January 6, 2025 7 min read

Gemini 2.0 Flash Experimental: What It Means for Azure-First Teams

Gemini Google AI LLM Multimodal Azure OpenAI

Google spent December 2024 shipping. Gemini 2.0 Flash, a new SDK, a real-time streaming API and a “thinking” model all landed within about a week, and the coverage blurred what is usable now with what is promised for later. If your platform is built on Azure OpenAI, you need to know which parts are worth evaluating this quarter and which parts you should ignore until they reach general availability.

I last compared the two ecosystems in Gemini vs GPT: Practical Comparison for Enterprise Applications, back when the line-up was Gemini Pro and Ultra. This post covers what changed with 2.0, with the release status of each piece as of early January 2025.

What actually shipped

The first thing to fix is the name. There is no “Gemini 2 Pro” you can call today. On 11 December 2024 Google released Gemini 2.0 Flash as an experimental model, model ID gemini-2.0-flash-exp, in Google AI Studio and Vertex AI. Google’s developer announcement says general availability and more model sizes will follow in January.

Item	Status on 6 January 2025	What you get
Gemini 2.0 Flash (`gemini-2.0-flash-exp`)	Experimental	Multimodal input (text, image, audio, video), text output, about 1M input tokens, 8K output tokens
Native image output and text-to-speech	Early-access partners only	Not available to most developers yet
Native tool use (Google Search, code execution)	Experimental, with 2.0 Flash	The model can decide to call Search or run Python itself
Multimodal Live API	Experimental	Bidirectional WebSocket streaming of audio and video, with tool use
Gemini 2.0 Flash Thinking (`gemini-2.0-flash-thinking-exp-1219`)	Experimental, released 19 December	Reasoning model that shows its thought process before answering
Google Gen AI SDK (`google-genai`)	Pre-1.0 (0.3.0 on PyPI)	One client for the Gemini Developer API and Vertex AI
Gemini 1.5 Pro and 1.5 Flash	GA	The production models today, including 1.5 Pro’s 2M-token context

The headline claim in the same announcement is that 2.0 Flash beats 1.5 Pro on key benchmarks at roughly twice the speed. That claim is the reason to pay attention, because Flash and Pro sit at very different price points. On the published Gemini API pricing as of January 2025, GA Gemini 1.5 Flash costs US$0.075 per million input tokens and US$0.30 per million output tokens for prompts up to 128K tokens. GPT-4o mini, the nearest GA option on Azure OpenAI, lists at US$0.15 input and US$0.60 output per million. If 2.0 Flash lands anywhere near 1.5 Flash pricing with Pro-level quality, that changes the cost maths for high-volume workloads such as document extraction and classification. But 2.0 Flash has no published price yet, it is still Google’s benchmark, and an experimental model has no SLA.

The new SDK matters more than it looks

The SDK news got less attention than the model, but it matters more for anyone maintaining code. Until now Google had two Python stories: google-generativeai for the AI Studio API and the Vertex AI SDK (import vertexai, installed from google-cloud-aiplatform) for Google Cloud. The new google-genai SDK puts both behind one Client, and you switch backends with constructor arguments.

import os

from google import genai
from google.genai import types

# Gemini Developer API (Google AI Studio key)
client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])

# Same code against Vertex AI instead:
# client = genai.Client(vertexai=True, project="<your-project-id>", location="us-central1")

response = client.models.generate_content(
    model="gemini-2.0-flash-exp",
    contents="Compare a lakehouse and a warehouse for a 20-person analytics team in five bullet points.",
    config=types.GenerateContentConfig(
        system_instruction="You are a pragmatic data architect. Be concise.",
        temperature=0.3,
        max_output_tokens=1024,
    ),
)

print(response.text)

It is the same pattern Azure teams already know from the openai package’s AzureOpenAI client. You prototype against the cheap, quick-to-provision endpoint, then move to the enterprise one, where Vertex AI gives you IAM, VPC Service Controls and regional controls, without rewriting the calling code. Treat the pre-1.0 version number seriously, though. Five releases shipped in its first week (0.1.0 to 0.3.0), so pin the exact version in requirements.txt.

Native tool use: grounding without the plumbing

The 2.0 feature I find most interesting for enterprise work is that Google Search is now a tool the model calls itself, not a separate retrieval step you wire up. You declare the tool and the model decides when to search. The response carries grounding metadata with the source URLs.

import os

from google import genai
from google.genai import types

client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])

response = client.models.generate_content(
    model="gemini-2.0-flash-exp",
    contents="What did Microsoft announce for Microsoft Fabric at Ignite 2024?",
    config=types.GenerateContentConfig(
        tools=[types.Tool(google_search=types.GoogleSearch())],
    ),
)

print(response.text)

metadata = response.candidates[0].grounding_metadata
if metadata and metadata.grounding_chunks:
    for chunk in metadata.grounding_chunks:
        print(f"- {chunk.web.title}: {chunk.web.uri}")

Two cautions. Older code that used the 1.5-era google_search_retrieval field does not carry over: 2.0 expects google_search. More importantly, grounding on the public web is not grounding on your data. For most of the organisations I work with, the hard question is “what does our policy say”, not “what happened on the internet this week”. That still needs a retrieval layer over your own content, whether that is Vertex AI Search on Google Cloud or Azure AI Search on Azure. Search-as-a-tool is excellent for market and competitor research assistants. It does not replace RAG.

Multimodal input is the real differentiator

Gemini has been natively multimodal since 1.0, and 2.0 Flash keeps that lead. Images, audio and video are all first-class inputs to the same call. For a data team, the useful cases are less glamorous than the demos: reading architecture diagrams, extracting tables from scanned forms, and summarising recorded walkthroughs.

import os

from google import genai
from google.genai import types

client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])

with open("architecture-diagram.png", "rb") as f:
    image_bytes = f.read()

response = client.models.generate_content(
    model="gemini-2.0-flash-exp",
    contents=[
        types.Part.from_bytes(data=image_bytes, mime_type="image/png"),
        "List every component in this diagram, the data flows between them, "
        "and any single points of failure.",
    ],
)

print(response.text)

For video and long audio, the Gemini Developer API uses client.files.upload(path=...), and the uploaded file must finish processing before you reference it. Vertex AI does not support that upload path. There you reference objects in Cloud Storage instead, which is one of the places the “same code on both backends” promise breaks down.

Live API and Flash Thinking: watch, don’t build

The Multimodal Live API streams audio and video in both directions over a WebSocket and supports tool calls mid-conversation. It is Google’s answer to the gpt-4o-realtime-preview model that Azure OpenAI has offered in public preview since October 2024. Both are previews, and both have the same hard problems: interruption handling, latency budgets, and working out how you would evaluate a voice conversation at all. If you are already prototyping a voice agent on Azure, I wouldn’t switch stacks for this. Run the same scripted test conversations against both and compare latency and tool-call reliability.

Gemini 2.0 Flash Thinking is a reasoning model in the same family as OpenAI’s o1, which reached Azure OpenAI as limited-access o1 (2024-12-17) in December. Google’s version exposes its thinking rather than hiding it, which helps with debugging prompts. It also has a much smaller context window than the base Flash model and is free only because it is experimental. The decision guide in When to Use o1 applies equally to Flash Thinking: reach for a reasoning model only when the task genuinely needs multi-step reasoning, not as a default.

How I’d approach it from an Azure-first platform

My position: run an evaluation of Gemini 2.0 Flash now, and put nothing experimental into production. Experimental models in AI Studio are rate-limited, have no SLA, and can change or disappear without the deprecation notice you’d get for a GA model version. Google shipped three different experimental model IDs in December alone (gemini-exp-1206, gemini-2.0-flash-exp and gemini-2.0-flash-thinking-exp-1219), and older -exp IDs such as gemini-exp-1121 have already been superseded.

Data use is the other caveat. Under the Gemini API terms, Google may use prompts and responses from the free tier to improve its products; the paid tier and Vertex AI do not. Run any evaluation that touches real or sensitive data on Vertex AI or a paid-tier key, never on a free AI Studio key.

What’s worth doing in the next few weeks:

Build a model-agnostic evaluation set. If you don’t have 50 to 100 representative prompts with expected outputs for your main use cases, that is the actual gap, regardless of vendor. Run them against GPT-4o on Azure OpenAI and gemini-2.0-flash-exp, and compare quality, latency and failure modes.
Test multimodal extraction specifically. Diagram reading, scanned documents and video summaries are where Gemini has historically been strongest. If 2.0 Flash is clearly better for one of these workloads, that is a real reason to add a second provider.
Keep the abstraction thin. A small internal interface (prompt in, structured result out) is enough to swap providers per use case. Heavy multi-provider frameworks add more surface than they save at this stage.

When I would not bother:

Your data, identity and governance all sit in Azure and Microsoft Entra, and you have no multimodal workload. Adding Google Cloud means a second set of IAM, network controls, data residency reviews and billing. That overhead is rarely worth it for a marginal quality gain on text tasks.
You need Australian data residency for regulated data. Confirm regional availability for the specific model version on Vertex AI before you plan anything. Experimental models typically launch in a narrow set of regions.

Where I land

Gemini 2.0 Flash is the most interesting thing Google has shipped for developers in a year, mainly because of price-performance and multimodal input, not chat quality. But until it reaches GA with published pricing, it belongs in your evaluation harness and not your production architecture. Build the harness now. The models will keep moving, and a harness is how you avoid re-deciding your vendor strategy every time a new one launches.