Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Gemini 2.5 Flash-Lite (Non-reasoning) | GPT-4.1 nano |
|---|---|---|
| Input ($/M tokens) | $0.1 | $0.1 |
| Output ($/M tokens) | $0.4 | $0.4 |
Verdict. GPT-4.1 nano takes the aggregate benchmark matchup 2–0 across 2 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.
Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, GPT-4.1 nano is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). GPT-4.1 nano makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Gemini 2.5 Flash-Lite (Non-reasoning) is strongest on Intelligence Index (6.7). GPT-4.1 nano leads on Coding Index (11.1), Intelligence Index (7.8).
Speed. On throughput, Gemini 2.5 Flash-Lite (Non-reasoning) generates tokens at 298 tok/s versus 187 tok/s — about 37% faster. On time-to-first-token, Gemini 2.5 Flash-Lite (Non-reasoning) responds in 280ms vs 720ms, which matters most for chat-style UIs.
Provider. Google and OpenAI sell to overlapping but distinct developer audiences: Google tends to ship frontier reasoning models with premium positioning, while OpenAI often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.
Workload cost. Workload scenarios (per million requests at 30M input + 15M output tokens): Gemini 2.5 Flash-Lite (Non-reasoning) costs $9.00 ($108/year); GPT-4.1 nano costs $9.00 ($108/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Gemini 2.5 Flash-Lite (Non-reasoning) ≈ $1.30/run, GPT-4.1 nano ≈ $1.30/run. At agent/realtime scale (200M input / 100M output per million requests): Gemini 2.5 Flash-Lite (Non-reasoning) ≈ $60/run, GPT-4.1 nano ≈ $60/run.
Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.
Head-to-head deltas
Data from Artificial Analysis API — 12 benchmarks
Both models have similar pricing. Check the detailed breakdown above for input vs output token costs.
GPT-4.1 nano wins 2 out of 12 benchmarks compared to 0 for Gemini 2.5 Flash-Lite (Non-reasoning). See the detailed benchmark chart above for per-category results.
Gemini 2.5 Flash-Lite (Non-reasoning) generates tokens faster at 298 tok/s vs 187 tok/s. Gemini 2.5 Flash-Lite (Non-reasoning) also has lower time-to-first-token (0.28s vs 0.72s).
Choose based on your priorities: both are similarly priced, GPT-4.1 nano for stronger benchmark performance, and Gemini 2.5 Flash-Lite (Non-reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.