Gemma 4 31B vs GPT-4.1 Mini

Across our 12-test benchmark suite (scored 1–5), Gemma 4 31B outperforms GPT-4.1 Mini on 7 tests, ties on 4, and loses on 1.

Where Gemma 4 31B wins:

• Tool calling (5 vs 4): Gemma 4 31B scores 5/5, tied for 1st with 16 other models out of 54 tested. GPT-4.1 Mini scores 4/5, ranking 18th. For agentic systems relying on function selection and argument accuracy, this is a meaningful edge.
• Agentic planning (5 vs 4): Gemma 4 31B tied for 1st with 14 other models out of 54. GPT-4.1 Mini ranks 16th. Combined with the tool calling advantage, Gemma 4 31B is notably better suited for multi-step autonomous workflows.
• Structured output (5 vs 4): Gemma 4 31B tied for 1st with 24 other models out of 54. GPT-4.1 Mini ranks 26th. JSON schema compliance matters for any API integration or data pipeline.
• Strategic analysis (5 vs 4): Gemma 4 31B tied for 1st with 25 other models out of 54. GPT-4.1 Mini ranks 27th. This covers nuanced tradeoff reasoning — relevant for decision-support and research tasks.
• Faithfulness (5 vs 4): Gemma 4 31B tied for 1st with 32 other models out of 55. GPT-4.1 Mini ranks 34th. Sticking to source material without hallucinating is critical in RAG and summarization contexts.
• Classification (4 vs 3): Gemma 4 31B tied for 1st with 29 other models out of 53. GPT-4.1 Mini ranks 31st — below the field median of 4.
• Creative problem solving (4 vs 3): Gemma 4 31B ranks 9th of 54; GPT-4.1 Mini ranks 30th. Both are above and at the p25 floor respectively, but Gemma 4 31B is significantly more competitive here.

Where GPT-4.1 Mini wins:

• Long context (5 vs 4): GPT-4.1 Mini scores 5/5 (tied for 1st with 36 models out of 55), vs Gemma 4 31B's 4/5 (rank 38 of 55). More importantly, GPT-4.1 Mini's context window is 1,047,576 tokens vs Gemma 4 31B's 262,144. If your use case involves processing very long documents or multi-session memory, GPT-4.1 Mini has a structural advantage beyond the benchmark score.

Ties (both models equal):

• Constrained rewriting (4/4), safety calibration (2/2), persona consistency (5/5), and multilingual (5/5) — no meaningful difference on these dimensions.

External benchmarks (GPT-4.1 Mini only): The payload includes Epoch AI third-party scores for GPT-4.1 Mini: 87.3% on MATH Level 5 (rank 9 of 14 models with this score) and 44.7% on AIME 2025 (rank 18 of 23). For context, the median MATH Level 5 score across models in our dataset is 94.15%, and the AIME 2025 median is 83.9% — placing GPT-4.1 Mini below median on both external math benchmarks. No equivalent external benchmark scores are available for Gemma 4 31B in this dataset.

Gemma 4 31B is priced at $0.13/MTok input and $0.38/MTok output. GPT-4.1 Mini runs $0.40/MTok input and $1.60/MTok output. That's a 3x input gap and a 4.2x output gap. At real-world volumes: at 1M output tokens/month, Gemma 4 31B costs $0.38 vs GPT-4.1 Mini's $1.60 — a $1.22 difference. At 10M tokens, that's $3.80 vs $16.00, saving $12.20. At 100M tokens, Gemma 4 31B costs $380 vs $1,600 for GPT-4.1 Mini — $1,220 in savings per month on output alone. For high-volume production pipelines — content generation, classification at scale, or agentic workflows making frequent tool calls — that cost gap compounds fast. GPT-4.1 Mini's pricing premium is only justified if you specifically need its 1M+ token context window or are already locked into the OpenAI ecosystem.

Choose Gemma 4 31B if: you're building agentic systems, tool-calling pipelines, or structured-output workflows and want the best benchmark performance at the lowest cost. At $0.38/MTok output, it's the clear value pick for classification tasks at scale, RAG applications requiring faithfulness, or any workload where strategic reasoning quality matters. Its multimodal input (text + image + video) also expands what you can build without switching models.

Choose GPT-4.1 Mini if: your use case genuinely requires processing documents or conversations exceeding 256K tokens — the 1M+ token context window is GPT-4.1 Mini's strongest differentiator and there's no equivalent in Gemma 4 31B. Also consider it if you're already deeply integrated with the OpenAI SDK and switching costs outweigh the $1.22/MTok output savings, or if math-heavy tasks are central to your application and you want the external MATH Level 5 and AIME 2025 data points for comparison.