What other models does Google offer?

Google's Gemini lineup spans multiple price and capability tiers, with Gemini 2.5 Flash Lite sitting at the fastest and most affordable end of the current family. Here are some other models Google supports:

• Gemini 2.5 Flash: Gemini 2.5 Flash is the stable mid-tier model in the 2.5 generation, priced at $0.30 per million input tokens and $2.50 per million output tokens, producing 203.2 tokens per second and ranking above average in intelligence among evaluated models, making it the natural step up from Gemini 2.5 Flash Lite for tasks where accuracy matters more than cost

• Gemini 2.5 Pro: Gemini 2.5 Pro is Google's stable flagship reasoning model, priced at $1.25 per million input tokens and $10.00 per million output tokens, supporting extended thinking over a 1M token context window and suited for complex analytical and multi-step tasks where accuracy is the priority

• Gemini 3 Flash: Gemini 3 Flash is a preview-stage model from the newer Gemini 3 generation, priced at $0.50 per million input tokens and $3.00 per million output tokens (as of 6/2/2026), ranked #20 out of 71 non-reasoning models in intelligence, and positioned for teams that want Gemini 3-generation quality at a cost point between Gemini 2.5 Flash Lite and Gemini 3 Pro

• Gemini 3 Pro: Gemini 3 Pro is Google's premium reasoning model in the Gemini 3 series, priced at $2.00 per million input tokens and $12.00 per million output tokens (as of 6/2/2026), ranked #30 out of 150 models on the Artificial Analysis Intelligence Index, and intended for the highest-complexity workloads where cost is secondary to reasoning quality

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

Gemini 2.5 Flash Lite is the speed and cost floor of Google's current public lineup, built for applications where throughput and price per token matter more than benchmark accuracy.

• Pricing: Input costs $0.10 per million tokens and output costs $0.40 per million tokens. That is one-third the input cost of Gemini 2.5 Flash at $0.30 and one-sixth the output cost, making it by far the least expensive option in the Gemini 2.5 family

• Speed: Output speed of 259.8 tokens per second ranks #1 across all models evaluated on Artificial Analysis (as of 6/2/2026), faster than Gemini 2.5 Flash at 203.2 tokens per second and Gemini 2.5 Pro at 129.7 tokens per second. Time to first token is 0.41 seconds (as of 6/2/2026), among the lowest latencies available

• Context window: Supports 1M tokens of input context, matching Gemini 2.5 Flash and Gemini 2.5 Pro

• Intelligence Index: Ranked #62 out of 85 models on the Artificial Analysis Intelligence Index (as of 6/2/2026), placing it below average in intelligence relative to the full set of evaluated models. It trades accuracy for speed and price

• Capabilities: Accepts text, image, speech, and video inputs. It is not a reasoning model and does not generate chain-of-thought, consistent with its positioning as a fast, cost-optimized option

Gemini 2.5 Flash Lite is the best fit for high-volume pipelines where per-token cost and response latency dominate requirements and the task does not need above-average reasoning quality.

What models should I consider using alongside Gemini 2.5 Flash Lite?

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

• Gemini 2.5 Flash: For requests within your product that require higher accuracy than Gemini 2.5 Flash Lite can deliver, such as nuanced summarization or complex instruction-following, route those to Gemini 2.5 Flash at $0.30 per million input tokens while keeping Gemini 2.5 Flash Lite as the default for simpler, high-volume tasks

• Gemini 2.5 Pro: For tasks that require extended reasoning or the highest available accuracy in the Gemini 2.5 family, Gemini 2.5 Pro handles the cases where Gemini 2.5 Flash Lite's intelligence ranking would produce unacceptable output quality

• GPT-4o mini (OpenAI): For workloads where OpenAI's function-calling format is already integrated into your stack, GPT-4o mini provides a cost-efficient cross-provider fallback that covers many of the same general-purpose instruction-following tasks as Gemini 2.5 Flash Lite

• Claude Haiku 3.5 (Anthropic): For structured output tasks requiring strict JSON adherence at low cost, Claude Haiku 3.5 offers a cross-provider alternative in a comparable budget tier with strong formatting reliability

• Llama 3.3 70B (Meta): For teams with data residency requirements that preclude cloud APIs, Llama 3.3 70B is an open-weight model that can be self-hosted and covers instruction-following tasks at a comparable capability level

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

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

• Below-average intelligence for complex tasks: Gemini 2.5 Flash Lite ranks #62 out of 85 evaluated models on the Artificial Analysis Intelligence Index (as of 6/2/2026). Tasks requiring nuanced reasoning, multi-step problem solving, or high factual precision should be routed to a higher-tier model rather than handled by Gemini 2.5 Flash Lite

• High verbosity increasing effective output cost: The model is described as "very verbose" on Artificial Analysis, generating 36M output tokens during the Intelligence Index evaluation (as of 6/2/2026). At $0.40 per million output tokens, verbose completions can make effective cost per task meaningfully higher than the headline pricing implies. Output length controls via system prompts or max-token limits are important

• Provider dependency: Routing all traffic through Google's API means any quota restriction or service disruption affects every workload relying on Gemini 2.5 Flash Lite. Given its use in high-volume pipelines, an outage at this tier can have outsized throughput impact

• Cost at scale: Even at $0.10 per million input tokens, very high request volumes accumulate spend quickly. Applications processing millions of requests per day should model total token costs carefully, particularly accounting for verbose output behavior

• No reasoning capability: Gemini 2.5 Flash Lite does not generate extended chain-of-thought, which means it will underperform on tasks that benefit from step-by-step problem decomposition. Teams that discover this gap mid-production face a routing or model swap to address it

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

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

• Build Your Own Router: Define what "best" means for your traffic by selecting from curated ML benchmarks or adding your own eval scores. For Gemini 2.5 Flash Lite workloads, this means you can set speed and cost as the primary weights and automatically route more complex requests to a higher-tier model without rewriting application logic

• One API, every provider: Access Gemini 2.5 Flash Lite and every other major LLM through a single endpoint and API key. Change providers by swapping the model string, with 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 Flash Lite spend stays within plan. Every request is attributed to a model, project, and tag in a unified billing dashboard across all providers

• 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 Lite?

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

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