Gemma 4 31B vs GPT-5.4 Nano
Gemma 4 31B is the stronger choice for most API and agentic use cases: it outperforms GPT-5.4 Nano on tool calling, faithfulness, classification, and agentic planning in our testing, while costing 70% less on output tokens ($0.38 vs $1.25 per million). GPT-5.4 Nano edges ahead on long-context retrieval (5 vs 4) and safety calibration (3 vs 2), and its 400K context window is meaningfully larger than Gemma 4 31B's 256K. If your workload is primarily long-document processing or you need tighter safety refusals, GPT-5.4 Nano justifies its premium — otherwise, Gemma 4 31B delivers more benchmark wins at a fraction of the cost.
Gemma 4 31B
Benchmark Scores
External Benchmarks
Pricing
Input
$0.130/MTok
Output
$0.380/MTok
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GPT-5.4 Nano
Benchmark Scores
External Benchmarks
Pricing
Input
$0.200/MTok
Output
$1.25/MTok
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Benchmark Analysis
Across our 12 internal benchmark tests, Gemma 4 31B wins 4, GPT-5.4 Nano wins 2, and 6 are tied.
Where Gemma 4 31B wins:
- Tool calling (5 vs 4): Gemma 4 31B tied for 1st among 54 models in our testing; GPT-5.4 Nano ranks 18th. For agentic pipelines and function-calling workflows, this is a meaningful gap — tool calling covers function selection, argument accuracy, and sequencing.
- Faithfulness (5 vs 4): Gemma 4 31B tied for 1st among 55 models; GPT-5.4 Nano ranks 34th. In RAG pipelines and summarization tasks where sticking to source material matters, this difference is operationally significant.
- Classification (4 vs 3): Gemma 4 31B tied for 1st among 53 models; GPT-5.4 Nano ranks 31st. For routing, tagging, or content categorization, Gemma 4 31B is noticeably more reliable.
- Agentic planning (5 vs 4): Gemma 4 31B tied for 1st among 54 models; GPT-5.4 Nano ranks 16th. Goal decomposition and failure recovery — critical for multi-step autonomous tasks — favor Gemma 4 31B clearly.
Where GPT-5.4 Nano wins:
- Long context (5 vs 4): GPT-5.4 Nano tied for 1st among 55 models; Gemma 4 31B ranks 38th. This test covers retrieval accuracy at 30K+ tokens, and GPT-5.4 Nano's 400K context window (vs 256K) reinforces this advantage.
- Safety calibration (3 vs 2): GPT-5.4 Nano ranks 10th of 55 models; Gemma 4 31B ranks 12th (though both are below the 75th percentile of 2 — note the field median is 2, so GPT-5.4 Nano's score of 3 is above median). For applications requiring accurate refusals without over-blocking legitimate requests, GPT-5.4 Nano performs better.
Tied (both models score equally):
- Structured output (5/5), strategic analysis (5/5), constrained rewriting (4/4), creative problem solving (4/4), persona consistency (5/5), and multilingual (5/5) are all tied. Both models hit the top tier on structured output, strategic analysis, persona consistency, and multilingual — no advantage to claim on these.
External benchmark (Epoch AI): GPT-5.4 Nano scores 87.8% on AIME 2025, ranking 8th of 23 models with a score available in our dataset. Gemma 4 31B has no AIME 2025 score in our data, so no direct comparison is possible — but GPT-5.4 Nano's 87.8% sits above the dataset median of 83.9%, marking it as a strong math reasoning model by that external measure.
Pricing Analysis
Gemma 4 31B costs $0.13/Mtok input and $0.38/Mtok output. GPT-5.4 Nano costs $0.20/Mtok input and $1.25/Mtok output. The input cost difference is modest, but output costs tell the real story. At 1M output tokens/month, GPT-5.4 Nano costs $1.25 vs Gemma 4 31B's $0.38 — a $0.87 gap. Scale to 10M output tokens and you're paying $12.50 vs $3.80: a $8.70/month difference. At 100M output tokens — realistic for high-volume production pipelines — that gap becomes $87/month. Gemma 4 31B's output cost is roughly 30% of GPT-5.4 Nano's. Developers running high-throughput agentic systems, batch classification, or document processing pipelines will see compounding savings with Gemma 4 31B. GPT-5.4 Nano's cost premium is harder to justify unless long-context or safety requirements genuinely drive the choice.
Real-World Cost Comparison
Bottom Line
Choose Gemma 4 31B if: You're building agentic pipelines, tool-calling systems, RAG applications, or classification workflows. It wins on tool calling, faithfulness, classification, and agentic planning in our testing, and at $0.38/Mtok output it's dramatically cheaper at scale. Its multimodal support (text + image + video input) and 256K context window are solid for most production use cases. Developers optimizing cost-per-task on high-volume jobs will find Gemma 4 31B hard to beat.
Choose GPT-5.4 Nano if: Your workload centers on long-document retrieval, context-heavy analysis across very large inputs, or you operate in a domain where safety calibration (accurate refusals) is a compliance or product requirement. Its 400K context window and top-tier long-context score (5/5, tied for 1st) give it a genuine edge for those tasks, and its 87.8% AIME 2025 score (Epoch AI) makes it a better fit if advanced math reasoning is in scope. Just budget for output costs that are 3.3× higher than Gemma 4 31B.
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.