Gemma 4 26B A4B vs GPT-4.1 Mini

Across our 12-test suite, Gemma 4 26B A4B outscores GPT-4.1 Mini on 6 benchmarks, loses on 2, and ties on 4. Here's the test-by-test breakdown:

Tool Calling (5 vs 4): Gemma scores 5/5, tied for 1st with 16 other models out of 54 tested. GPT-4.1 Mini scores 4/5, ranked 18th of 54. For agentic workflows that depend on function selection accuracy and argument sequencing, this is a meaningful gap.

Structured Output (5 vs 4): Gemma scores 5/5, tied for 1st with 24 others out of 54 tested. GPT-4.1 Mini scores 4/5, ranked 26th of 54. JSON schema compliance and format adherence are critical for any pipeline consuming model output programmatically — Gemma has the edge here.

Faithfulness (5 vs 4): Gemma scores 5/5, tied for 1st with 32 others out of 55 tested. GPT-4.1 Mini scores 4/5, ranked 34th of 55. When the task is summarization or RAG — where sticking to source material matters — Gemma is more reliable in our testing.

Classification (4 vs 3): Gemma scores 4/5, tied for 1st with 29 others out of 53 tested. GPT-4.1 Mini scores 3/5, ranked 31st of 53. For routing, tagging, or categorization tasks, Gemma's advantage is clear.

Strategic Analysis (5 vs 4): Gemma scores 5/5, tied for 1st with 25 others out of 54 tested. GPT-4.1 Mini scores 4/5, ranked 27th of 54. Nuanced tradeoff reasoning with real-world numbers favors Gemma.

Creative Problem Solving (4 vs 3): Gemma scores 4/5, ranked 9th of 54. GPT-4.1 Mini scores 3/5, ranked 30th of 54. Generating non-obvious, feasible ideas is a consistent Gemma strength in our tests.

Constrained Rewriting (3 vs 4): GPT-4.1 Mini wins here, scoring 4/5, ranked 6th of 53. Gemma scores 3/5, ranked 31st of 53. Compression tasks with hard character limits are a notable weakness for Gemma.

Safety Calibration (1 vs 2): GPT-4.1 Mini scores 2/5, ranked 12th of 55. Gemma scores 1/5, ranked 32nd of 55. This is Gemma's clearest weakness — it ranks in the bottom half of all tested models on refusing harmful requests while permitting legitimate ones. Both models score below the median (p50 = 2), but GPT-4.1 Mini is meaningfully better here.

Ties (both score equally): Long context (5/5 each, both tied for 1st of 55), multilingual (5/5 each, both tied for 1st of 55), persona consistency (5/5 each, both tied for 1st of 53), and agentic planning (4/4 each, both ranked 16th of 54).

External benchmarks (Epoch AI): GPT-4.1 Mini has external benchmark data: 87.3% on MATH Level 5 (ranked 9th of 14 models with this data) and 44.7% on AIME 2025 (ranked 18th of 23). These place it below the median of tested models on both competition math benchmarks (p50 for MATH Level 5 is 94.15%; p50 for AIME 2025 is 83.9%). Gemma 4 26B A4B has no external benchmark data in our payload, so direct comparison on these dimensions isn't possible.

Gemma 4 26B A4B costs $0.08/MTok input and $0.35/MTok output. GPT-4.1 Mini costs $0.40/MTok input and $1.60/MTok output — 5× more on input and 4.57× more on output. In practice, that gap compounds fast. At 1M output tokens/month, Gemma costs $0.35 vs GPT-4.1 Mini's $1.60 — a $1.25 monthly difference that's trivial. At 10M output tokens, that's $3.50 vs $16.00 — a $12.50 gap worth noticing. At 100M output tokens — a realistic scale for production APIs, high-volume summarization, or real-time chat — Gemma costs $35 vs $160, saving $125/month. For developers running batch pipelines, document processing, or any high-throughput workload, Gemma 4 26B A4B's pricing advantage is a concrete cost driver. Consumers using either model through a flat-rate subscription are less affected by per-token rates, but the underlying cost efficiency may translate to availability or rate limit differences at the provider level.

Choose Gemma 4 26B A4B if your workload centers on tool calling, structured output, RAG/summarization pipelines, classification and routing, or strategic analysis — it outscores GPT-4.1 Mini on all of these in our testing, and does so at 78% lower output cost. Its 262K context window is large enough for most document-processing tasks, and its MoE architecture (only 3.8B parameters activate per token) means inference is efficient. It's the stronger general-purpose API choice for most developers. Choose GPT-4.1 Mini if you need a context window beyond 262K (its 1M-token window is nearly 4× larger), if safety calibration is a hard requirement for your deployment (it scored 2/5 vs Gemma's 1/5 in our tests), or if constrained rewriting at strict character limits is a core task. GPT-4.1 Mini also has third-party math benchmark data (87.3% on MATH Level 5 per Epoch AI), which may matter if quantitative reasoning is a priority and you want external validation beyond our internal suite.