Google: Gemma 4 26B A4B

Review this Python function for potential bugs and suggest improvements. Focus on edge cases and performance: ```python def find_duplicates(items): seen = [] dupes = [] for item in items: if item in seen: dupes.append(item) seen.append(item) return dupes ```

The function has a critical performance issue: using `item in seen` on a list creates O(n²) complexity. Edge cases to consider: empty input returns empty list (correct), but duplicate duplicates get added multiple times to the result. Suggested fix: use a set for `seen` to achieve O(n) lookup, and use a set for `dupes` initially to prevent duplicate entries in the output, then convert to list if ordering matters. Also consider: what if items aren't hashable? You'd need a different approach for lists or dicts as items.

With a 262K token context window, Gemma 4 26B can review entire codebases in one pass, making it effective for architectural code review. This example shows its ability to identify both algorithmic complexity issues and subtle logic bugs. The multimodal capability means it could also review UI screenshots alongside code, though text-only code review remains its primary strength.

I have a video of my product prototype in action (30 seconds, showing the unboxing and setup process). Write a compelling product description for our landing page that highlights the user experience shown in the video.

Based on the video input, Gemma 4 26B would analyze the unboxing sequence, setup steps, and user interactions visible in the footage. It would then generate a description like: 'Setup takes under two minutes—no tools required. The magnetic connection system means you'll feel that satisfying click the moment components align correctly. Our packaging doubles as a storage case, and the quick-start guide is printed inside the lid so you'll never lose it. The LED indicator walks you through each step: blue for ready, green for connected, amber if something needs adjustment.'

The video modality support distinguishes this model from text-only alternatives at similar price points ($0.08/$0.26 per Mtok is mid-range). This example demonstrates cross-modal reasoning—translating visual information into marketing copy. The trade-off: video processing consumes tokens rapidly, so a 30-second video might use 50K+ tokens of your 262K budget, limiting how many clips you can analyze in one session.

Analyze this architectural diagram image and explain the data flow, potential bottlenecks, and suggest one optimization. The system handles 10K requests/second at peak.

Examining the diagram, Gemma 4 26B would identify components and connections, then provide analysis such as: 'Data flows from the load balancer through three API gateway instances to a single PostgreSQL primary. The bottleneck is clear: all write operations funnel through one database node. At 10K req/s, if even 20% are writes, you're pushing 2K writes/second through a single point of failure. Optimization: implement read replicas for the 80% of traffic that's read-heavy, and consider sharding the writes by user ID range across multiple primaries. This would distribute load and eliminate the single point of failure.'

Image understanding combined with technical reasoning makes Gemma 4 26B suitable for documentation analysis and system design review. The 26B parameter count provides enough reasoning depth for multi-step technical analysis. However, without public benchmarks, it's difficult to quantify accuracy against specialized architecture review tools—you'll want to validate its suggestions against your specific domain knowledge.

When Gemma 4 26B handles screenshots and spec sheets together

A 12-person SaaS company needs to turn customer support tickets—often containing screenshots, error logs, and video clips—into structured documentation updates. Gemma 4 26B processes image, text, and video in a single pass at $0.08/$0.26 per Mtok, which pencils out to roughly $0.15 per complex ticket when you're averaging 8K tokens in and 3K out. The 262K context window means you can load an entire product manual alongside the ticket media without chunking. If your tickets are text-only or you're processing fewer than 200/day, Claude Haiku costs less and runs faster. But once you cross into mixed media at volume, Gemma 4 26B's multi-modal handling and context depth justify the spend.

Why Gemma 4 26B works for contract review with exhibits

A 4-attorney firm receives contracts with 20-40 pages of exhibits—floor plans, financial statements, scanned addenda. Gemma 4 26B ingests the entire package (text plus images) in one 80K-token request, extracts key terms, and flags non-standard clauses. At $0.08 input, that's $6.40 per contract batch, and the 262K window means no pagination logic. The trade-off: without public benchmarks, you're flying blind on accuracy versus GPT-4o or Claude Opus. Run a 20-contract pilot and compare extraction precision before committing. If precision matters more than cost, start with Opus and step down only after you've validated Gemma's output on your clause types.

When Gemma 4 26B's video modality beats frame-by-frame pipelines

A 30-person creator platform moderates 500 user-uploaded videos daily, each 2-5 minutes long. Gemma 4 26B accepts video directly, so you skip the frame-extraction step that older pipelines require. At $0.08 input and roughly 40K tokens per video, you're spending $3.20 per video analyzed—half the cost of running GPT-4V on extracted frames. The 262K context window lets you process a full 5-minute clip without splitting. The risk: no public benchmarks means you can't verify false-positive rates against known datasets. Start with a 100-video shadow run alongside your current system, measure agreement, and switch only if Gemma matches your precision floor. If you're under 200 videos/day, the setup cost outweighs the per-unit savings.