What other models does Google offer?

Google's Gemini family spans several tiers designed for different cost and capability trade-offs. Here are some other models Google supports:

• Gemini 2.5 Flash: Gemini 2.5 Flash is Google's mid-tier reasoning-capable model, offering faster output speeds (206.6 tokens/sec as of 06/01/2026) and a 1M token context window at a lower price point than 2.5 Pro, making it the preferred option when cost efficiency matters more than peak accuracy

• Gemini 2.0 Flash: Gemini 2.0 Flash is a lightweight, low-latency model in the 2.x generation, priced at $0.15 per million input tokens, suited for high-volume workloads that don't require extended thinking or the highest benchmark performance

• Gemini 1.5 Pro: Gemini 1.5 Pro is a previous-generation flagship from Google notable for its 2M token context window, the largest available in Google's public lineup, making it valuable for extremely long-document tasks even as newer models have surpassed it on most benchmarks

• Gemini 1.5 Flash: Gemini 1.5 Flash is a cost-optimized model from the 1.5 generation, positioned below 1.5 Pro in capability but designed for high-throughput, low-latency use cases where minimal per-token cost is the primary constraint

How does Gemini 2.5 Pro differ from Google's other models?

Gemini 2.5 Pro sits at the top of Google's current lineup as the flagship reasoning model, positioned for tasks where maximum accuracy justifies higher cost and latency.

• Pricing: Input costs $1.25 per million tokens (or $2.50 for prompts over 200K tokens); output costs $10.00 per million tokens. This is roughly 4x the input cost of Gemini 2.5 Flash and over 8x the input cost of Gemini 2.0 Flash

• Context window: Supports a 1M token context window, matching Gemini 2.5 Flash and 2.0 Flash, but half the 2M window available in Gemini 1.5 Pro

• Speed: Output speed of 129.7 tokens/sec is fast for a reasoning model but slower than Gemini 2.5 Flash at 206.6 tokens/sec. Time to first token averages 20.98 seconds, reflecting the extended thinking process

• Intelligence Index ranking: Ranked #79 of 150 models on the Artificial Analysis Intelligence Index with a score of 35, placing it just below the median of 36 across all evaluated models

• Capabilities: Supports extended thinking and multimodal inputs including text, images, speech, and video. It is the only current Google model with extended thinking enabled by default

Gemini 2.5 Pro is best suited for complex reasoning tasks, multi-step analysis, and workloads where accuracy on hard problems outweighs the cost premium over Flash-tier models.

What models should I consider using alongside Gemini 2.5 Pro?

No single model is optimal for every task. Here are models worth pairing with Gemini 2.5 Pro depending on what your product needs:

• Gemini 2.5 Flash: Route lighter tasks like summarization, classification, and short-form generation to Gemini 2.5 Flash to capture roughly an 8x reduction in output cost while keeping the same provider and context window

• Claude Opus 4 (Anthropic): For complex multi-document legal or research analysis requiring careful instruction following, Claude Opus 4 provides an alternative flagship-tier option that may outperform on instruction-tuned tasks

• GPT-4o (OpenAI): For tool use and function-calling pipelines where OpenAI's ecosystem integrations are already in place, GPT-4o can handle agentic workflows with lower latency than reasoning-focused models

• Llama 3.3 70B (Meta): For workloads where you need a capable open-weight model to run on private infrastructure or reduce provider dependency, Llama 3.3 70B handles general instruction following at a fraction of the cost of proprietary flagships

• Gemini 2.0 Flash (Google): For very high-volume, low-complexity requests within the same Google ecosystem, Gemini 2.0 Flash at $0.15 per million input tokens can serve as a cost-efficient fallback for the simplest query types

What are the challenges of using Gemini 2.5 Pro in my product?

Like any production LLM, Gemini 2.5 Pro comes with tradeoffs worth planning for:

• High time to first token: Average TTFT of 20.98 seconds makes Gemini 2.5 Pro unsuitable for real-time or low-latency user-facing interactions. Applications expecting sub-second responses will need to route those requests elsewhere

• Output cost at scale: At $10.00 per million output tokens, verbose responses compound quickly. A product generating 100M output tokens per month faces $1,000 in output costs from this model alone, making active token budget management critical

• Provider dependency: Running exclusively on Gemini 2.5 Pro through Google's API means any Google AI outage or quota enforcement directly interrupts your service. There is no automatic fallback without additional routing infrastructure

• Cost at scale: Input pricing at $1.25 per million tokens doubles to $2.50 for prompts exceeding 200K tokens, meaning long-context workloads that seem affordable at low volume become expensive quickly as usage scales

• Reasoning latency tradeoff: Extended thinking improves accuracy on hard tasks but introduces non-deterministic processing time. Applications that need consistent, predictable response times will find the latency variance difficult to manage without a fallback routing strategy

Why should I use Merge Gateway to route LLM requests with Gemini 2.5 Pro and every other model?

Using Gemini 2.5 Pro through Merge Gateway gives you access to the model itself and the infrastructure layer around it:

• One API, every provider: Access Gemini 2.5 Pro and every other major LLM through a single endpoint and API key. Change providers by swapping the model string (no application code changes required)

• Intelligent routing and automatic failover: Merge routes around Google outages automatically. Routing policies based on cost, latency, or quality can reduce spend by 40–60% without touching your application code

• Cost governance: Set hard or soft project budgets so Gemini 2.5 Pro spend stays within plan. Every request is attributed to a model, project, and tag in a unified billing dashboard across all providers

• Build Your Own Router: Define what "best" means for your traffic by selecting from curated ML benchmarks or adding your own eval scores. The router scores each available model against your weights and picks the winner per request, with a plain-language explanation of every decision

• Security and compliance controls: Apply DLP rules and prompt injection protection before every request reaches Google. Enforce per-project model and region policies without adding that logic to your application

How can I start using Merge Gateway to route requests with Gemini 2.5 Pro?

Getting Gemini 2.5 Pro running through Merge Gateway takes a few minutes:

1. Create an account and get your API key from the dashboard.

2. Install the Merge Gateway SDK: run pip install merge-gateway-sdk (Python) or npm install merge-gateway-sdk (Node). Alternatively, if you're already using the OpenAI SDK, set base_url = "https://api-gateway.merge.dev/v1/openai" and your existing code works as-is.

3. Make your first request using the provider/model format. For Gemini 2.5 Pro, the model string is google/gemini-2.5-pro. Swap the model string to route to any other provider without changing anything else.

4. Configure a routing policy in the dashboard to set failover behavior, cost limits, and optimization strategy. Your first policy can be as simple as naming Gemini 2.5 Pro as primary with one fallback.

Full setup instructions and SDK references are in the Merge Gateway docs.