Gemini 2.5 Pro
Gemini 2.5 Pro
Google DeepMind's current top model. Leads on long-context tasks and is competitive at the top of coding benchmarks. The standout feature is the context window — 1M tokens, usable in practice, not just on paper.
Key Differentiators
Long context: 1,048,576 token (1M) context window with retrieval that holds up across the range. Genuinely useful for tasks that require ingesting entire codebases, long research documents, or video. Closest competitor is well behind on effective context length.
Coding benchmarks: Led LiveCodeBench at launch (May 2025). Strong on competitive programming tasks.
Free tier: Available via Google AI Studio at no cost for experimental use. Rate-limited, but usable for prototyping and evaluation.
Benchmarks
- Led LiveCodeBench at launch (May 2025)
- Top-tier long-context retrieval
Pricing
| Tier | Input | Output |
|---|---|---|
| ≤200K tokens | $1.25/MTok | $10/MTok |
| >200K tokens | $2.50/MTok | $15/MTok |
Paid API via Google Cloud Vertex AI. Free via AI Studio with rate limits.
Strengths
- Best-in-class long-context handling — ideal for large codebase review, document analysis
- Strong on coding tasks across multiple evals
- Free access via AI Studio makes it easy to test
- Multimodal — handles images, video, and audio natively
Weaknesses
- API latency can be higher than OpenAI/Anthropic equivalents at scale
- Tool use / function calling less reliable than Claude or GPT at complex nested calls
- computer-use not a first-class capability
- Google Cloud ecosystem lock-in for production use
Use Cases
Best for: reading and reasoning over entire codebases (can fit more in one call than any competitor), long document analysis, multimodal tasks, competitive coding problems.
Not the default choice for: agentic-workflows with complex tool chains (Claude handles this better), computer-use tasks, or situations where API reliability SLA matters.
Related
evals · rag · agentic-workflows · gemma-4