What other models does Google offer?

Google's Gemini family covers multiple price and capability tiers, from lightweight flash models to full reasoning flagships. Here are some other models Google supports:

• Gemini 2.5 Pro: Gemini 2.5 Pro is Google's flagship reasoning model, supporting extended thinking and multimodal inputs across a 1M token context window, priced at $1.25 per million input tokens for prompts under 200K tokens (as of 06/01/2026) and suited for the most complex analytical tasks

• Gemini 2.0 Flash: Gemini 2.0 Flash is a previous-generation flash model from Google priced at $0.15 per million input tokens (as of 06/01/2026), positioned below Gemini 2.5 Flash in both capability and recency, and best suited for high-volume workloads that don't need the accuracy improvements in the 2.5 series

• Gemini 1.5 Pro: Gemini 1.5 Pro is a prior-generation flagship notable for its 2M token context window, which remains the largest in Google's public lineup and makes it useful for extremely long-document retrieval tasks even though newer models surpass it on standard benchmarks

• Gemini 1.5 Flash: Gemini 1.5 Flash is the cost-optimized tier from the 1.5 generation, designed for high-throughput, latency-sensitive workloads where per-token cost is the dominant constraint rather than benchmark accuracy

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

Gemini 2.5 Flash occupies the mid-tier in Google's current lineup, balancing competitive intelligence scores with output speeds that outpace the flagship 2.5 Pro.

• Pricing: Input costs $0.30 per million tokens; output costs $2.50 per million tokens (as of 06/01/2026). That is roughly one-quarter the input cost and one-quarter the output cost of Gemini 2.5 Pro, while remaining more expensive than Gemini 2.0 Flash at $0.15 input / $0.60 output

• Speed: Output speed of 206.6 tokens/sec ranks #4 across all models on Artificial Analysis (as of 06/01/2026), significantly faster than Gemini 2.5 Pro at 129.7 tokens/sec. Time to first token is 0.59 seconds, compared to 20.98 seconds for 2.5 Pro

• Context window: Supports a 1M token context window, matching Gemini 2.5 Pro and 2.0 Flash, and half of Gemini 1.5 Pro's 2M window

• Intelligence Index: Scored 21 on the Artificial Analysis Intelligence Index (as of 06/01/2026), ranking #32 among non-reasoning models evaluated, placing it above average for its price tier

• Output verbosity: Gemini 2.5 Flash generated approximately 17M output tokens during evaluation versus a 9.2M median (as of 06/01/2026), which means output cost per task can run higher than pricing per token suggests

Gemini 2.5 Flash is the strongest choice for latency-sensitive applications that need faster TTFT and throughput than 2.5 Pro can deliver, without dropping to the older 2.0 generation.

What models should I consider using alongside Gemini 2.5 Flash?

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

• Gemini 2.5 Pro: For tasks within your product that require extended reasoning or the highest available accuracy, route those specific requests upstream to Gemini 2.5 Pro while keeping Gemini 2.5 Flash as the default for general workloads

• Gemini 2.0 Flash: For the highest-volume, lowest-complexity tasks like short-form classification or simple extraction, Gemini 2.0 Flash at $0.15 per million input tokens (as of 06/01/2026) reduces costs on requests that don't benefit from 2.5-generation improvements

• Claude Haiku 3.5 (Anthropic): For structured output tasks requiring strict JSON adherence at low cost, Claude Haiku 3.5 provides a cross-provider alternative at comparable speed and pricing tiers

• GPT-4o mini (OpenAI): For workloads where OpenAI tool-call compatibility is required or existing integrations are already built on the OpenAI SDK, GPT-4o mini covers many of the same cost-efficient use cases as Gemini 2.5 Flash

• Llama 3.3 70B (Meta): For teams that need self-hosted or private deployment to satisfy data residency requirements, Llama 3.3 70B provides a capable open-weight alternative for instruction-following tasks where cloud APIs are restricted

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

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

• Output verbosity driving cost: Gemini 2.5 Flash generated approximately 17M output tokens during Artificial Analysis evaluation versus a 9.2M median (as of 06/01/2026). If your prompts produce long outputs, effective cost per task can be higher than the listed $2.50 per million output tokens implies, and output length should be controlled through system prompts or max-token limits

• Output cost relative to 2.0 Flash: At $2.50 per million output tokens versus $0.60 for Gemini 2.0 Flash (as of 06/01/2026), Gemini 2.5 Flash is over 4x more expensive on output. Applications running at scale need to confirm the accuracy uplift justifies that difference for each workload

• Provider dependency: Routing all traffic through Google's API means a quota restriction or regional outage directly affects every model in your stack that relies on Gemini. Diversifying across providers mitigates this risk

• Cost at scale: At high request volumes, $0.30 per million input tokens compounds quickly, particularly for chat-style applications that include long conversation histories in every request context

• No native function-calling parity across providers: Moving from Gemini 2.5 Flash to a non-Google fallback requires mapping tool-call schemas, which adds integration complexity if your routing policy needs to switch providers mid-stream

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

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

• Cost governance: Set hard or soft project budgets so Gemini 2.5 Flash spend stays within plan. Every request is attributed to a model, project, and tag in a unified billing dashboard across all providers, which is especially important given 2.5 Flash's tendency toward verbose output

• One API, every provider: Access Gemini 2.5 Flash 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

• 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 Flash?

Getting Gemini 2.5 Flash 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 Flash, the model string is google/gemini-2.5-flash. 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 Flash as primary with one fallback.

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