R1 0528 vs Gemini 3.1 Flash Lite Preview

Across our 12-test suite, R1 0528 wins 4 benchmarks, Gemini 3.1 Flash Lite Preview wins 3, and they tie on 5.

Where R1 0528 wins:

• Tool calling (5 vs 4): R1 0528 ties for 1st among 54 models tested; Gemini 3.1 Flash Lite Preview ranks 18th. For agentic systems that chain function calls, this is a meaningful gap — accurate argument selection and sequencing matter at every step.
• Agentic planning (5 vs 4): R1 0528 ties for 1st among 54 models; Gemini 3.1 Flash Lite Preview ranks 16th. Goal decomposition and failure recovery are where reasoning models typically shine, and this confirms it.
• Long context (5 vs 4): R1 0528 ties for 1st among 55 models; Gemini 3.1 Flash Lite Preview ranks 38th of 55 — a notable drop. R1 0528's 163,840-token context window paired with a top retrieval score makes it the clear pick for large-document analysis. Ironically, Gemini 3.1 Flash Lite Preview has a far larger context window (1,048,576 tokens) but scores lower on our 30K+ retrieval test.
• Classification (4 vs 3): R1 0528 ties for 1st among 53 models; Gemini 3.1 Flash Lite Preview ranks 31st. One point gap in categorization accuracy matters in routing and triage applications.

Where Gemini 3.1 Flash Lite Preview wins:

• Structured output (5 vs 4): Gemini 3.1 Flash Lite Preview ties for 1st among 54 models; R1 0528 ranks 26th. JSON schema compliance is critical for any pipeline consuming model outputs programmatically. Importantly, R1 0528 has a documented quirk — it can return empty responses on structured output tasks because reasoning tokens consume the output budget. This is a real reliability risk.
• Strategic analysis (5 vs 4): Gemini 3.1 Flash Lite Preview ties for 1st among 54 models; R1 0528 ranks 27th. Nuanced tradeoff reasoning with real numbers favors the Flash Lite.
• Safety calibration (5 vs 4): Gemini 3.1 Flash Lite Preview ties for 1st among 55 models; R1 0528 ranks 6th. Both are above median (p50 = 2), but Gemini 3.1 Flash Lite Preview is tighter on refusing harmful requests while permitting legitimate ones.

Ties (5 benchmarks): Both models score identically on constrained rewriting (4/5), creative problem solving (4/5), faithfulness (5/5), persona consistency (5/5), and multilingual (5/5). No differentiation here.

External benchmarks (Epoch AI): R1 0528 has scores on third-party math benchmarks. It scores 96.6% on MATH Level 5 (rank 5 of 14 models with this data, sole holder of that score) and 66.4% on AIME 2025 (rank 16 of 23). The MATH Level 5 score sits above the median of 94.15% for models with this data — strong competition math performance. The AIME 2025 score of 66.4% falls below the median of 83.9%, placing R1 0528 in the lower half of reasoning models on that harder olympiad test. Gemini 3.1 Flash Lite Preview has no external benchmark scores in our data. No SWE-bench Verified data is available for either model.

R1 0528 costs $0.50/M input and $2.15/M output. Gemini 3.1 Flash Lite Preview costs $0.25/M input and $1.50/M output — half the input price and 30% cheaper on output. At 1M output tokens/month, that's a $6.50 difference — trivial. At 10M output tokens, you're paying $6,500 more for R1 0528. At 100M output tokens, the gap is $65,000/month. For high-volume production pipelines — content classification, document processing, chatbots — that cost gap demands justification. R1 0528 earns it for workflows that genuinely need superior tool calling or long-context retrieval. But if your primary tasks are structured output generation, strategic analysis, or anything multimodal, Gemini 3.1 Flash Lite Preview delivers equivalent or better benchmark scores at lower cost. Note: R1 0528 is a reasoning model that uses reasoning tokens, which consume output budget — actual costs on short tasks may run higher than the per-token rate suggests.

Choose R1 0528 if: your workload centers on agentic pipelines, tool-calling chains, or long-document retrieval — it scores 5/5 on all three in our testing and ranks at or near the top of our 54-model pool on each. It's also the pick for math-heavy tasks (96.6% on MATH Level 5 per Epoch AI). Be aware: it requires high max_completion_tokens settings, returns text only, and can produce empty responses on structured output tasks due to reasoning token budget consumption.

Choose Gemini 3.1 Flash Lite Preview if: you need reliable structured output (5/5, tied for 1st), multimodal inputs (images, audio, video, files), or a lower cost floor for high-volume deployment. It also wins on strategic analysis and safety calibration. At $0.25/M input and $1.50/M output, it's the smarter default for most production pipelines where agentic tool use isn't the primary demand. Its 1M-token context window is available if you need massive input capacity, though our retrieval benchmarks favor R1 0528 at the tested range.