Gemini 2.5 Pro vs GPT-5.6 Sol
Head-to-head specifications
| Metric | Gemini 2.5 Pro | GPT-5.6 Sol | Difference |
|---|---|---|---|
| Intelligence Index | 29.0 | 59.0 | -50.8% |
| Coding Index | 33.3 | 77.4 | -57.0% |
| Agentic Index | 7.1 | 54.0 | — |
| Context window | 1M tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.85 | $1.54 | -44.8% |
| Output speed (tokens/s) | 136 | 57 | +138.6% |
| Access | Proprietary API | Proprietary API | — |
- GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 29.0).
- Gemini 2.5 Pro is the cheaper model to run at $0.85/1M blended tokens — about 1.8× cheaper.
Verdict: Gemini 2.5 Pro or GPT-5.6 Sol?
Gemini 2.5 Pro advantages
- Affordability (+45%)
- Output speed (+58%)
GPT-5.6 Sol advantages
- General intelligence (+51%)
- Coding ability (+57%)
- Agentic task performance (+87%)
Which should you choose?
- Choose the Gemini 2.5 Pro if you want the lowest cost per token at scale.
- Choose the GPT-5.6 Sol if you need the strongest overall reasoning and accuracy.
- Choose the Gemini 2.5 Pro if low latency and fast generation matter for your application.
Value for money
GPT-5.6 Sol offers more intelligence per dollar (1.1× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Gemini 2.5 Pro vs GPT-5.6 Sol: which should you choose?
Gemini 2.5 Pro — Google multimodal model with an Intelligence Index of 29, a 1M-token context window and a blended price of $0.85/1M tokens.
GPT-5.6 Sol — OpenAI multimodal model with an Intelligence Index of 59, a 1M-token context window and a blended price of $1.54/1M tokens.
Gemini 2.5 Pro vs GPT-5.6 Sol: GPT-5.6 Sol scores higher on the Intelligence Index. GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 29.0). Gemini 2.5 Pro is the cheaper model to run at $0.85/1M blended tokens — about 1.8× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.6 Sol scores 59.0 versus 29.0. For software development, the Coding Index puts GPT-5.6 Sol ahead (77.4 vs 33.3). On agentic, multi-step tool-use tasks, GPT-5.6 Sol measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Gemini 2.5 Pro accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, Gemini 2.5 Pro generates faster (136 vs 57 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Gemini 2.5 Pro is the cheaper model to run ($0.85 vs $1.54 per 1M tokens). Gemini 2.5 Pro is proprietary api and GPT-5.6 Sol is proprietary api. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the Gemini 2.5 Pro better than the GPT-5.6 Sol?
GPT-5.6 Sol takes the overall edge, though Gemini 2.5 Pro wins in specific areas worth weighing. GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 29.0).
What is the main difference between the Gemini 2.5 Pro and the GPT-5.6 Sol?
GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 29.0). Gemini 2.5 Pro is the cheaper model to run at $0.85/1M blended tokens — about 1.8× cheaper.
Which is better value?
GPT-5.6 Sol offers more intelligence per dollar (1.1× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Which should I choose?
Choose the Gemini 2.5 Pro if you want the lowest cost per token at scale. Choose the GPT-5.6 Sol if you need the strongest overall reasoning and accuracy.
Methodology
Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.