Gemma 4 26B A4B vs GPT-5.4 Nano

Across our 12-test suite, Gemma 4 26B A4B wins 3 benchmarks, GPT-5.4 Nano wins 2, and the two tie on 7. Here is the test-by-test breakdown:

Tool Calling (Gemma wins: 5 vs 4): Gemma scores 5, tied for 1st with 16 other models out of 54 tested. GPT-5.4 Nano scores 4, ranked 18th of 54. Tool calling covers function selection, argument accuracy, and sequencing — the backbone of agentic workflows. This gap is meaningful for developers building multi-step agents or API orchestration pipelines.

Faithfulness (Gemma wins: 5 vs 4): Gemma scores 5, tied for 1st with 32 others out of 55 tested. GPT-5.4 Nano scores 4, ranked 34th of 55. Faithfulness measures how well a model sticks to source material without hallucinating. For RAG applications, summarization, or document Q&A, Gemma's advantage here is directly actionable.

Classification (Gemma wins: 4 vs 3): Gemma scores 4, tied for 1st with 29 other models out of 53 tested. GPT-5.4 Nano scores 3, ranked 31st of 53. This covers accurate categorization and routing — relevant to content moderation pipelines, intent detection, and triage systems.

Safety Calibration (GPT-5.4 Nano wins: 3 vs 1): GPT-5.4 Nano scores 3, ranked 10th of 55 (shared with only one other model). Gemma scores 1, ranked 32nd of 55. This is the most significant gap in the comparison. Safety calibration measures whether a model appropriately refuses harmful requests while permitting legitimate ones. Gemma's score of 1 puts it at the bottom quartile of our tested models (p25 = 1). For consumer-facing products or regulated industries, this is a disqualifying gap.

Constrained Rewriting (GPT-5.4 Nano wins: 4 vs 3): GPT-5.4 Nano scores 4, ranked 6th of 53. Gemma scores 3, ranked 31st of 53. Constrained rewriting tests compression within hard character limits — relevant for headline generation, ad copy, and notification text.

Ties (7 benchmarks): Both models score identically on structured output (5/5, tied for 1st), strategic analysis (5/5, tied for 1st), creative problem solving (4/4, rank 9), long context (5/5, tied for 1st), persona consistency (5/5, tied for 1st), agentic planning (4/4, rank 16), and multilingual (5/5, tied for 1st). These ties cover a wide swath of common tasks — both models are genuinely equivalent on most everyday use cases.

External Benchmark — AIME 2025 (Epoch AI): GPT-5.4 Nano scores 87.8% on AIME 2025 (rank 8 of 23 models in Epoch AI's dataset), placing it among the stronger math-capable models by that external measure. No AIME 2025 score is available for Gemma 4 26B A4B in our payload, so a direct comparison cannot be made. Developers with math-heavy workloads should treat GPT-5.4 Nano's score as a meaningful signal, while noting we cannot confirm Gemma's math performance on this benchmark.

Gemma 4 26B A4B costs $0.08/MTok input and $0.35/MTok output. GPT-5.4 Nano costs $0.20/MTok input and $1.25/MTok output — 2.5× more on input and 3.6× more on output. At real-world volumes, the gap is immediate and compounding. At 1M output tokens/month: Gemma costs $0.35, GPT-5.4 Nano costs $1.25 — a $0.90 difference, negligible in isolation. At 10M output tokens/month: $3.50 vs $12.50 — a $9 monthly gap that starts to matter for bootstrapped teams. At 100M output tokens/month: $350 vs $1,250 — a $900/month difference that is a real budget line item. Developers running high-volume pipelines — document processing, classification, RAG retrieval — should weight this heavily in favor of Gemma. The cost argument flips only if GPT-5.4 Nano's safety calibration advantage is a hard compliance requirement, or if the AIME 2025 math performance (87.8%, rank 8 of 23 on Epoch AI's benchmark) reflects a genuine capability gap for math-heavy applications.

Choose Gemma 4 26B A4B if: you are building API-heavy or high-volume pipelines where output cost matters — at $0.35/MTok output versus $1.25, it pays for itself quickly. It is the better choice for agentic tools (tool calling score: 5 vs 4), RAG and document workflows (faithfulness: 5 vs 4), classification and routing tasks (4 vs 3), and any multimodal workflow involving video inputs (supported modality: text+image+video). Its 262K context window and identical max output token limit make it a strong fit for long-document tasks. Choose GPT-5.4 Nano if: your deployment is consumer-facing, regulated, or involves sensitive content moderation — its safety calibration score of 3 vs Gemma's 1 is a genuine differentiator here, and the additional cost may be justified by reduced moderation overhead. It also wins on constrained rewriting (4 vs 3), making it the better pick for tight-format copy generation. If math reasoning is a core requirement, GPT-5.4 Nano's 87.8% on AIME 2025 (Epoch AI) is supporting evidence, though Gemma's math performance on that benchmark is unknown from our data. GPT-5.4 Nano's 400K context window also exceeds Gemma's 262K if extreme-length contexts are needed, though its max output is capped at 128K vs Gemma's 262K.