Current as of March 2026. Gemini 2.5 Flash is the Flash-class version of the 2.5 Pro model — faster and cheaper, with the same 1M context window. Compared to GPT-4o-mini, you’re paying similar input costs but getting 8x the context headroom.
Specs
| Provider | |
| Input cost | $0.30 / M tokens |
| Output cost | $2.50 / M tokens |
| Context window | 1.0M tokens |
| Max output | 8K tokens |
| Parameters | N/A |
| Features | function_calling, vision |
What it’s good at
1M context at a reasonable price
$0.30/M input for a 1M context window is a solid deal. For document analysis, large archive summarization, or any task where you’d otherwise be building retrieval infrastructure, this model removes that complexity entirely.
Multimodal throughput
Native vision support is fast and cheap enough for high-frequency image classification or video frame description at scale. Good fit for OpenClaw agents that need to process visual inputs regularly.
Where it falls short
Reasoning depth
This is where you feel the difference from the Pro variant. Multi-step logical puzzles and complex architectural decisions — the model will try but you’ll see more errors than you’d get from a Pro-tier model.
Instruction following in long context
It occasionally drops negative constraints from system prompts when the context gets long. You’ll likely need more explicit prompt engineering to keep it on track compared to GPT-4o-mini.
Best use cases with OpenClaw
- Large document analysis — Process a 1M token archive in one shot. No retrieval pipeline, no chunking errors, just the whole thing in context.
- Multimodal tagging at scale — Fast, cheap, and capable enough for image classification and frame description in high-frequency loops.
Not ideal for
- Complex logic — Hallucination rate on deep reasoning tasks is higher than flagship models. Don’t use it for anything where a wrong answer has real consequences.
- Strict JSON schemas — Nested structured output can be unreliable. If your agent depends on well-formed JSON, GPT-4o-mini is more consistent.
Run it through Haimaker
Skip juggling API keys. One Haimaker key gives you access to every model on the platform. Tell OpenClaw:
Add Haimaker as a custom provider to my OpenClaw config. Use these details:
- Provider name: haimaker
- Base URL: https://api.haimaker.ai/v1
- API key: [PASTE YOUR HAIMAKER API KEY HERE]
- API type: openai-completions
Add the auto-router model:
- haimaker/auto (reasoning: false, context: 128000, max tokens: 32000)
Create an alias "auto" for easy switching. Apply the config when done.
Or skip model selection entirely — Haimaker’s auto-router picks the best model for each task so you don’t have to.
OpenClaw setup
Configure the custom Gemini provider in OpenClaw with your API key. Set maxTokens to 8192 explicitly — the default can cause truncated responses on longer outputs.
{
"models": {
"mode": "merge",
"providers": {
"google": {
"baseUrl": "https://generativelanguage.googleapis.com/v1beta",
"apiKey": "YOUR-GOOGLE-API-KEY",
"api": "openai-completions",
"models": [
{
"id": "gemini-2.5-flash",
"name": "Gemini 2.5 Flash",
"cost": {
"input": 0.3,
"output": 2.5
},
"contextWindow": 1048576,
"maxTokens": 8192
}
]
}
}
}
}
How it compares
- vs GPT-4o-mini — GPT-4o-mini is more reliable on structured output and stricter instruction-following. Gemini 2.5 Flash gives you 8x the context window at comparable input cost.
- vs Claude 3.5 Haiku — Haiku is better at coding and following nuanced instructions. Gemini 2.5 Flash is cheaper and handles native multimodal inputs without extra setup.
Bottom line
Use it when context size is the bottleneck and you don’t need frontier-level reasoning. It’s a strong fit for large-scale data ingestion and multimodal processing where volume matters more than precision.
TRY GEMINI 2.5 FLASH ON HAIMAKER
For setup instructions, see our API key guide. For all available models, see the complete models guide.