Updated: 2026-07-07

Google announced Gemini 1.5 Pro on February 15, 2024 with a figure that reshaped the LLM conversation: 1 million token context (internal tests had already reached 10M). Compared to GPT-4 Turbo’s 128k or Claude 2.1’s 200k, it is an order-of-magnitude jump. This article covers what ultra-long context really enables, how it affects RAG, and the obstacles still present.

Key takeaways

  • 1M token context is real and measurable: in "needle in haystack" tests, Gemini 1.5 retrieves >95% up to 530k tokens and degrades gradually.

  • Long context does not kill RAG: cost ($7 per million input tokens) and latency (30-60s) still favour targeted retrieval for most queries.

  • Google’s context caching reduces real cost by 75-90% for repeated use on the same corpus.

  • The "lost in the middle" problem persists: models attend to the beginning and end of context more than the centre.

  • The Mixture of Experts (MoE) architecture explains the inference efficiency that makes such long context viable.

What Gemini 1.5 Pro brings

  • Mixture of Experts (MoE) architecture: explains inference efficiency.

  • 1M tokens context in general availability, up to 10M experimental.

  • Native multimodal: text, image, audio, and video in the same prompt.

  • Quality equivalent to Gemini 1.0 Ultra at lower per-token cost.

  • Available on Google AI Studio and Vertex AI.

The 1M figure is marketing, but it is measurable: in "needle in haystack" tests (finding a buried fact in a long corpus), Gemini 1.5 retrieves >95% up to 530k tokens and degrades gradually. Not perfect attention, but usable.

What 1M tokens means

For concrete perspective:

  • 1M tokens is roughly 750k words, or 3-4 full books.

  • The complete Lord of the Rings (~500k words) fits comfortably.

  • A mid-size codebase (~500k lines) fits.

  • All transcribed company meetings for a month fit.

This rewrites what is possible to include in a single LLM prompt.

Impact on RAG

"Does Gemini 1.5 kill RAG?" Short answer: no, but it changes the game.

RAG remains relevant for several concrete reasons:

  • Cost. 1M input tokens in Gemini 1.5 Pro costs ~$7. At 1,000 queries/day that is $7,000/day. RAG with embeddings and targeted retrieval can be 100x cheaper.

  • Latency. Processing 1M tokens takes ~30-60 seconds. Typical RAG responds in 2-5 seconds.

  • Precision. Even with high recall, the LLM can "lose" information across 100k tokens. For precision-critical queries, targeted retrieval wins.

  • Updates. 1M tokens is not your entire dynamic corporate database: it still needs retrieval for changing content.

Cases where Gemini 1.5 changes the rules:

  • Single very long document analysis (contract, report, transcript): better to feed it whole than chunk it.

  • Multi-document with cross-references: if 10 documents refer to each other, including all of them in context preserves relationships.

  • Entire codebase for assisted development tasks.

  • Small-to-mid knowledge bases (under 1M total tokens) with caching: economically viable.

Context caching: the key piece

Google introduced context caching to amortise long-context cost: load the large document once, cache it, and subsequent queries against the same context become much cheaper. Real cost drops 75-90% in repeated-context use cases. This enables economical "fat RAG" (a single LLM call with lots of context) as a viable pattern.

This pairs well with proxy architectures like LiteLLM for managing caches and costs across multiple providers.

Multimodal in the same prompt

Gemini 1.5 processes image, audio, and video natively in the same request:

from google.generativeai import GenerativeModel

model = GenerativeModel("gemini-1.5-pro")
response = model.generate_content([
    "Resume lo que ocurre en este vídeo:",
    {"inline_data": {"mime_type": "video/mp4", "data": video_bytes}}
])

Video is processed frame by frame (~1 frame/s). A 1-hour video equals ~3,600 frames; each frame tokenises to ~258 tokens; total ~930k tokens, which fits comfortably in context. Transformative use cases: recorded meeting analysis, long podcast indexing, QA over educational video, compliance review of call centre recordings.

Real limitations

Honestly:

  • Operational cost: even with caching, 1M tokens is not cheap.

  • Latency: tens of seconds to respond. Not suitable for interactive chat.

  • Hallucination persists: long context does not guarantee precision.

  • "Lost in the middle": models attend to beginning and end of context more than the centre; documents in the middle have lower recall.

  • Regional availability: Gemini is not available in all regions.

  • Compliance: privacy and regulation integration is additional work.

Prompt engineering considerations

Long prompts need specific techniques:

  • Repeat instructions at the end of the context: the model tends to ignore instructions placed very early.

  • Use clear delimiters: <document> tags or similar markers.

  • Number sections so the model can reference them.

  • Chain of thought helps even more with long context: asking it to "analyse step by step" makes a measurable difference.

How to evaluate quality

Do not rely solely on the official "needle in haystack" benchmark. Run your own evaluation:

  • Queries over domain documents of 500k+ tokens: measure real recall in your data.

  • Comparison with targeted RAG: same query, both systems, judge quality side by side.

  • Cost per query with context caching in your specific use case.

  • p50/p95 latency with your typical context sizes.

A golden set of 50-100 queries with expected answers allows objective comparison. This evaluation practice complements the approach covered in open LLM evaluation for building robust internal benchmarks.

Architectural design with long context

How your stack might change:

  • More selective retrieval with larger context: fewer chunks, each one bigger.

  • Per-user context cache for personalisation.

  • Multi-stage retrieval: retrieve first, then a long-context LLM call, then response.

  • Small models with preloaded context for repetitive tasks where the corpus is fixed.

The optimal architecture depends on the use case. "More context" does not solve everything: it is a tool, not a panacea.

Conclusion

Gemini 1.5 Pro is a real leap in long-context processing capability. It changes LLM architectural possibilities and makes previously unviable cases feasible. It does not replace targeted RAG (cost and latency still favour retrieval for many cases), but broadens the solution range. The long-context race is not over, and next iterations will continue pushing the ceiling.