Gemma 4 31B vs GPT-4.1 Nano

Across our 12-test benchmark suite (scored 1–5), Gemma 4 31B wins 7 tests outright, ties 5, and loses none. GPT-4.1 Nano wins zero.

Strategic Analysis (5 vs 2): This is the starkest gap. Gemma 4 31B ties for 1st among 54 models; GPT-4.1 Nano ranks 44th of 54. For any task involving nuanced tradeoff reasoning with real numbers — business decisions, resource allocation, scenario planning — GPT-4.1 Nano's score of 2 is a genuine liability.

Creative Problem Solving (4 vs 2): Gemma 4 31B ranks 9th of 54 (tied with 20 others); GPT-4.1 Nano ranks 47th of 54. A 2/5 on creative problem solving puts GPT-4.1 Nano in the bottom tier of our tested models for generating non-obvious, feasible ideas.

Tool Calling (5 vs 4): Gemma 4 31B ties for 1st among 54 models; GPT-4.1 Nano ranks 18th. For agentic workflows where function selection and argument accuracy are critical, Gemma 4 31B's top-tier score is a practical advantage.

Agentic Planning (5 vs 4): Gemma 4 31B ties for 1st among 54 models; GPT-4.1 Nano ranks 16th. Combined with the tool-calling edge, Gemma 4 31B is the stronger foundation for multi-step AI agents.

Multilingual (5 vs 4): Gemma 4 31B ties for 1st among 55 models; GPT-4.1 Nano ranks 36th. If you serve non-English users, this gap is operationally meaningful.

Classification (4 vs 3): Gemma 4 31B ties for 1st among 53 models; GPT-4.1 Nano ranks 31st. For routing, tagging, and categorization pipelines, Gemma 4 31B's advantage is real.

Persona Consistency (5 vs 4): Gemma 4 31B ties for 1st among 53 models; GPT-4.1 Nano ranks 38th. Character stability and injection resistance favor Gemma 4 31B for chatbot and role-based deployments.

Ties (5 benchmarks): Both models score identically on structured output (5/5), constrained rewriting (4/4), faithfulness (5/5), long context (4/4), and safety calibration (2/2). The long context tie is notable — both score 4/5 despite GPT-4.1 Nano's much larger 1M-token window vs Gemma 4 31B's 256K, suggesting the quality of retrieval at depth is comparable for the range we tested.

External Benchmarks (GPT-4.1 Nano only): The payload includes third-party scores for GPT-4.1 Nano on Epoch AI benchmarks. On MATH Level 5, it scores 70% — ranking 11th of 14 models with that data, below the median of 94.15% among scored models. On AIME 2025, it scores 28.9% — ranking 20th of 23 models with scores, well below the median of 83.9%. These results (Epoch AI) reinforce that GPT-4.1 Nano is not positioned for demanding math tasks. No equivalent external benchmark data is present for Gemma 4 31B in this payload.

The pricing gap between these two models is nearly negligible in practice. Gemma 4 31B costs $0.13/M input tokens and $0.38/M output tokens; GPT-4.1 Nano costs $0.10/M input and $0.40/M output — a price ratio of 0.95, meaning they're essentially at parity. At 1M output tokens/month, the difference is $0.02 in GPT-4.1 Nano's favor. At 10M output tokens, that grows to $0.20. At 100M output tokens, you'd save $20 with GPT-4.1 Nano. No serious workload should make a model decision based on this spread. Cost is not a differentiator here — capability is. The one pricing-adjacent consideration: if your application requires GPT-4.1 Nano's 1M-token context window to avoid chunking long documents, that architectural saving may offset even a larger price gap, but on raw token cost alone, both models are in the same tier.

Choose Gemma 4 31B if you're building agentic systems, multi-step pipelines, or anything requiring strategic reasoning or creative problem solving — it outscores GPT-4.1 Nano decisively on all of those dimensions at effectively the same price. It's also the better choice for multilingual applications, classification/routing systems, chatbots that need strong persona consistency, and any workflow where tool-calling reliability matters. Choose GPT-4.1 Nano if your application specifically requires a context window beyond 256K tokens — its 1M-token window is a genuine architectural advantage for processing very large documents in a single pass. Also consider GPT-4.1 Nano if you're already embedded in the OpenAI ecosystem and the integration cost of switching outweighs the capability gap, or if low-latency response time is your primary constraint (per its design description as the fastest, cheapest GPT-4.1 series model). For most use cases, however, Gemma 4 31B's benchmark profile is substantially stronger.