We Fed It an Entire Codebase. Here Is What Happened.
When Google announced Gemini 1.5 Pro with a one-million-token context window in February 2024, the AI world collectively lost its mind. One million tokens. That is roughly 750,000 words. Entire novels. Full codebases. Hours of meeting transcripts. All in a single prompt.
We had to test it. Not with toy examples or cherry-picked demos, but with the kind of messy, real-world inputs our clients actually deal with. So we spent three weeks running Gemini 1.5 Pro through everything we could throw at it -- legal document sets, multi-file codebases, 400-page policy manuals, and six hours of transcribed client calls.
The results were genuinely surprising. Not because the million-token window is fake -- it is very real. But because "can hold a million tokens" and "can reason well across a million tokens" are two very different statements.
A million-token context window is not a million-token reasoning engine. The window is real. The quality gradient across that window is what matters.
What Gemini 1.5 Pro Actually Handles Well
The strongest use case we found was needle-in-a-haystack search across massive document sets. We uploaded a 200,000-word collection of HR policy documents for a client and asked Gemini to find every mention of remote work eligibility across the entire corpus. It nailed it. Every reference, cross-referenced with the specific document and section.
This is genuinely transformative for certain workflows. Law firms reviewing discovery documents. Compliance teams auditing policy manuals. Research teams scanning academic papers. When the task is "find this specific thing across a massive amount of text," Gemini 1.5 Pro with its million-token window is the best tool available. As we discussed in our piece on AI for law firms, document search is one of the highest-ROI applications of AI in professional services.
Code analysis was similarly impressive. We fed it an entire Next.js application -- roughly 180 files, 45,000 lines of code -- and asked it to trace data flow from the API layer through to the frontend rendering. It produced a coherent map of the data flow that was about 85 percent accurate, which is remarkable given that it was working with the full codebase in a single pass.
Transcript analysis was another standout. Six hours of sales call recordings, transcribed and uploaded. We asked Gemini to identify recurring objections across all calls and rank them by frequency. The output was structured, accurate, and genuinely useful for the sales team.
Where the Million-Token Window Struggles
Here is where things get honest. The million-token window is impressive for retrieval and search tasks. It is notably weaker for tasks that require sustained reasoning across the full context.
We gave Gemini a 300-page contract set and asked it to analyze each contract against a 12-point evaluation rubric, then produce a comparative matrix. By the third contract, the analysis criteria had subtly shifted. By the seventh, it was applying different standards than it had for the first. The model could hold all the text, but it could not maintain analytical consistency across the full span.
Instruction following also degraded at scale. With shorter inputs, Gemini follows formatting and structural instructions well. But when we loaded 500,000 tokens of source material plus a detailed 2,000-word system prompt, the system prompt instructions started getting partially ignored. The model prioritized processing the content over following our specific output requirements.
This is not a fatal flaw. It is a characteristic you need to design around. If you understand that quality degrades as you approach the far end of the context window, you can structure your workflows accordingly -- chunking tasks, using the large window for retrieval and smaller windows for analysis.
The practical question is not "how many tokens can the model hold?" It is "how many tokens can the model hold while still doing useful work?" Those are different numbers.
Gemini 1.5 Pro vs Claude 3.5 vs GPT-4o: The Honest Comparison
We run all three models in production across different client engagements. Each has a distinct sweet spot. Here is how they actually compare on the dimensions that matter for business use. We covered the Claude vs ChatGPT comparison in depth in our head-to-head review, but Gemini deserves its own seat at the table.
When to Use Which
After three weeks of testing, our recommendation framework is straightforward.
Use Gemini 1.5 Pro when your task is primarily search and retrieval across very large document sets. If you need to find specific information buried in hundreds of thousands of words, nothing else comes close. The 2 million token window that Google made available to developers in mid-2024 extends this advantage even further. The price point is also the most competitive of the three -- at $1.25 per million input tokens for prompts under 128K, it is significantly cheaper than both Claude and GPT-4o for high-volume retrieval tasks.
Use Claude when the task requires sustained analytical reasoning, complex instruction following, or coding. Claude's 200K context window is smaller, but the quality remains remarkably consistent across the full window. For the kind of work we do -- building AI agents, analyzing contracts, processing structured business data -- Claude is the better tool. We explain the reasoning behind this choice in our piece on agent memory versus context windows.
Use GPT-4o when you need multimodal capabilities (image analysis, voice), when you need the broadest plugin ecosystem, or when employee familiarity matters more than raw analytical power.
The Bigger Picture: Context Windows Are Not Memory
Here is the thing that most coverage of Gemini's million-token window misses entirely. A large context window is not the same as memory. When the conversation ends, every token in that window disappears. The model retains nothing from one session to the next.
For businesses building AI into their operations as persistent agents -- not just one-off tools -- this matters enormously. You can feed Gemini your entire codebase today, but tomorrow it will have forgotten every insight it generated. The context window is a desk, not a filing cabinet.
This is why we continue to build primarily on Claude, supplemented by Gemini for specific large-context retrieval tasks. Claude's ecosystem -- particularly the tools Anthropic is building around persistent memory and agent infrastructure -- is solving the harder problem. The million-token window is impressive engineering. But the business value lives in what happens after the conversation ends.
Gemini 1.5 Pro is a genuinely useful tool. We use it. We recommend it for the right use cases. But it is not a replacement for Claude or GPT-4o -- it is a complement to them. The largest context window wins a specific category of tasks. For everything else, the model that reasons best within its window still wins.