Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | GPT-4.1 nano | Gemini 2.5 Flash-Lite (Non-reasoning) |
|---|---|---|
| Input ($/M tokens) | $0.1 | $0.1 |
| Output ($/M tokens) | $0.4 | $0.4 |
Verdict. GPT-4.1 nano wins the overall benchmark matchup 2–0 across 2 overlapping categories, but raw benchmark score is only one input to the decision.
Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, Gemini 2.5 Flash-Lite (Non-reasoning) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Gemini 2.5 Flash-Lite (Non-reasoning) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. GPT-4.1 nano is strongest on Coding Index (11.1), Intelligence Index (9.6). Gemini 2.5 Flash-Lite (Non-reasoning) leads on Intelligence Index (6.7).
Speed. On throughput, Gemini 2.5 Flash-Lite (Non-reasoning) generates tokens at 276 tok/s versus 133 tok/s — about 52% faster. On time-to-first-token, Gemini 2.5 Flash-Lite (Non-reasoning) responds in 310ms vs 760ms, which matters most for chat-style UIs.
Provider. OpenAI and Google sell to overlapping but distinct developer audiences: OpenAI tends to ship frontier reasoning models with premium positioning, while Google 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): GPT-4.1 nano costs $9.00 ($108/year); Gemini 2.5 Flash-Lite (Non-reasoning) costs $9.00 ($108/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): GPT-4.1 nano ≈ $1.30/run, Gemini 2.5 Flash-Lite (Non-reasoning) ≈ $1.30/run. At agent/realtime scale (200M input / 100M output per million requests): GPT-4.1 nano ≈ $60/run, Gemini 2.5 Flash-Lite (Non-reasoning) ≈ $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 276 tok/s vs 133 tok/s. However, Gemini 2.5 Flash-Lite (Non-reasoning) has lower time-to-first-token (0.31s vs 0.76s).
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.