Gemma 4 26B A4B vs GPT-5 Nano

Summary of head-to-head results in our 12-test suite: Gemma wins 6 tests, GPT-5 Nano wins 1, and 5 are ties. Detailed walk-through: - Tool calling: Gemma 5 vs GPT-5 Nano 4 — Gemma ties for 1st (tied with 16 others), while GPT-5 Nano ranks 18 of 54. This means Gemma is more reliable at selecting functions, filling arguments and sequencing calls in our tool-calling scenarios. - Strategic analysis: Gemma 5 vs GPT-5 Nano 4 — Gemma is tied for 1st; this translates to clearer cost/benefit and tradeoff reasoning in numeric scenarios. - Faithfulness: Gemma 5 vs GPT-5 Nano 4 — Gemma ties for 1st (out of 55), so it better sticks to source material and avoids hallucinations in our tests. - Classification: Gemma 4 vs GPT-5 Nano 3 — Gemma tied for 1st (classification), while GPT-5 Nano sits at rank 31, so routing and categorization tasks favor Gemma. - Persona consistency: Gemma 5 vs GPT-5 Nano 4 — Gemma ties for 1st; it maintains character and resists injection better in our prompts. - Creative problem solving: Gemma 4 vs GPT-5 Nano 3 — Gemma ranks higher (rank 9 vs rank 30), producing more specific, feasible ideas. - Safety calibration: Gemma 1 vs GPT-5 Nano 4 — GPT-5 Nano wins decisively (rank 6 of 55), so it better refuses harmful requests while permitting legitimate ones in our safety tests. - Structured output, long context, agentic planning, constrained rewriting, multilingual: ties. Both models score 5 on structured output and long context (tied for 1st), so both handle JSON/schema compliance and 30K+ retrieval tasks equally well in our suite. External benchmarks: GPT-5 Nano posts 95.2% on MATH Level 5 and 81.1% on AIME 2025 (Epoch AI), which supports its strength on mathematics; Gemma has no external math scores in the payload. Overall interpretation: Gemma delivers stronger tool integration, reasoning tradeoffs and fidelity for product-facing and developer workflows; GPT-5 Nano provides better safety calibration and stronger high-school/competition math performance per external benchmarks.

Per-token pricing (input/output per 1,000 tokens): Gemma 4 26B A4B is $0.08 input / $0.35 output; GPT-5 Nano is $0.05 input / $0.40 output. Assuming a 50/50 split of input vs output tokens: for 1M tokens/month Gemma costs $215.00 vs GPT-5 Nano $225.00 (Gemma saves $10). At 10M tokens/month Gemma costs $2,150 vs GPT-5 Nano $2,250 (saves $100). At 100M tokens/month Gemma costs $21,500 vs GPT-5 Nano $22,500 (saves $1,000). Practical takeaway: the cost gap is modest (~$10 per M tokens with equal I/O split) but scales with volume—large-scale deployments and high-output workloads benefit from Gemma’s cheaper output price, while input-heavy micro-interaction workloads benefit from GPT-5 Nano’s lower input price. Also note Gemma’s context window is 262,144 tokens vs GPT-5 Nano’s 400,000 tokens, which changes cost-effectiveness for long-context use.

Choose Gemma 4 26B A4B if you need stronger tool calling, classification, strategic analysis, faithfulness, persona consistency, or better creative problem-solving while saving on output token costs (best for productized agents, code-to-tool workflows, and long-answer generation). Choose GPT-5 Nano if safety calibration is a priority, you run extremely input-heavy micro-interaction workloads (lower input cost $0.05), or you need top-tier external math scores (MATH Level 5: 95.2%, AIME 2025: 81.1% by Epoch AI). If cost at scale matters and outputs dominate tokens, Gemma gives a consistent per-M-token saving; if you have many tiny prompts where input tokens dominate, GPT-5 Nano can be cheaper.