Both models have improved intelligence on Artificial Analysis index with lower end-to-end response time. Also 24% to 50% improved output token efficiency (resulting in lower cost).
Gemini 2.5 Flash-Lite improvements include better instruction following, reduced verbosity, stronger multimodal & translation capabilities. Gemini 2.5 Flash improvements include better agentic tool use and more token-efficient reasoning.
Model strings: gemini-2.5-flash-lite-preview-09-2025 and gemini-2.5-flash-preview-09-2025
I'm not even sure how to evaluate what a "better" LLM is, when I've tried running the exact same model (Qwen3) and prompt and gotten vastly different responses on Qwen Chat vs OpenRouter vs running the model locally.
2.0 Flash is significantly cheaper than 2.5 Flash, and is/was better than 2.5-Flash-Lite before this latest update. It's a great workhorse model for basic text parsing/summary/image understanding etc. Though looks like 2.5-Flash-Lite will make it redundant.
I’ve been tinkering with the last version for code gen. This update might finally put it on par with Claude for latency. Anyone tried benchmarking the new preview yet?
Google seems to be the main foundation model provider that's really focusing on the latency/TPS/cost dimensions. Anthropic/OpenAI are really making strides in model intelligence, but underneath some critical threshold of performance, the really long thinking times make workflows feel a lot worse in collaboration-style tools, vs a much snappier but slightly less intelligent model.
It's a delicate balance, because these Gemini models sometimes feel downright lobotomized compared to claude or gpt-5.
Gemini 2.5 Flash has been the LLM I've used the most recently for a variety of domains, especially image inputs and structured outputs which beat both OpenAI and Anthropic in my opinion.
Okay this is a nitpick but why wouldn't you increment a part of the version number to signify that there is an improvement? These releases are confusing.
Google has historically always made bad UX choices like this. Conway’s law definitely applies here. Too many different silos building every Google project.
This really captures something I've been experiencing with Gemini lately. The models are genuinely capable when they work properly, but there's this persistent truncation issue that makes them unreliable in practice.
I've been running into it consistently, responses that just stop mid-sentence, not because of token limits or content filters, but what appears to be a bug in how the model signals completion. It's been documented on their GitHub and dev forums for months as a P2 issue.
The frustrating part is that when you compare a complete Gemini response to Claude or GPT-4, the quality is often quite good. But reliability matters more than peak performance. I'd rather work with a model that consistently delivers complete (if slightly less brilliant) responses than one that gives me half-thoughts I have to constantly prompt to continue.
It's a shame because Google clearly has the underlying tech. But until they fix these basic conversation flow issues, Gemini will keep feeling broken compared to the competition, regardless of how it performs on benchmarks.
Another issue: Gemini can’t do tool calling and (forced) json output at the same time
If you want to use application/json as the specified output in the request, you can’t use tools
So if you need both, you either hope it gives you correct json when using tools (which many times it doesn’t). Or you have to do two requests, one for the tool calling, another for formatting
At least, even if annoying, this issue is pretty straightforward to get around
When this happened to me it was because, I can only guess, it was the Gemini servers were overloaded. Symptoms: Gemini model, Opaque API wrapper error, truncated responses. To be fair the Anthropic servers are overloaded a lot too but they have a clear error. I gave Gemini a few days on the bench and it fixed itself without any client side changes. YMMV.
> Today, we are releasing updated versions of Gemini 2.5 Flash and 2.5 Flash-Lite, available on Google AI Studio and Vertex AI, aimed at continuing to deliver better quality while also improving the efficiency.
Typo in the first sentence? "... improving the efficiency." Gemini 2.5 Pro says this is perfectly good phrasing, whereas ChatGPT and Claude recognize that it's awkward or just incorrect. Hmm...
Why do all of these model providers have such issues naming/versioning them? Why even use a version number (2.5) if you aren't going to change it when you update the model?
This industry desperately needs a Steve Jobs to bring some sanity to the marketing.
The switch by Artificial Analysis from per-token-cost to per-benchmark-cost shows some effect!
Its nice that labs are now trying to optimize what I actually have to pay to get an answer - It always annoys me to have to pay for all the senseless rambling of the less-capable reasoning models.
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[ 3.4 ms ] story [ 78.8 ms ] threadAnthropic learned this lesson. Google, Deepseek, Kimi, OpenAI and others keep repeating it. This feels like Gemini_2.5_final_FINAL_FINAL_v2.
Both models have improved intelligence on Artificial Analysis index with lower end-to-end response time. Also 24% to 50% improved output token efficiency (resulting in lower cost).
Gemini 2.5 Flash-Lite improvements include better instruction following, reduced verbosity, stronger multimodal & translation capabilities. Gemini 2.5 Flash improvements include better agentic tool use and more token-efficient reasoning.
Model strings: gemini-2.5-flash-lite-preview-09-2025 and gemini-2.5-flash-preview-09-2025
From OpenRouter last week:
* xAI: Grok Code Fast 1: 1.15T
* Anthropic: Claude Sonnet 4: 586B
* Google: Gemini 2.5 Flash: 325B
* Sonoma Sky Alpha: 227B
* Google: Gemini 2.0 Flash: 187B
* DeepSeek: DeepSeek V3.1 (free): 180B
* xAI: Grok 4 Fast (free): 158B
* OpenAI: GPT-4.1 Mini: 157B
* DeepSeek: DeepSeek V3 0324: 142B
Something that distinguishes between a completely new pre-training process/architecture, and standard RLHF cycles/optimizations.
Flash is super fast, gets straight to the point.
Pro takes ages to even respond, then starts yapping endlessly, usually confuses itself in the process and ends up with a wrong answer.
It's a delicate balance, because these Gemini models sometimes feel downright lobotomized compared to claude or gpt-5.
today: "before you marry someone, put the person in front of a slow AI model"
;-)
I've been running into it consistently, responses that just stop mid-sentence, not because of token limits or content filters, but what appears to be a bug in how the model signals completion. It's been documented on their GitHub and dev forums for months as a P2 issue.
The frustrating part is that when you compare a complete Gemini response to Claude or GPT-4, the quality is often quite good. But reliability matters more than peak performance. I'd rather work with a model that consistently delivers complete (if slightly less brilliant) responses than one that gives me half-thoughts I have to constantly prompt to continue.
It's a shame because Google clearly has the underlying tech. But until they fix these basic conversation flow issues, Gemini will keep feeling broken compared to the competition, regardless of how it performs on benchmarks.
https://github.com/googleapis/js-genai/issues/707
https://discuss.ai.google.dev/t/gemini-2-5-pro-incomplete-re...
If you want to use application/json as the specified output in the request, you can’t use tools
So if you need both, you either hope it gives you correct json when using tools (which many times it doesn’t). Or you have to do two requests, one for the tool calling, another for formatting
At least, even if annoying, this issue is pretty straightforward to get around
I’ve seen that behavior when LLMs of any make or model aren’t given enough time or allowed enough tokens.
Gemini 2.5 Pro is _amazing_ for software architecture, but I just get tired of poking it along. Sonnet does well enough.
Typo in the first sentence? "... improving the efficiency." Gemini 2.5 Pro says this is perfectly good phrasing, whereas ChatGPT and Claude recognize that it's awkward or just incorrect. Hmm...
Pelicans: https://github.com/simonw/llm-gemini/issues/104#issuecomment...
This industry desperately needs a Steve Jobs to bring some sanity to the marketing.