access & Providers

Places where this model is available, based on the synced detail-page metadata.

Google

Resources & Documentation

Official model cards, release notes, docs, and other references synced from the source page.

Community discussion

What people think about Gemini 1.0 Pro Deprecated

Gemini 1.0 Pro Deprecated discussions are most active in r/Bard, r/Discount_Subscription, r/claude. Top Reddit threads cluster around benchmark and model-comparison threads, coding workflow discussions.

The strongest match in this snapshot has 1712 upvotes and 99 comments.

Stop blaming Claude. Your harness is the problem.

I've been running Claude Code on Opus 4.7 for 8+ hours a day on Max 5x. Zero quota issues. Here's what I actually did.

Most people complaining about Claude "going dumb" or "eating tokens" set it up like this: no memory, no tools, no rules, dump 40 files into one context window, then wonder why it hallucinates. That's not a Claude problem.

Context discipline cuts token usage roughly in half

Put a CLAUDE.md at your repo root. Stack overview, ownership matrix, hard rules — run tsc --noEmit after every edit, max 50 lines per bugfix, one fix per commit, never touch auth/Stripe/middleware without explicit approval. It loads every session. Claude stops asking the same questions.

Persistent memory lives at ~/.claude/projects/yourproject/memory/ — typed markdown files with prefixes like user_, feedback_, project_, reference_. Keep an index in MEMORY.md. You stop re-explaining your project at the start of every conversation.

Biggest single quota win: subagents for grep-work. Spawn an Explore or general-purpose agent to do the file-digging. They burn their own context, return a summary. Your main window stays clean.

Workflow discipline is where most setups fall apart

Auto-retros after every non-trivial session. Save them to docs/retros/YYYY-MM-DD-topic.md. The next session loads the latest retro automatically — continuity without re-briefing.

verification-before-completion as a hard rule. Claude cannot say "done" or "fixed" without running the verify command and showing you the output. Kills hallucinated success completely.

Atomic commits, one fix per commit, hard line limits. Clean history, easy rollback, and it forces Claude to actually scope its work.

For architecture decisions or anything involving security/migrations: one phrase triggers Claude to spawn Gemini Pro + Flash + Sonnet in parallel and synthesize. Three independent reads are better than one confident monologue.

MCP servers — let it act instead of copy-pasting

The ones I actually use:

- supabase — SQL, migrations, schemas directly from chat
- github — PRs, diffs, issues, file reads
- chrome-devtools-mcp + playwright — Claude can browse your deployed site, take screenshots, evaluate JS. It QAs itself.
- context7 — current library docs, not stale training data. Kills a specific class of hallucination entirely.
- firecrawl — on-demand scraping
- sentry — production errors read and triaged from chat
- gemini MCP — powers the multi-model consultation panel

OSS worth actually installing

graphify — takes any input (code, docs, papers, images) and produces a clustered knowledge graph as HTML + JSON. On large repos, Claude reads the graph instead of 200 files. Massive.

claude-flow — swarm orchestration, hooks, memory coordination, SPARC, TDD, code review swarms. github.com/ruvnet/claude-flow

Superpowers skills — search "superpowers skills claude code" on GitHub. The ones I use most: systematic-debugging, verification-before-completion, dispatching-parallel-agents, test-driven-development.

CodeRabbit skill reviews diffs and auto-fixes review comments. Claude Retrospective skill generates the retros mentioned above.

Hooks automate the grunt work

PreToolUse, PostToolUse, SessionStart, PreCompact, Stop. Auto-save memory, auto-run tsc on edits, sync state before compaction. Claude thinks, the harness does the janitor work.

TL:DR!

1. Write CLAUDE.md
2. Turn on persistent memory
3. Install graphify + claude-flow + 6-7 MCPs
4. Auto-retros + verification-before-completion as non-negotiables
5. Subagents for grep and file exploration
6. 50-line limit per bugfix
7. Consultation panel for hard calls

5+ hours a day, ~250 tool calls per session, atomic commits, full deploy → screenshot → verify cycles. Max 5x, no quota hit.

Claude isn't the problem. The harness is!

EDIT: https://github.com/anothervibecoder-s/claudecode-harness

I made a claude.md example based on my CLAUDE.md file, you can tell claude to fill this based on your projects!

If it helped, just star it!

Open Reddit thread
r/yumeshipping 1,712 upvotes 99 comments February 11, 2026
Why AI roleplay will never actually satisfy you

**──────────────────────────────────**

I’ve been seeing lots of posts that discuss the subject of AI use as a way to connect with your F/O. The negative impact of AI has already been highlighted plenty of times in this sub, so I will not waste time repeating what’s already been said.

Instead, I will share my own experience with AI chat bots and try to explain why I found it lacking in comparison to other alternatives after a prolonged use. With this post I am hoping to reach either those who are tempted to try AI for roleplay or those who already do.

**──────────────────────────────────**

I’ll preface this by saying I used to be obsessed with chatting with AI chat bots. Find a new character I like? Janitor, here I come. New sona idea? Hell yes, plenty of bots to try it out with. And there were some very creative bots I saw people make too, which made me excited to write myself into their story. That lasted for around two years, coming in waves where I would be on my phone chatting 24/7 for around a week or so before getting burnt out and leaving for a few months before the cycle would begin all over again.

Now, I have reached a point where I can’t even look at AI-writing without feeling sick. Here are the reasons why that go beyond the common arguments you hear against AI:

>**| It stops feeling real. |**

No matter which model you use, if you have talked to a bot for long enough you will inevitably start seeing patterns in the way it talks, the way it describes things, the decisions it makes.

And I don’t mean just certain phrases like “you belong to me, mind body and soul” or “you’re playing with fire”. Oh no, what I mean is that every single sentence will have that artificial feel to it that you can’t explain. You may try to give it directions: to use less metaphors, tell it to sound more human. And it may make it bearable for a while. But once your brain has seen enough of it, nothing will ever help you get rid of that feeling of “something is off but I don’t know what”.

**Note:** To back up what I’m saying, here are some of the models I’ve tried and faced the same problems: Claude Opus 4.1, Claude Sonnet 4.5, Gemini Pro 2.5, DeepSeek R1T Chimera, DeepSeek R1, GLM 4.6, among others.

**૭ℓ ; ૭ℓ ; ૭ℓ**

>**| It doesn’t actually make sense. |**

This one is hard to see through at first. When you start out role playing with AI, it feels novel and magical. The plot seems to write itself and you truly feel like the potential is limitless. Sometimes, the AI even surprises you.

However, after a while you start realizing that the AI doesn’t actually think. You may think that this is obvious, but when you are fully engaged in the story it does not cross your mind that the AI makes things up as it goes for every individual reply, because from your human perspective the story makes sense. The AI does not have an understanding of set up and payoff. It is incapable of writing a good story and it does not understand how human relationships work.

If you’re still unconvinced, here’s the thing that really made me pause when I first realized it after hours of rerolls and frustrated OOC instructions — it does not actually have a consistent personality.

What do I mean by that? Let’s say your F/O is a confident, charming, flirtatious and extroverted dreamer who is a huge romantic, with his main flaw being that he is selfish when it comes to the things he wants. The AI will try to follow that description and act out that personality, using the language that is usually associated with such characters in media.

At first, it will seem to be successful, especially if the bot is very well-written. The problems will arise when it starts needing to make decisions that matter. Because that is when your beloved character will turn into a caricature of themselves with zero nuance or depth until it gets to a point where you need to spoon feed the AI for it to give you what you want.

Suddenly, the selfish dreamer who is faced with the possibility of their lover breaking up with them needs to decide how to act — do they grovel? do they use force? do they bargain? Their reaction will depend on countless factors that the AI simply will not take into account, because it doesn’t have an understanding of what “realistic” is. It will read the trait “selfish” and insert a reaction that it thinks will fit in accordance to what it’s seen before.

Perhaps it will make the character blame the user, because that is what selfish people do. However, that will be entirely out of character, because they are also a huge romantic and do not want to lose their lover. But that’s okay, we can reroll. Now they’re crying and apologizing, but wait, that character is also meant to be confident and self-assured, would their pride really allow them to go about it this way? See what I’m getting at here? There is no thought being put into this. It’s guess work through and through. And the AI goes through this exact process every single time it tries to think of a response for you.

Isn’t that so boring? You’re not even speaking to an approximate version of your F/O. You are speaking to a collection of traits that the AI thinks make your F/O who they are while masquerading as them and changing their tune every other reply. And God help you if your F/O has an actually complex and layered personality.

Also, you may argue that some models have a feature that forces them to “think”. I know what it is, I’ve used it and seen how it works, and I can confidently say that there is no actual meaningful thought happening there. It is still very narrow focused on tackling one task — responding to your LATEST message. It does not work as a means to make the story have foresight.

**૭ℓ ; ૭ℓ ; ૭ℓ**

>**| You can do better. |**

After chatting with AI for long enough, at a certain point you find yourself giving corrections to it more than actually role playing, and that is when it’s a good idea to take a step back and think if what you’re doing is actually worth it.

When the character has been mischaracterized, reduced to a stereotype, or simply made to sound artificial, even if technically still in character, it really ends up being easier to just use your head — fantasize, write your own stuff. Not because you’re purposefully trying to restrict yourself (if you’re addicted, this won’t work), but because it’s just… better.

I was honestly shocked by how much more in character I could make myself sound in comparison to anything the AI chat bots could give me. I will warn you though: if you have already been resorting to talking to AI for a while now, it will be hard at first. It was for me, at least. Because I have characters I used to role play with that are the complete opposite of me, and I was tempted to ask AI how they’d react in certain situations even as I tried to write it myself. And sometimes I did. And guess what? I ended up using none of the phrases it gave me and thought of my own that sounded 100 times better every single time.

Because your brain works well when it’s cornered. I was so profoundly disappointed with anything that AI could offer me that I simply had no other choice but to think of my own solutions just to satisfy that role playing itch, and that is when I learned that from the start AI was never going to be it for me or anyone who knows what quality writing is like.

**──────────────────────────────────**

**TL;DR:** AI role playing is a pointless endeavour because after you have chatted with it long enough you will understand that it does not think or actually understand your F/O’s character, as well as incapable of writing a consistent story that makes sense. If you rely on it as the sole means of connecting with your F/O you will only be set up for disappointment when the bot will inevitably stop feeling and sounding real. You are better off using your imagination or writing the same stories yourself.

**──────────────────────────────────**

Open Reddit thread
r/LocalLLaMA 518 upvotes 185 comments April 11, 2026
If you haven't yet given Gemma 4 a go...do it today

I have a modest rig that allows me to run Qwen 3.5 27B or even 35B via Ollama. Qwen has been amazing to work with and I've been fine with the slow drip trade-off.

Then Google released Gemma4.

Its fast - like 4 or 9B fast. Accuracy and confidence wise, reminds me of that first release of Gemini Pro that could actually produce code that would run.

As a "local guy" this shift in useability and confidence for a small self hosted LLM reminded me of what Deepseek brought to the table years ago with the thinking capability.

Give it a go when you have a chance, and apply the settings that google recommends, it does make a difference (slightly slower but better)

I tried a few releases and this one worked the best for all the tests I threw at it with law interpretation, python, brainstorming & problem solving.

bjoernb/gemma4-26b-fast:latest (not affiliated with whoever made this)

in the next few days I'll start checking the abliterated versions to see how they stand with pentest & sysec tasks vs Qwen

For testing https://github.com/witness-taco/ollama-benchmark-ui

Open Reddit thread
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